17 - 19 October 2019

THE 12TH INTERNATIONAL SYMPOSIUM ON HEALTH INFORMATICS AND BIOINFORMATICS

T G T A A A T G A G A A G T T G G G T A A A A A A A A T T T A A A A A A G G A A A A A A A G G G G G G G G A A A G G G G G G T T G G G G G G G T T T T G G T T G G G T T T G G T A A T T G G T T T A A A A A A A A T T T A A A A A A T T A A A A A A A T A T T T T T T A A A T T T T G T A G A A T T T A A

17 - 19 OCTOBER 2019

HIBIT 2019 ABSTRACT BOOK THE 12TH INTERNATIONAL SYMPOSIUM ON HEALTH INFORMATICS AND BIOINFORMATICS

TABLE OF CONTENTS

1 8 WELCOME MESSAGE KEYNOTE LECTURERS

2 19 ORGANIZING COMMITEE INVITED SPEAKERS

3 29 SCIENTIFIC COMMITEE SELECTED ABSTRACTS FOR ORAL PRESENTATIONS

6 61 PROGRAM POSTER PRESENTATIONS Welcome Message

The International Symposium on Health Informatics and Bioinformatics, (HIBIT), now in its twelfth year HIBIT 2019, aims to bring together academics, researchers and practitioners who work in these popular and fulfilling areas and to create the much- needed synergy among medical, biological and information technology sectors. HIBIT is one of the few conferences emphasizing such synergy. HIBIT provides a forum for discussion, exploration and development of both theoretical and practical aspects of health informatics and bioinformatics and a chance to follow current research in this area by networking with other bioinformatician.

The Organizing Comitee Izmir Biomedicine and Genome Center Ezgi KARACA, Gökhan KARAKÜLAH, Serap ERKEK, Yavuz OKTAY

1 Organızıng commıtee

Conference Co-Chairs Ezgi KARACA Gökhan KARAKÜLAH Serap ERKEK Yavuz OKTAY

Volunteer Students Aleyna ERAY Deniz DOĞAN Esra ESMERAY Gülden Özden YILMAZ Hamdiye UZUNER Işıl TAKAN Kaan OKAY Perihan Yağmur GÜNERİ

Administrative Staff Burcu İNCE Özlem DALAN Hakan ÖZLER

2 Scıentıfıc commıtee

Abdullah KAHRAMAN, University Hospital Zurich Alper KÜÇÜKURAL, University of Massachusetts Medical School Aslı SUNER, Ege University Athanasia PAVLOPOULOU, Izmir Biomedicine and Genome Center Attila GÜRSOY, Koc University Aybar Can ACAR, Middle East Technical University Bahar DELİBAŞ, Kadir Has University Barış FİDANER, Izmir Biomedicine and Genome Center Barış SÜZEK, Mugla Sıtkı Koçman Universty Bilge KARACALI, Izmir Institute of Technology Burcu BAKIR-GÜNGÖR, Abdullah Gul University Emre GÜNEY, Pompeu Fabra University Ercüment ÇİÇEK, Bilkent University Evren KOBAN, Ege University Jens ALLMER, Wageningen University Joao RODRIGUES, Stanford University Levent CAVAS, Dokuz Eylül University Malik YOUSEF, Zafat Academic College Maria-Jesus MARTIN, European Bioinformatics Institute Nurcan TUNÇBAĞ, Middle East Techical University Ogün ADEBALI, Sabanci University Oğuz DİCLE, Dokuz Eylül University Özlen KONU, Bilkent University Öznur TAŞTAN, Sabancı University Panagiotis KASTRITIS, MLU Halle Rengül ÇETIN ATALAY, Middle East Techical University Tolga CAN, Middle East Techical University Tuğba ÖZAL İLDENİZ, Acibadem University Tuğba SÜZEK, Mugla Sıtkı Koçman University Tunca DOĞAN, Hacettepe University, Institute of Informatics Türkan HALİLOĞLU, Bogazici University Uğur SEZERMAN, Acibadem University Volkan ATALAY, Middle East Techical University Yeşim AYDIN SON, Middle East Techical University Zerrin IŞIK, Dokuz Eylül University

3 4 5 Program th October 17 Thursday

09:15–10:00 10:00–10:45 09:00–09:15 Keynote Lecture I Keynote Lecture II Mehmet ÖZTÜRK Rita CASADIO Hasan DEMİRCİ Structure-Based Antibiotic Welcome & Functional and Structural Development Driven by Ambient- Opening Speech Features of Disease-Related Temperature Serial Femtosecond Protein Variants X-ray Crystallography 10:45–11:15 Coffee Break Omics Technologies 11:15 - 11:40 Nurcan TUNÇBAĞ Personalized Medicine Guided by Integrative Network Modeling 11:40 - 12:05 Tunahan ÇAKIR Network-based Analysis of Transcriptome Data to Unravel Molecular Mechanisms Behind Cellular Impairments 12:05 - 12:30 Zerrin IŞIK Network Based Approaches for Compound Target Identification 12:30 - 12:45 Ercüment ÇİÇEK SPADIS: An Algorithm for Selecting Predictive and Diverse SNPs in GWAS 12:45–13:45 Lunch 13:45 - 14:00 Poster Mounting 14:00 - 14:25 Özlen KONU Comparative Transcriptomics of Zebrafish and Mammals 14:25 - 14:50 Burcu BAKIR-GÜNGÖR Network Oriented Multi-Omics Data Analysis Methodologies to Enlighten the Molecular Mechanisms of Human Complex Diseases 14:50 - 15:05 Ogün ADEBALİ Comparison of Nucleotide Excision Repair Profiles between Gray Mouse Lemur and Human 15:05 - 15:20 Yasin KAYMAZ A Hierarchical Random Forest Approach for Cell Type Projections Across Single Cell RNAseq Datasets 15:20 - 15:35 İlayda Betül UÇAR Assessing the Impact of Proteome Redundancy Minimization in UniProtKB 15:35 - 15:50 Arda SÖYLEV Discovery of Structural Variations in Ancient Genomes 15:50–16:20 Coffee Break 16:20 - 16:30 QiaGen 16:30 - 16:45 Betül BAZ Genome-Scale Metabolic Network Reconstruction of Klebsiella Pneumoniae HS11286 16:45 - 17:00 Tuğçe BOZKURT & Umut GERLEVİK Identification of a Novel Missense Variant in the PRKAR1A Gene and Its Pathogenicity Mechanism 17:00–17:45 Keynote Lecture III Mehmet SOMEL 17:45–19:10 Ancient Genomics in Anatolia: Challenges and Poster Session I Opportunities 19:15–22:00 Conference Dinner at Bogazici Restraurant 6 th October 18 Frıday 09:00–09:45 Keynote Lecture IV Matthias SAMWALD Transformative Artificial Intelligence in Life Science and Health Care: Mapping the Challenges

Computational Structural Biology and Drug Design 09:45 - 10:30 Serdar DURDAĞI Integration of Machine Learning, Text Mining, Binary QSAR Models and Target-Driven Virtual Screening Approaches for the Identification of Novel Small Molecule Therapeutics 10:30 - 10:45 Nuray SÖĞÜNMEZ Allosteric Communications in Inactive States of Human β2-Adrenergic Receptor (β2-AR) 10:45 - 10:55 Seyit KALE The Role of Computational Modeling in Understanding Nucleosome Dynamics

10:55–11:30 Coffee Break 11:30–12:15 Keynote Lecture V Türkan HALİLOĞLU Dynamic Order in Allosteric Interactions of Proteins 12:15 - 12:30 Özge KÜRKÇÜOĞLU Exploring Allosteric Communication on the Ribosomal Tunnel in Human and Bacteria 12:30 - 12:45 Özge DUMAN Intrinsic Allosteric Dynamics in G-Protein Coupled Receptors 12:45 - 13:00 Ahmet RİFAİOĞLU Receptor-Ligand Binding Affinity Prediction via Multi-Channel Deep Chemogenomic Modeling 13:00 - 13:15 Mert GÖLCÜK Molecular Dynamics Simulations of the Dynein Linker Movement 13:15 - 13:30 Merve ARSLAN Effects of Vernier Zone Residues on Antibody Multi-Specificity

13:30–14:30 14:30-14:45 14:45-16:15 Lunch Poster Mounting Poster Session II

Bioengineering and Health Informatics 16:15 - 16:40 Oğuz DİCLE Medical Informatics Sailing in the Wind of Artificial Intellicence 16:40 - 17:05 Alper SELVER Combining Clinical and Molecular Data Using Machine Learning 17:05 - 17:30 Serhat TOZBURUN Decision Support Systems Based on Artificial Intelligence in Colonoscopy 17:30 - 17:45 Heval ATAŞ CROssBAR: Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning 17:45 - 18:00 Rafsan AHMED MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules 18:00–18:15 Closing Ceremony & Travel Awards

7 KEYNOTE LECTURERS Hasan Demirci Stanford University & Koç University Crystallography High-resolution ribosome structures determined by cryo X-ray crystallography X-ray cryo by High-resolution ribosome structures determined Structure-Based Antibiotic Development Driven Driven Development Antibiotic Structure-Based by Ambient-Temperature Serial Femtosecond X-ray X-ray Femtosecond Serial Ambient-Temperature by ribosome and its functional complexes. relevant classes of antibiotics targeting it, e.g. macrolides and ketolides, also with relevant classes of antibiotics targeting it, e.g. these classes antibiotics. development of the next generation of of aiding of the goal temperatures Overall, collect the ability to at near-physiological diffraction data structural dynamics of the provide new fundamental insights into the promises to short time by using record-low amount of sample during and XFEL beamtime . This XFEL beamtime . and sample during of record-low amount using time by short near atomic resolution any FEL source to by date structure is the largest one solved to near- structural studies, at these results will enable routine expect that (3 MDa). We clinically- to bound the large ribosomal subunit of physiological temperatures, better understand the structural mechanisms better understand the structural mechanisms underpinning between the interactions We complexes. decoding and other substrates such as antibiotics ribosomes and in record- ribosomal subunit large (50S) have also determined the structure of data from ribosome microcrystals in liquid suspension at ambient temperature. from ribosome microcrystals in liquid suspension at data complexes and decoding with microcrystals programmed ribosomal subunit 30S derivatives diffracted to either antibiotic compounds or their next-generation bound to beyond 3.4 Å resolution. Our results demonstrate the feasibility of using SFX to thus far relied crystals kept at cryogenic on large ribosome temperatures to reduce serial femtosecond X-ray of will describe the application Here I damage. radiation free-electron diffraction obtain laser (XFEL) to crystallography (SFX) using an X-ray have provided important insights into the mechanism have provided important of translation. Such studies have 10

KEYNOTE LECTURERS Mehmet Somel Middle East Techical University opportunities The increasing of ancient genomes has been accelerating availability studies in Ancient genomics in Anatolia: challenges and challenges in Anatolia: genomics Ancient by extremely low coverage data. I will further share examples of new genetic-based will further share examples of I coverage data. extremely low by insights including human societies into Anatolian years ago, their from 10,000 regional movements and social traditions. of endogenous DNA (median <1% of shotgun sequenced molecules). DNA (median <1% of shotgun sequenced of endogenous Hence, the genomes partial (median <0.1x coverage). In this talk, are typically only produced I will discuss different computational approaches to overcome the limitations posed endeavors by producing and analyzing ancient DNA data from archaeological analyzing ancient DNA data producing and endeavors by human and animal remains years. However, of Anatolian origin, from the last 10,000 DNA drive rapid Anatolia of most parts the temperate climate conditions across and most biological material examined harbor only trace amounts degradation, a number of fields, including human history, molecular evolution, and conservation evolution, and molecular a number of fields, including human history, has been contributing to these The METU/Hacettepe Ancient DNA Group biology. 12

KEYNOTE LECTURERS Rita Casadio University of Bologna disease-related protein variants protein disease-related Functional and structural features of features and structural Functional Modern sequencing technologies provide an unprecedented amount of data are perturbing the protein stability. the type of variations and their relations to the property of being related to disease to related being of property the their relations to variations and of type the of sets data with much larger previous findings obtained or not. Our results support protein sequences and it adds to the notion that not all the disease related variations of interest among neutral and disease-related for discriminating variations, including mostly affected by the presence functional pathways of disease-related variation and the highest number of disease-relatedstructural/functional domains that contains we highlight interesting physico-chemical related to features variations. Moreover, functional annotation of disease related variations, including recent developments Starting from proteins whose 3D structure suited to link to phenotyping. genotyping can be structural features that and is known, we recently described all the functional residue variations is linked to disease onset and collected in public databases. In databases. in public disease onset and collected residue variations is linked to salient of dissection scientific the many focusing on studies have been recent years, disease-relatedfeatures of will give an I different perspectives. Here variations from overview Group at the University of what the Biocomputing for of Bologna provides about single-nucleotideabout variations occurring in coding regions and leading to changes in the expressed protein sequences. significant A fraction of these single- 14

KEYNOTE LECTURERS Matthias Samwald Medical University of Vienna Transformative Artificial Intelligence in Life in Life Intelligence Artificial Transformative The advances of deep learning spark hopes that Artificial Intelligence could Science and Health Care: the Challenges Mapping and Health Science may ask: is this enough to really make new breakthrough discoveries and to offer to discoveries and make new breakthrough really to is this enough ask: may to from data the difficult road patients? In this talk I will explore better therapies to in our facing are we opportunities questions, risks and the and action, knowledge to full potential. quest to bring Artificial Intelligence to its radically transform the life sciences and medicine in the 21st century. While we century. life sciences in the 21st medicine radically transform the and now have more powerful means of recognizing patterns in large datasets we 16

KEYNOTE LECTURERS Turkan Haliloglu Bogazıcı university Proteins Dynamic Order in Allosteric Interactions of of Interactions in Allosteric Dynamic Order Allostery is a fundamental dynamic mechanism that underlies functional long underlies functional dynamic mechanism fundamental that Allostery is a Understanding and exploiting intrinsic dynamics of proteins are thus likely to open are thus likely to exploiting intrinsic proteins of dynamics Understanding and new ways for design and discovery. fluctuations reveal an order in allosteric communication pathways and directionality and in allosteric communication pathways order fluctuations reveal an the function as will be or control modulate in function. This posits key events to of large dynamic protein complex systems. demonstrated with the exemplary cases to protein function. Here, we combine Langevin dynamics and Elastic Network protein function. Here, we combine Langevin to how proteins act for their Models (ENM) to explore and understand function. With the disclosedfluctuations, intrinsic global dynamic modes of motion from local to time-delayed lagging residue sites in their correlation patterns with preceding and distance interactions in proteins. Hybrid methodologies with their combined novel and characterize aspects provide means to study internal dynamics and relate 18

KEYNOTE LECTURERS INVITED SPEAKERS INVITED SPEAKERS 20 Combining ClinicalandMolecularDataUsingMachineLearning ALPER SELVER the machinelearning-based fusionofindividualmodels. to corresponds which data using ensembles, and molecular of clinical of complementarityanddiversitywillbepresentedfortheintegration Finally, grand-challenges. organizations andlarge-scale themeasures through recentexamplesfrommulti-centric strategies willbediscussed for data set preparation,deepmodelapplication,andevaluation deep models to imaging and genomics problems.Thecurrenttrends of theapplicationemerging and similarities the differences will cover compared to their olderalternatives:artificialneuralnetworks.Then,it the stage for deep learning tools and what is changed/not-changed set first talk will This hypotheses. biological novel to produce learning new approachesofmachine be highlyaffectedbythese and will field is the genomics science, problems. Asadata-driven in variousclinical black-box deepmodelsconstantlyoutperforms the existingapproaches architectural formationandparameteradjustment,theperformanceof Albeit beingverycomplexintermsofinterpretability, convergence, techniques. learning thedominationofmachine and re-enabled analysis medicalimage Deep Learning haschangedthecourseofdata-driven Personalized MedicineGuidedbyIntegrativeNetworkModeling TUNÇBAĞ NURCAN guided precisionmedicine. perspective to the analysis of mutation effect towards the network- eventually toand therapeutic data, clinical this study provides a novel drug treatmentdata.Wethat frommutations tonetworksand believe therapeutic hypothesesforeachgroup based onavailable several These resultsalsoenableustosuggest associated withtheirsurvival. proximity togetherwithnetwork-guidedsignificantly groupingis grouping ofmutationsinclose the patientsbasedonpresence as inputandidentifyputativeunderlyingmolecularpathways. Indeed, Steiner forestproblemto integrate avarietyof'omic'data collecting the prize- Integrator software thatsolve purpose weuseourOmics subnetworks and pathways that are inferred frommutations. For this the heterogeneity. We additionallyidentifytheaffectedpatient-specific “mutation patch”,the mutations,thatwecall decreases significantly may result inphenotypicallysimilartumors. 3D spatial clustering of pathway,the same in or function protein, in thesame proximity close mutations, thatarespatiallyin that different motivation is Our main which isthemostaggressivetypeofbraintumorswithapoor survival. of patientswithGlioblastomaMultiform(GBM), the mutationprofiles knowledge is fundamental in precisionmedicine.In this work, we tackle transforming patient molecular data specific interpretable into clinically the mainobstacleindevelopingtreatmentstrategies.Therefore, is tumor.the information about the patient’s Molecular heterogeneity Precision medicineaimsto find the best treatment strategy based on 21

INVITED SPEAKERS INVITED SPEAKERS 22 Medical InformaticsintheEraofArtificialIntelligence OĞUZ DICLE signal processing. on interest who havespecial the radiologists collaboratewithengineers main concernof imaging informatics.Healthprofessionals, especially the is Image processing the imaginginformatics. called is informatics medical The fourthsubspecialty in informatics. the centerofclinical in one of the mostimportant components of HIS. Computer are engineers become support systems years, decision In recent informatics. clinical (HIS) rethemaininterestof patient care.Hospitalinformation systems and research for clinical and informationtechnologies of informatics the application is informatics and practice.Clinical the relatedresearch and epidemiologists, statisticiansand public healthdepartments provide information theyneed.Web portals serve themainfunctioninthisarea Professionalsalso ensurepublic health practiceshaveaccessto the to thelatest medicalresearch. access also ensuring health carewhile use oftechnologyto guide howthepubliclearnsabouth health and the involve the professionals where field the second is informatics Mostly this area. those who Public health works ingeneticsinvolves nursingand include chemical, dental informatics. care. Subspecializes helping analyze biomedical informationfor research and patient in thisspecialtyareconcernedwithstoring, retrieving, sharingand in thisarea.OneofthetheseareasisBioinformatics.Practitioners need inthisfield. Over time,four separate working areas appeared new toolsandmethods.Medicalinformaticsemergedto meet this storage, data analysis and data distribution had to be done withtotally in data management emerged.Bothdata field ofscience collection, images, labdatarecords, theneedforanew andthepatientmedical As the digitalization of every singleelementinmedicinesuchas medical age. from the beginning of 2000’s. This was also the beginningof big data with a lot hospital informationsystemimplementations of successful of internetsurfingjustafterithasbeenlaunchedin1998. We havemet 1996. Google becamethechoice literature wasintroducedin medical now agreatrepositoryof is 1995. Pubmed,which with WWWin 1985. In 1993 İnternet came to the scenery as a game changer together PACS applications werebegan in 1982. DICOM 1.0 was introduced in vision to data management inhospital and medical processes.Early Language and OS werebegan to use inpractice.Thisbrought a new of medical informaticssocietywasfounded. In theseyearsMUMPS Poland Later, and USA. in themidof1970’s IMIA which isstillthehead Germany. In early1970’s first researchcenterswereestablishedin 1949. In 1960’s first trainingprograms started in France, Belgiumand area washeldinGermanybytheaffordsofGustaveWagerin this in developed inthesecondhalfof20th century. Thefirstorganization mainly disinclines Medical informaticsisoneofthenewscientific influence ofAI on medicalinformatics. Now it will be augmented by AI. In this speechI will elaborate the a combinationofartand science". is motto "Medicine As ageneral chance atbetterhealthcare,with moreefficiencyandprecision. offers humans a in medicine intelligence set ofconditions.Artificial anticipate, detect,and offer suggested actions inresponseto a given the future. Computers havebecomesmarter;theyarenowable to to managein have informatics that medical challenges one ofthe relationship. Thisis psychological harmandthe physician-patient about of theseare concerns privacy,Several discrimination, challenges. legal andethical significant also raises But AIinmedicine rational reasons. the rightdiagnosisortreatmentatthetimewithbest enhance Deep learningalgorithmswillbea great time savingmethod. It will learning.the powerofmachine revealed toolswhich of thesuccessful IBM Watsondecisions. before their a greattoolforthephysicians one is be will It isexpectedthatdeeplearning for therightchoice. possibilities and and classify identifytheproblems.It precise gives verysensitive to find the features and attributes from big data which helpsto verify, Deep learningisastrongcandidateforthesolution.It is verycapable given withthesamedilemma. a smartbenefitforthepatients.Hundredsofotherexamplescanbe other data lab data, medicalimagesandseveral together to achieve year.to handlegenedata, for physicians not easy andfeasible Itis for a physician to read millions of articles published new every scientific overcome to interpret the data in a reasonable time.It is not possible professions suchasradiology to human power is nolongersufficient analyzed inorderto be usefulasamatureknowledge.Inmanymedical However, data and information isalways need to be interpreted and patients got the opportunity to reach moreinformationabout health. the worldand It canbedistributedallover without anyloss. archived benefits. Patientseveral data now istotally under controlandcanbe gained and healthcare medicine information systems, With theuse thinking, learninganddecisionmakingmachine. processing capacitywehavebeenestimatingto be able human-like learning evokedanhugehopeandby means ofbigdata and learning.improved machine which Itiscalleddeeplearning. Deep In recent years anewconceptwasintroducedto technology world making.support systems. Neuralnetworkswidelywereusedindecision the neural networks expectingto model human learning and decision from inspired of the scientists some algorithms fordecades.Meanwhile learning.machine concerning dominated these mainly Expertsystems by 1971. In thefollowingyearsmanyalgorithmsweredeveloped in everypart of humanlife.We beganto use personalcomputers problems complex computers tosolve done toutilize been attempts have After hisstudiesmany and artificialintelligence. computer science the machines. Turing is widely considered to be the father of theoretical Alan Turing wasthefirstmaninhistorywhosucceededtocomputewith 23

INVITED SPEAKERS INVITED SPEAKERS 24 Comparative Transcriptomics ofZebrafishandMammals ÖZLEN KONU for abetterunderstandinghumandiseaseandtreatment. comparative transcriptomicsapproach between zebrafishand humans of and effectivity Comparizome demonstratingthefeasibility drug exposure and cancer biology studies willbe presented using their humancounterpart. Different examplesfromdevelopment, datasets for thezebrafishparalogs microarray expression along with a webserver wehavedeveloped to statistically analyzeselected rapamycin, anmTOR inhibitor.demonstrate Comparizome, Next,Iwill upon theirexposureto lines and mousecell transcriptomes ofzebrafish I willfirstpresentourmethodology that Herein, is usedforcomparing transcriptomes duetodrug treatment orpathologiessuchascancer. syntenic to those ofmammalsallowingforcomparisonschangesin orthologous and are highly model. Zebrafishgenes human disease frequently usedasa Zebrafish isafreshwatervertebratespecies Small MoleculeTherapeutics Target-Driven VirtualScreeningApproachesfortheIdentificationofNovel Integration ofMachineLearning, Text Mining, BinaryQSARModelsand SERDAR DURDAĞI significantly reducethe rateof biopsy. artificial intelligence can bepromis removal, the applications. In addition to theof lea segmentation.Besides, opti applicatio learning underartificial intelligence. The most be blind as spots, well failurecan beattribut colonoscopy, for creation aof rapid dec detection, and classification from real Machineintelligence provides a platform suitablefor applications such as processing, Decision support systems based on artificial intelligence in colonoscopy Tozburun Serhat smallidentification novel molecules. therapeutic of 1. Figure positioning studies (Figure will 1) summarized.1 be as structure as well the identifiedfor compounds whichhigh show predicted binding affinity againstspecifictarget stru mechanism analysis,energy free calculations (i.e., MM/GBSA) using short/long multiple MD simulations by molecular simulations arethen tested by lo LigandMechanics PolarizedDocking used in moresophisticated molecular simulations approaches (i.e., Induced Fit Docking based predictedon binding energy results using high throughputvirtual screening (HTVS) techniques based screening smallof molecule databasesfor different targets will behighlighted. Filtered structures as template targets in molecular modeling studies. In t structuresthese from databasethe protein (PDB, protein enabled databank)have datatoused be these recent theligands,predictoptimal and molecular modeling that enable tools construction novelligands. ofIn novel therapeuticsmallmolecules. were appliedindifferentprojectsforthe identificationof techniques 1. Figure studies (Figure 1)willbesummarized.1-6 drug re-positioning studiesand based pharmacophoredevelopment(E-pharmacophore) target structures, aswellstructure- binding affinityagainstspecific simulations fortheidentifiedcompoundswhichshowhighpredicted energy calculations(i.e.,MM/GBSA)usingshort/long multiple MD free analysis, mechanism The molecular (MD) simulations. dynamics predicted by molecular simulationsare then tested by long molecular Docking- QPLD).Potent high binding affinity compounds that are Induced Fit Docking- IFD, and Quantum PolarizedMechanics Ligand used inmoresophisticatedmolecularsimulationsapproaches (i.e., are techniques (HTVS) using highthroughput virtual screening results be highlighted.Filteredbased onpredictedbindingenergy structures targets will databases fordifferent molecule ofsmall based screening and ligand- modeling studies.Inthistalk,examplesofstructure-based have enabledthesedata to be usedastemplatetargets in molecular these structuresfrom the protein database (PDB, protein data bank) defined crystal,aswellNMRandcryoEMstructuresaccessto construction of novel ligands.Inrecentyears,a growing number of the optimalligands,andmolecularmodelingtoolsthat enable statistical methodsforanalysisofthebinding data that help to predict range of simulation tools for description of protein-ligand binding, and computational chemistry.a provides Computationalchemistry and structuralbiology advances madeinmolecular exploit therecent benefit fromnew, more rationalapproachestodrugdesignwhich The industryappears to be suffering frominnovationcostsand would automatically detected, localized, and classified using methods years,a growing number of defined crystal, as aswell NMR and cryo EM structures and access to

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INVITED SPEAKERS SERHAT TOZBURUN

Decision Support Systems Based on Artificial Intelligence in Colonoscopy

Machine intelligence provides a platform suitable for applications such as processing, analysis, anomaly detection, and classification from real-time images of medical imaging devices. In particular, it allows the creation of a rapid decision-support system. In highly operator-dependent procedures such as colonoscopy, for example, colorectal polyps can be easily overlooked during the procedure. This medical failure can be attributed to factors such as poor bowel preparation, mucus on the polyp, bowel folds, and blind spots, as well as the inexperience of the endoscopist. At this point, abnormal tissue structures can be automatically detected, localized, and classified using methods such as machine learning and deep learning under artificial intelligence. The most common method in the literature of this particular application is convolutional neural networks, and also autoencoder- decoder architecture for segmentation. Besides, optimization and cross-validation methods are used carefully in the development of the learning model and, therefore, the model can be generalized for other possible medical applications. In addition to contributions such as adaptive contrast enhancement and motion blur removal, the development of a new generation of optical imaging endoscope devices integrated with artificial intelligence can be promising to enable rapid classification of different types of polyps and significantly reduce the rate of biopsy.

26 TUNAHAN ÇAKIR

Network-Based Analysis of Transcriptome Data to Unravel Molecular Mechanisms Behind Cellular Impairments

Among other functional genomics data types, transcriptomics is still the most accessible one due to its higher genome coverage and lower cost. Availability of public transcriptome databases is another important factor that prompts the use of transcriptomic data to elucidate molecular details of cellular impairment mechanisms. Measured mRNA levels in transcriptome experiments are indicative of corresponding protein activities. Since proteins mostly function by interacting with each other, it is oversimplification to interpret the change in mRNA levels by ignoring interactions between corresponding proteins. There are two major approaches for incorporating interaction information to the analysis of transcriptomic data. The first one is mapping the significance of change of mRNA levels (e.g. p-values) on an organism- specific protein-protein interaction network to identify a highly altered subnetwork. This enables selection of significantly changed genes in response to cellular impairment whose products physically interact with each other. The second approach is to use correlation between mRNA levels of gene pairs to create a co-expression network, which can later be divided into modules to identify different functions affected from cellular impairment. The advantage of the first approach is that it uses experimentally known physical interactions between proteins. The second one, on the other hand, is not limited by the known physical interactions, which is still incomplete, and it does not ignore the possibility of nonphysical interactions between gene products. In this talk, cognitive impairments due to aging and cellular impairments due to neurodegeneration will be analyzed via network-based approaches using corresponding transcriptomic data.

27 INVITED SPEAKERS 28 Network BasedApproachesforCompoundTarget Identification ZERRIN IŞIK eventually reducesdevelopmentcosts. to identify potential off-targets in experimentaldrug developments, The proposedapproachwouldhelp tissues. cancer for different metrics walk-based metric provided the bestrecallvaluesout of six centrality much better performance comparedto the physicalone.Therandom a PPI networkachieves showedthatthefunctional Our experiments metrics to prioritize themostprobable targets of thegivencompound. network centrality compound onthePPInetwork,andcomputesseveral of a (PPI) responses networks. Themethodfirstmapstranscriptomelevel protein-protein interaction and tissue-specific compound-treated cells data of potential off-targetsofacompoundbyusinggeneexpression of potentialtargets of agivencompound. Our approach computes a network-based approachproposedforcomputationalidentification treatment mightbealsolimited.Thefocusofthistalkisexplaining identified moreexhaustively, theobservedsideeffectsaftera disease due to unknown off-targets. If protein targets of compounds would be compound wet-labmight appear and lead unsuccessful experiments development stage.Consequently,of any unexpectedsideeffects All proteintargets of a compound might not be defined in the SELECTED ABSTRACTS FOR ORAL PRESENTATIONS SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 30 Mellon University,ComputationalBiologyDept.,PittsburghPA15213*Correspondance University, FacultyofNaturalSciencesandEngineering,Tuzla, Istanbul349063Carnegie 1. BilkentUniversity,ComputerEngineeringDept.Cankaya,Ankara068002Sabanci Serhan Yilmaz PREDICTIVE ANDDIVERSE INGWAS SNPS SPADIS: ANALGORITHM FOR SELECTING the number of candidate genes identified (Panelidentified the numberof candidategenes B) fordifferent in termsofPearson’s(Panel squaredcorrelation coefficient A)and 1. Figure Keywords: cs.bilkent.edu.tr/spadis. accompanying software tool isreadilyavailableat http://ciceklab. (https://www.biorxiv.org/content/early/2018/05/17/256677) and the show thatSPADIS outperformsitscounterparts.SPADISon bioRxiv is methods and feature selection regression-based with offtheshelf and also performrigoroussimulationexperimentscompare SPADIS genes (Figure 1B),and (iii) runsfastermuch(Figure 1C).We networks and settings (Figure 1A), (ii) it identifies more candidate performance onaverageacrossmultiple improvements inregression of SNPs areselectedand provides consistentand statistically significant in 15outof17phenotypeswhensamenumber prediction performance phenotypes andshowthat (i) SPADIS hasbetteraveragephenotype of ArabidopsisThalianagenotypeandcontinuousflowering time methodSConESonadataset compare SPADIS tothestate-of-the-art ensures a constant factor approximation to the optimal solution. We a submodularsetfunctionwithgreedyalgorithmthat by maximizing phenotype better. We presentSPADISthis novelidea that implements incorporating complementarytermsandthus,would help toexplainthe SNP network that are diverseintermof location would correspond to In thecurrentstudy, wehypothesizethatfeaturesonanSNP-selecting redundant featureswhenthegoal istoexplainacomplexphenotype. of used forfunctionallyrelatedfeatures,itleadstotheselection network. Weassumption isfrequently arguethatwhileconnectedness network and favors theselectionof SNPs that are connectedonthe a setoffeaturesguided by aSNP-SNP- SConES.selects by incorporatingbiologicalbackgroundinformation efficiently loci learning methodintheliterature,whichaimsto select alargesetof Currently,times. from longexecution suffer only onemachine is there not allow theincorporationofbackgroundbiologicalknowledgeand methods havebeenused;however,regularized regression theydo regionsandintroduceliteraturebias.Asonealternative, non-coding the complexityofproblem.However, thoseapproachesignorethe problem byfocusingoncodingregionstoreduce methods tacklethis that grows exponentiallywiththenumberofgenomicvariants.Many problem multiple lociisacomputationally challenging traits. Selecting for understandingthegeneticbasisofthese phenotype is critical of multiple locithat selection Therefore, theefficient explain the Complex traitsoftencannotbeexplainedbyindividualvariants. 1 , OznurTastan Figure showstheimprovementofSPADIS overSCONES SNPSelection,SubmodularOptimization 2 ,* , A. ErcumentCicek 1,3 SELECTED ABSTRACTS FOR ORAL PRESENTATIONS

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- [1] Azencott et. al. (2013) Efficient network-guided multi- Efficient [1] Azencott et. al. (2013) network-guided iterature bias. As one alternative, regularized regression methods have been used; however, they do not do not methods they have however, beenused; regression regularized As one alternative, bias. iterature

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. SNP Selection, Submodular Optimization 71- : Bilkent University 1 SNP network (GS, GM, GI, and GS and GI, GM, (GS, network SNP - Azencottal.(2013)et. Efficient network SPADIS: An Algorithm for Selecting Selecting SPADIS: for An Algorithm igure 1. 1. igure , (ii) it identifies it (ii) , more candidate genes http://ercumentcicek.com SNP GroupLasso and GraphLasso and GroupLasso References: [1] http://people.sabanciuniv.edu/otastan/ SCONES shown are averaged over 17 phenotypes. Blue bar in bar Blue 17 phenotypes. over averaged shown are address author’s Corresponding F B) (Panel identified genes candidate number of A) the and (Panel (13): i1

allenging problem that grows exponentially with the number of genomic variants. Many methods tackle this problem by problem this Many tackle methods variants. genomic of number the with exponentially grows that problem allenging A) ch l introduce and regions one only is there Currently, times. execution long from suffer and knowledge biological background of incorporation the allow method in machine learning focusing on coding regions to reduce the complexity of the problem. However, th However, problem. the of complexity the reduce to regions on coding focusing Complex traits often cannot be explained by individual variants. Therefore, the efficient selection of multiple loci that explain that loci multiple of selection efficient the Therefore, variants. by individual be explained cannot often Complex traits the understanding for critical is phenotype the information background SNPs are connected that on netw the cur the In phenotype. complex a explain to is goal when the features redundant of selection the to leads it features, related fea we study, selecting hypothesize that incorporating complementary terms and thus, would help to explain the phenotype better. We SPADIS present that better. phenotype the explain would to help complementary and thus, terms incorporating fu set submodular a by maximizing idea novel this implements approximation to the optimal solution. We compareSPADIS to the state Arabidopsis Thaliana genotype and continuous flowering time phenotypes time flowering continuous and genotype Thaliana Arabidopsis 1 experimentssimulation andcompare SPADIS regress shelf the with off phenotype prediction performance in 15 out of 17 phenotypes when phenotypes 17 of out 15 in performance prediction phenotype SPADIS outperforms SPADIS counterparts. its on is bioRxiv andstatistically significant Keywords the accompanythe SNPs. References: (13): Bioinformatics 29 cuts. graph with locus association mapping Corresponding author’si171-i179. Sabanci University, 1. address: http://people. [email protected], 34906, Istanbul Tuzla, Ankara Cankaya, Bilkent University, 2. sabanciuniv.edu/otastan/; 06800 – [email protected] - http://ercumentcicek.com number of SNPs selected, selected, k. All values over 17 shown are averaged number of SNPs indicates phenotypes. Blue bar the best performance of SCONES for GS-HICN)and GI, GM, network (GS, SNP-SNP the corresponding over improvement of SPADIS indicates the k value. The red bar and SCONES, C shows CPU time measurements of SPADIS, Panel SCONES. and GraphLasso GroupLasso Univariate (baseline Lasso, method), 173.219 to is varied from 1.000 considered SNPs when the number of SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 32 Kadir HasUniversity,Istanbul,TURKEY 2. DepartmentofBioinformaticsandGenetics,FacultyEngineeringNaturalSciences, Engineering, KadirHasUniversity,Istanbul,TURKEY 1. GraduateProgramofBioinformaticsandGenetics,SchoolScience Nuray Söğünmez ( STATES OF HUMAN ALLOSTERIC COMMUNICATIONS ININACTIVE Β 2 -AR) Interestingly,portion ofthereceptorincorporating the theintracellular the allosteric inhibitor-binding siteswitchedto an acceptor in PhaseII. and donor in PhaseII. On theotherhand, the donor of characteristics in residues, thereby unraveling thecausality. The orthosteric-bindingsite structure. TEanalysisidentified residuesdrivingthefluctuationsofother The amount of MI and in PhaseII TE notably intensified in the overall ones, thehighestdegreeof MI was stillshared betweenloop regions. For distal residues,although the averageMI was smaller than proximal in bothphases. the lowestbetweentransmembraneandloopregions was thehighestbetweenloopregionsand shared byproximalresidues the average information Overall, than hydrophobic ones. sharing According to MI analysis, polar residues contribute more to information finite samplingeffect. of were optimizedfor eachresidue,followedbythecorrection times and delay mean positions.Numberofbins to their atoms withrespect (TE) wascalculatedbasedonchangesintheatomicfluctuations ofC Additionally, andresidue-location. both residue-types entropytransfer wasanalyzedbased on shared informationbetweenresidue-pairs, receptor. In this study, mutual information (MI), which representsthe by the inaccessible closure of third intracellular loop (ICL3) of the bindingcavitywascompletely state, G-protein inactive In this“highly” “Phase II”; alongwiththeknowninactivestate designated as “PhaseI”. and designatedas state ofthereceptor wasrevealed inactive distinct X-rayan inactive structureofhuman Prior to this study, a1.5 μs longMDsimulationwasconductedon fluctuations [4]. analysisof atomic cross-correlation run [3]usinga time-delayed previously obtained from a 1.5 μs longmoleculardynamics(MD) allosteric siteswillbe conducted for two inactive states, which were this study, adetailedinvestigationof of mostproteins[2].Forcharacteristic two distant parts,isauniversal [1]. Allostery,communication between definedasthecommunication understanding ofthetarget-specificnetwork andallosteric dynamic industry; yet,GPCR-targeted drug discoverystilllacksacomprehensive have becomethemostimportantdrugtargetsinpharmaceutical They physiologicalprocesses. a criticalroleinmultipleessential and inthemammaliangenome,playing most diversesuperfamilies receptor (GCPR)superfamily, whichisknownto be oneofthelargest β 2-adrenergic receptor( β 2-AR was distinctly revealed as aninformationacceptorinPhaseI, revealed 2-AR wasdistinctly 1 , E.DemetAkten Β 2 -ADRENERGIC RECEPTOR 2 β 2-AR) is a member of G-protein coupled 2-AR) isa member ofG-protein β 2-AR (PDBID:2RH1)wherea β 2-AR dynamicsandputative α

SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 33 2-Adrenergic Receptor. BMC Struct. 2-Adrenergic Receptor. 2-AR; Protein Allostery; Mutual Allostery; Mutual 2-AR; Protein Β β [email protected] [1]. Weis WI, Kobilka BK. WI, Kobilka Molecular Basis The of G [1]. Weis GPCRs; Human GPCRs; 2-Adrenergic Receptor. J. Phys. Chem. B. 2019 Apr 4;123(17):3630- 2019 Phys. Chem. B. J. 2-Adrenergic Receptor. +90 212 533 65 32 (x1350) 3 on Intrinsic Dynamics of Human Pubmed PMID: 24206668 Biol. 2013 Dec;13(1):29. Intrinsic Dynamics and Causality in ED. Akten [4]. Sogunmez N, Distinct Inactive States of Human Correlated Motions Unraveled in Two β 42. Pubmed PMID: 30946584 Corresponding Author: References: References: Jun of Biochem. 2018 Activation. Annu. Rev. Receptor Protein–Coupled PMID: 29925258 20;87:897-919. Pubmed A. Correlated Extracting the Causality of der Vaart van H, [2]. Kamberaj Sep 2009 Dynamics Simulations. Biophys. J. Motions from Molecular Pubmed PMID: 19751680 16;97(6):1747-55. Intracellular Loop Effect of Akten ED. Uyar A, P, Doruker Ozcan O, [3]. region in the receptor in region in the this “highly” inactive state, which represented region is the intracellular where active state an of extreme opposite the by ligand-binding initiated orthosteric drive the extracellular known to binding. G protein Keywords: Transfer Information, Entropy ionic lock, which majorly contributes to receptor inactivation, turned turned inactivation, receptor to contributes majorly lock, which ionic that showed clearly These findings in Phase II. site major acceptor into a the intracellular to from the extra- transfer was directed the information SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 34 2. IzmirBiomedicineandGenomeCenter,Balcova,Izmir,TURKEY Library ofMedicine,NationalInstitutesHealth,BethesdaMD20894,USA, 1. ComputationalBiologyBranch,NationalCenterforBiotechnologyInformation, Seyit Kale UNDERSTANDING NUCLEOSOME DYNAMICS THE ROLE OF COMPUTATIONAL MODELING IN 1,2 [email protected] Corresponding Author: PMID: 31269447. remodeling.nucleosome Rep. 2019 CellJul2,282-294. Pubmed Histone octamerstructureisalteredearlyinISW2ATP-dependent PersingerBhardwaj SK, J, Ranish J, Panchanko AR,Bartholomew B. References: Keywords: nucleosomal DNA. for the changes inthecoreoctamer[1],both with implications can induceconformational remodeler how theactionofachromatin dependent behavior, histone variants,canleadto DNA-sequence or into howintricateepigeneticvariations,suchassubstitutionsbetween experimental input, molecular simulationscanelicituniqueinsights Guided by of thenucleosome. picture ofthedynamics a molecular modeling and biophysics to complement experimentaleffortswith be elucidated at the molecular level.We usetools of computational remains to modifications by chromatininteractorsandepigenetic protection canbetuned acid aroundit,yethow this for thenucleic a detailedlookinto how thehistoneoctamerprovidesaprotectivecore have permitted techniques structural andcomplementaryexperimental in advances Recent proteins, knownashistones. basic, well-conserved of approximately150 base pairsofDNAwrapped around eighthighly isadisk-shaped,nucleo-protein that iscomposed 200 kDacomplex repeating architecturalunitofchromatin.This the smallest nucleosome, the lies tasks. Attheheartofthisconflict conflicting are inherently Protectionto it material andregulationofaccess of our genomic Nucleosome, ChromatinRemodeling, MolecularDynamics [1]

Hada A, Hota SK, Luo J, HotaSK, Hada A, LinY, Kale S, ShaytanAK, Izmir Biomedicine and GenomeCenter,Izmir Biomedicine SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 35

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Effects umanization approachesmainly engineer ntibody ernier zone residuesare key for modulation of antibody specificity. Vernier zone regionsunderlie Figure 1. variable regions of heavy and light chains. in CDR regions are colored cyan, framework (FW) regions are colored in dark blue (heavy chain) (light chain), vernier blue zone residues are labelled as stars light and in orange, disulfide bonds are represented in yellow. multi-specificity. This study might lead to a novel antibody engineering novel antibody a to lead might This study multi-specificity. strategy for antibody specificity modulation. Keywords: et.al study, we found that certain interactions vernier between zone et.al study, and CDR residues convert a mono-specific antibody into a bispecific have comparable also bispecific antibodies Surprisingly, [2]. antibody binding affinities of mono-specificwith those Because vernier ones. in the literature, we tried zone regions are usually underestimated represent them in a schematic secondary structure to of antibody 1). Certain locations of vernier zone, especially the one facing (Figure for providing antibody be important antigen, were documented to determining regions (CDRs) determining regions is responsible of antibodies which for approaches mainly engineer Humanization vernier antigen binding. order CDRs in of correct canonical structures provide zone residues to specificity affinity, Unlike binding affinity. restore/improve binding to antibody modulate to is usually hard it and quantified be cannot modelling and using homology design. By rational specificity by Bostrom sequences from molecular dynamics simulations for antibody being developed for targeted therapy. Antibody-antigen interaction Antibody-antigen therapy. targeted being developed for successful for consider of to main properties specificity is one of antibody drug development. specificity Although antibody has not been yet, certain structural and physicochemical completely understood characteristics [1]. In this study, roles are known to play important we hypothesize that vernier zone residues of are key for modulation zone regions underlie complementarity Vernier specificity. antibody Monoclonal antibodies are one of the most important biological drugs are one of the most important biological Monoclonal antibodies [1] Sci Acad Natl Proc interactions. [2] the Corresponding No:58/5 Cad. Mithatpasa Campus, Health University Eylul Dokuz Center, Genome and Biomedicine Izmir Turkey Izmir, Balcova 1. Figure CDR regionsare colored incyan, chain), (light blue yellow. References: represent themin a schematic of especially Thisstudy mightnovel leada to antibody engineering strategy for ant Keywords development. Although development. play to known are characteristics physicochemical v binding. antigen for responsible is which antibodies of (CDRs) regions determining complementarity H and quantified be cannot specificity affinity, binding Unlike affinity. binding restore/improve to order in CDRs antibody modulate to hard usually is it simulations dynamics molecular interactions between antibody mono therapy. targeted for developed being drugs biological important most the of one are antibodies Monoclonal A

Merve Arslan Center, Izmir, Turkey and Genome 1. Izmir Biomedicine Izmir, Turkey Institute, Dokuz Eylul University, and Genome 2. Izmir Biomedicine EFFECTS OF VERNIER ZONE RESIDUES ON RESIDUES ZONE VERNIER OF EFFECTS MULTI-SPECIFICITY ANTIBODY SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 36 No:58/5 BalcovaIzmir, Turkey Center,Health Campus,Mithatpasa Cad. DokuzEylulUniversity Address: Corresponding Author’s 2009;323(5921):1610. PubMedPMID:19299620. that interactwithHER2andVEGFattheantigenbindingsite.Science. [2] BostromJ, Yu S-F, Kan D, etal.Variants oftheantibody herceptin 24938786. Proc Natl Acad Sci. USA2014 111(26), E2656-2665. PubMed PMID: of specificityandaffinityinantibody-proteinOrigins interactions. References: [1] Peng, H. P., H., Lee, Jian,J. K. W., &Yang, S. A. Izmir Biomedicineand Genome SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 37 3 , Mehmet SOMEL , Mehmet 2 Structural Variations; High Throughput Sequencing; Sequencing; High Throughput Structural Variations; , Can ALKAN 1 performance of our algorithm using real data and simulations, which and simulations, data performance of our algorithm using real revealed surprisingly with variants identified in modern high overlap of our of coverage, indicating the accuracy low depth genomes at approach. Keywords: Ancient Dna compared to modern genomes. Indeed, no known algorithms that compared to modern genomes. Indeed, detect structural variations in ancient genomes have been developed the ancientSV we describe discovers In this paper algorithm that so far. different variations including types of structural deletions, inversions, duplications and mobile elementinsertions in ancient genomes. Our read- uses various sequencingapproach such as split-read, signatures evaluated the We technology. pair and read-depth with HTS short-read have been developed for this purpose. In parallel, sequencing the parallel, sequencing In purpose. this for have been developed with short-read has become possible ancient populations genomes of genomes have thousands of ancient sequencing technologies and the World. of parts the other and from Anatolia been sequenced both since ancient DNA is generally not well preserved, there are However, many technical difficulties in analysing such including genome data, rates and lower coverage the shorter read lengths, higher error Structural variations are genomic Structural variations rearrangements that affect more genome. These than 50 bps in the are commonly linked variations significance have profound in disorders and with various genomic analysis. and evolutionary population With the emergence of high- sequencing throughput (HTS) technologies, studies on the discovery algorithms several common and these variants have become more of [email protected] Arda SÖYLEV Konya, Agriculture University, Konya Food and of Computer Engineering, 1. Department [email protected] Turkey, Ankara, Turkey, Bilkent University, of Computer Engineering, 2. Department [email protected] University, Ankara, Turkey, Sciences, Middle East Technical 3. Department of Biological DISCOVERY OF STRUCTURAL VARIATIONS IN VARIATIONS STRUCTURAL OF DISCOVERY GENOMES ANCIENT SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 38 Riss, DE. 2. ComputationalBiology,BoehringerIngelheimPharmaGmbH&CoKG,Biberachander 1. InformaticsGroup,HarvardUniversity,Cambridge,MA,USA. Yasin Kaymaz CELL RNASEQ DATASETS FOR PROJECTIONS CELLTYPE ACROSS SINGLE RANDOM FORESTA HIERARCHICAL APPROACH Hierarchical classification, HieRFIT Key Phrases: (https://github.com/yasinkaymaz/HieRFIT). through GitHub available as anRpackage,freely implemented is examples. HieRFIT withinter/intra-species in celltypecross-projections datasets showing itseffectiveness scRNAseq analyzed publiclyavailable when necessary.internal nodesofthehierarchy UsingHieRFIT, were- at types bylabelingcells to incorrect of cells avoiding theassignment distributions forcandidate uncertainties while class labelsand resolves predictions. Weincorrect that adjustsprobability scheme a scoring use approachaccuracy andreduces improves classification hierarchical treestructure.Wedecision organized ina hierarchical showthat our use anensembleapproach combining multiplerandom forest models, data. Werelationships, alongwiththereference the class representing tree structure algorithm takesasinputahierarchical classification Random Forest“Hierarchical forInformationTransfer”.novel This accuracy. We namedourtoolas‘ for improvedclassification information about type relationships cell random theselimitationsby forests that using a overcomes priori Here, we present a new celltype projection tool based on hierarchical cell typesarerepresentedwithonlyafewcells. data in whichrare protocols produce low-throughput some single-cell input thresholdofquerydata and notrelyonaminimum as classes shouldbeableto handlemanycandidatecell an idealclassification novel typesandagood types. However,representation ofknowncell few orno types, andwhenthequerydata contains cell represented training data work well whenthereference is composedof a few well- current approacheshavelimitations.Existingalgorithms experiments, type features. Whilemethodsexistforcellassignmentacross step forproper interpretationofbiological a critical targeted data is atlases andnewlygenerated, cell information transferbetweenexisting smaller-scale individualstudies.Therefore,integrationandaccurate to information fromsuchprojectsprovidesnewdimensions the rich studies, includinglargescalecellatlasprojects.Beingabletoutilize in thescopeofscRNAseq disease, motivatinganexponentialincrease with phenotype and types andpreviouslyunknownrelationships meaningful categories.Theseapproacheshaverevealednovelcell into functionalandbiologically cells and toclassify individual cells, methods tostudybiologicalvariationamong an explosioninnovel has ledto RNA sequencing(scRNAseq) of single-cell The emergence 1 , NathanLawless single-cell RNAseq,Celltypes, Machine learning, single-cell 2 , TimothySackton HieRFIT 1 ’, whichstandsfor SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 39 Timothy Sackton Informatics Group, Informatics Sackton Timothy Corresponding Author: Corresponding MA, Cambridge, USA. +1 617-495-9492 Phone: University, Harvard Email: [email protected] SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 40 2. FacultyofEngineeringandLifeSciences,IstanbulMedeniyetUniversity,Istanbul,Turkey 1. DepartmentofChemicalEngineering,IstanbulTechnical University,Istanbul,Turkey Pelin Güzel BACTERIA THE RIBOSOMAL TUNNELINHUMAN AND EXPLORING ALLOSTERIC COMMUNICATION ON 1,2 Keywords: design drugswithhighselectivity forbacterialribosome. to for newstrategies insights provide useful findings all Here, involved. between tunnel and ptc, where relativelylow conserved nucleoidesare both organisms usesimilar allostericcommunicationmechanisms suggest that tunnel ofhumancomplexyet. Atthispoint,calculations study forthe no experimental is there while plausibility ofthefindings, predicted forT. thermophilusagreewithpreviousdata supporting the pathways method. Allosteric of thiscomputationallyefficient success the highlighting network ofnucleotides, point tosame simulations Conclusion: conserved non-Watson-Crick basepairatA4396-C3909. the pathways areobservedforthehumanribosome,including optimal andsuboptimal [5].Similar base pairA2450-C2063 Crick suboptimal conserved non-Watson-paths accomodate the universally and A2451(ptc), agreeingwithapreviousstudy[4].Inaddition, path involving A2503, G2061 and U2504, which linkA2062 (tunnel) Results: find optimalandsuboptimalpathsbetweenptctunnel. networks arethensubjectedto shortest path algorithms withk=20 to on 500 ns longtrajectoriesand used to generate thenetwork.Both are calculatedbased path calculationfromCGMD;cross-correlations [1]. Insuboptimal between nucleotides strengths interaction local using [2,3]. Ink-shortestcomplexes path method; a network isgenerated both T.ribosomal exittunnelin and humanribosomal thermophilus trajectories areusedto predict allostericpaths between ptc and moleculardynamics(CGMD) path calculation usingcoarse-grained Method: drugswithhighselectivitytopathogens. tested forspecies-specific organisms ishighlyimportant to revealallostericregionsthat can be allosteric communicationpathways in ribosomal complexes of different task. Exploringpotential tunnel andptcinordertocontrolthecrucial for allosterybetweenthe side. Thereisaccumulatingevidence solvent form polypeptides that are emergedfromtheribosomalexittunnelto peptidyl transferase center(ptc)catalyzespeptide bond to synthesis of the complex. On thecomplex,a conserved rRNAcavitynamed structural rnpcorewithonlyadditional parts at the solventsurface from acommon prokaryotes andeukaryotesseemtobeevolved kingdoms of life. Theselarge ribonucleoproteins (rnp) found in both Background: , Ozge Kurkcuoglu InT.optimal both methodspointtothesame thermophilus, Graph-based k-shortest paths method [1]and suboptimal Ribosomal Complexes, Allostery, Shortest Paths Results fromour graph-based approach [1] and MD Ribosome complexes synthesize proteins acrossall synthesize Ribosomecomplexes 1 SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 41 [email protected] [1] Guzel, P; Kurkcuoglu O., BBA-General Subjects, O., Kurkcuoglu Guzel, P; [1] 2364. 2695-2696. 2006, 22(21), et. al., Bioinformatics, J., B. [3] Grant, A.S., Thum, C., Mankin, Molecular Cell, 2008, N., [4] Vazquez-Laslop, 30, 190–202. A. Dahlberg, E., M. A.,[5] Bayfield, Nucleic Acids Thompson, J., 5512–5518. Research, 2006, 32, Corresponding author’s address: References: 3131-3141. 1861(12), 2017, A.,[2] Górecki, 30- Chemistry Journal of Computational et.al. 2009, SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 42 3. BioinformaticsGraduateProgram,MuğlaSıtkıKoçmanUniversity,Muğla,T 2. DepartmentofComputerEngineering,MuğlaSıtkıKoçmanUniversity,Muğla,T Wellcome Trust GenomeCampus,Hinxton,CambridgeCB101SD,UK 1. EuropeanMolecularBiologyLaboratory,BioinformaticsInstitute(EMBL-EBI), Maria JesusMartin Ilayda B.Ucar MINIMIZATIONREDUNDANCY INUNIPROTKB ASSESSING THEIMPACT OF PROTEOME redundant set of sequences for the same species available in UniProtKB.in available species for thesame redundant setof sequences the proteinsin our setiscomparedagainst the domainsinnon- protein domainannotations,the InterPronovel domains availablefor Similarly,sequences. are categorizedas“novel” the lossof toassess showing <50% identity to any other sequenceinwholeUniProtKB UniProtKBthe remaining using BLASTand thesequences sequences any other sequence inthisnon-redundant set arefurthercompared to UniProtKBin usingBLAST.showing <50%identityto Thesequences available for thesamespecies the non-redundantsetofsequences our setagainst in compared thesequences sequences, wefirst novel and the lossofproteindomainannotations. Tothe lossof assess loss isassessedbased on two aspects; thelossof novel sequences The information and Mycobacteroidesabscessus. toyonensis Bacillus redundant proteomesalreadyinUniParc, namelyRhizobiummeliloti, each withalargenumberof bacterial species of threerepresentative created aset we first potential informationloss, work, toassess In this proteomes removed. corresponding toredundant novel informationlostasthesequences through there isaneedto this effort. Hence, developmethodsto assess UniProtof informationlost and thereispotentiallysomelevel releases the UniProt Archive(UniParc). Theprocedureisstillapplied as partof redundant proteomes weremadeavailablefordownload only from (approximately 47 million entries)weredeprecated [1]. These corresponding toredundant proteomes mid-2015 andthesequences methods. Thisprocedurewasfirstimplementedforbacterialspeciesin groups using acombinationofmanualand within species automatic team developedaprocedureto identify highlyredundant proteomes biological resultsinUniProtKB. To deal withthisissue,UniProtKB abilityto effectively navigateand find meaningful to hinder theuser’s and redundancystarted Thesize contributions werelimited. sequence the redundancyinUniProtKBinvolved, wereincreasing as theirunique sequencing projects', especially whensequencingof similar organisms in 2014. Furthermore,entries nearly 90million reaching thegenome projects, UniProtKBin genomesequencing vast increase doubled in size and DDBJ. GenBank operates betweenEMBL, which of the Asaresult Database Collaboration(INSDC) International NucleotideSequence Historically, UniProtKBfrom was importingallcodingsequences supplemented withautomaticannotation. sequences of nucleotide the manuallyannotated section and TrEMBL, whichofferstranslations of SwissProt,of functionalinformationonproteins.Itconsists collection The UniProt Knowledgebase (UniProtKB) is thecentralhub for the 2 , RamonaBritto 1 1 , BorisasBursteinas 1 , BarisE.Suzek urkey urkey 2,3 and E-mail: [email protected] E-mail: Address: Corresponding Author’s 28025334. 26;2016:baw139. doi: 10.1093/database/baw139. PubMed PMID: the UniProtin Knowledgebase.Database(Oxford).2016Dec N, O'DonovanC,MartinMJ.proteome redundancy Minimizing [1] BursteinasB,C, Redaschi Rivoire BrittoR,BelyB,A, Auchincloss References: Sequence Annotation. Keywords: their support tothiswork. for 19/079/10/2/2 Project BAP University Koçman Sıtkı Muğla We gratefullyacknowledgetheEMBL-EBIprogram and internship of genomesequencingprojects. that important is becomingexceedinglydue number to the increasing procedure help inimprovingtheproteomeredundancyminimization also factor in proteindomain annotations. We anticipate our work will however, our initial findingssuggest the decision on redundancy should in termsneedstobefurtherevaluatedbyUniProtKB curationteam, of theloss of informationloss.Thesignificance leads tosomelevel procedure the redundancyminimization we identified In conclusion, protein domainsrepresent“novel”GOmolecularfunctions. Based onInterpro2GO mappings, onaverage,~2%ofthese“novel” unique InterProproteomes, respectively.their in domainsrepresented domains), 5.42%(358 domains) and7.9%(412ofallthe R. meliloti,B.account for12.2%(800 toyonensis,andM.abscessus proteomes, respectively. Similarly, “novel”domainannotationsin proteins), 0.08%(898 proteins) and0.17%(2431 proteins) oftheir B.R.meliloti, account for0.3%(3071 toyonensis,andM.abscessus in Based onourinitialresults,thenumberof“novel”sequences current procedure. corresponding proteome is categorized as redundant based on the wouldn’timportant regions) berepresentedinUniProtKB (orlost)ifthe annotations (e.g. correspondingmolecularfunctions,structurally domain or “novel” sequences “novel” domain annotations.These The InterPro domainspresentinonlyoursetiscategorizedas“novel” Protein Databases, Protein SequenceAnalysis,Protein

43 SELECTED ABSTRACTS FOR ORAL PRESENTATIONS ORAL FOR ABSTRACTS SELECTED

SELECTED ABSTRACTS FOR ORAL PRESENTATIONS SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 44 Carolina atChapelHill,NC27599,USA 2. DepartmentofBiochemistryandBiophysics,SchoolMedicine,UniversityNorth 34956, Turkey 1. MolecularBiology,GeneticsandBioengineeringProgram,SabanciUniversity,Istanbul, Sancar Veysel Kaya AND HUMAN REPAIR PROFILES BETWEEN GRAY MOUSE LEMUR COMPARISON OF NUCLEOTIDE EXCISION 2 , OgünAdebali (Microcebus murinus). BMCbiology, 15(1), 110. doi:10.1186/s12915- assembly and centromere characterization of the gray mouse lemur D.,R., Brown,A. ...Worley, C.(2017). Hybrid denovogenome K. [3] Larsen, P. Liu,Y., Harris,R.A., A., Murali,S. C.,Campbell,C. seq. NatureProtocols, 14(1),248-282. repair withXR- mapping of nucleotideexcision (2019) Genome-wide [2]Hu J., LiW., AdebaliO., Yang Y., OztasO., SelbyC.P., Sancar A.* Biological Chemistry, 292(38), 15588-15597. coligenomes.TheJournal of repair mapsofthehuman and E. repair andexcision of DNAexcision mechanism (2017) Molecular References: Genome Alignment,MouseLemur Keywords: organism. which might eventually contributemouselemurto become a model of DNA damage across mouselemurand repair preferences human, This workwillprovideanunderstandingoftheevolutionaryconservation regions isthennormalized,RPKM values arecalculatedand compared. homologous regionsreciprocally. XR-Seq readsforallhomologous in anunbiasedway,and mouselemurgenomes defined the wehave in human repair levels relativeexcision compare thewindow-based between repairprofilesof lemur and human genomes.To and assess homologous regions wasfurtherusedto the association better assess annotated gene provided.Thisadditionalinformationonthedefined and the possibilityof each homologous region overlappingwith an the alignmentscoreofeachhomologous region, strandinformation, homologous regionsinbothgenomesarelisted.Further detailssuchas (Mmur_3.0) to the humangenome(hg19). Chromosomal positionsof software package[4]hasbeenusedto align themouselemurgenome regions betweengenomes,NucmernucleotidealignerfromMummer repair. excision the mapsofgenome-wide To establishoverlapping fibroblasts wereusedto generate excisedoligomerpool representing between mouselemurandhuman.UV-irradiated humanandlemur a directcomparisonofDNA damagerepairprofiles able toachieve [3], wewere genome at chromosome-scale murinus) (Microcebus type ofrepair.profiling ofthis of thegraymouselemur Bytheassembly of nucleotide excisionrepairwith XR-seq [2], provides comprehensive DNA adducts in animals.[1]Correspondingly, mapping genome-wide bulky to remove the primarymechanism repairis Nucleotide excision 1 , ÜmitAkköse 1 Damage Recognition; Repair Mapping; Cross-Species, Damage Recognition; RepairMapping; Cross-Species, [1] HuJ., SelbyC.P., AdarS., AdebaliO., SancarA.* 1 , ZeynepKaragöz 1 , LauraLindsey-Boltz 2 , Aziz SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 45 genomes. Genome Biol. 2004;5(2):R12. genomes. Author’sCorresponding Address: [email protected] Ogün Adebali Turkey Istanbul, 34956, University, http://adebalilab.org/Sabanci 017-0439-6 A, Phillippy S, Delcher AL,[4] Kurtz Antonescu M, Shumway M, Smoot large comparing for software open and SL.Salzberg C, Versatile SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 46 1. GebzeTechnical University,DepartmentofBioengineering,Gebze,Kocaeli Betül Baz PNEUMONIAE HS11286 RECONSTRUCTION OF KLEBSIELLA METABOLICGENOME-SCALE NETWORK 1 , Tunahan Çakır metabolic network modelwillclarifythefunctional capacityofthe was performed.Thenew carbon sources of growth rateondifferent metabolic network. Asa validation stepofour model, thesimulation to have amoreaccurateandreliable within themodelwaschecked the referencestrainusedasa template.Afterthat, the inconsistency reactions that to are specific our target organism but do not exist in automatically. metabolic network was used to obtain The KEGG-based metabolicnetworkoftheorganism used toreconstructaKEGG-based and respirationofthebacteria. Inafurtherstudy, RAVEN Toolbox was metabolites, and1262 genes. Themodelcould simulate thegrowth strainHS11286 consists of 2332 reactions, 1757 for K.pneumoniae metabolicmodel based analysis. Thereconstructedgenome-scale that can simulatephenotypicbehavioroftheorganismviaconstraint- biomass componentswereneededto turn the draft model into a model that producemissing reactions and some reactions reaction, exchange model. Theadditionofbiomassformation a completefunctional draft model wasimprovedbyamanualcurationprocessto have homology,model viasequence cut-offs. withspecified Thegenerated generated by retrieving a possible set of reactions from the template [4] wereprovided as input. Hence, thedraft metabolic network was iKp1289 metabolic networkmodelcalled genome-scale high-quality KPPR1, and its recentlyreconstructed HS11286 and K.pneumoniae strain scope of proteome informationof reconstruction, K.pneumoniae the of it[3].Within species similar target organism andgenetically reconstruction based on protein similarityviaBlastp between the Toolbox 2.0 was usedforsemi-automated draft metabolic model study,for curationandvalidation[2].Inthis MATLAB-based RAVEN literature review later needlaboriousmanualeffortswithextensive However,network modelsatthegenome-scale. thesedraft models draft metabolic for theautomated of organism-specific reconstruction bioinformatics tools gene products [2]. Atpresent,thereareseveral reactions catalyzedby a mathematicalrepresentationofbiochemical metabolic network reconstruction is networks. In silico genome-scale data-driven by constructing further insights give approaches can novel treatmentsfor infections. Computational systems biology and to consequently design elucidate molecularmechanisms It is important to demonstrate metabolicinteractionswithinbacteria [1]. infections hospital-associated opportunisticand severe causes strainHS11286 carbapenemase production. K.pneumoniae including bacterial pathogen, and has threemultidrug-resistanceplasmids Klebsiella pneumoniaeHS11286 is an important gram-negative Enterobacteriaceae(CR-CPE) strain Carbapenemase-producing an emergingproblemofpublichealth.Carbapenem-resistantor of multidrug-resistantGlobal risinginthelevels bacteria hasbeen 1 SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 47 Metabolic Network; Genome-ScaleMetabolic Reconstruction; [1] Liu, Lu, et al. "Identification and characterization and characterization et al. "Identification of [1] Liu, Lu, [email protected] iKp1289 metabolic model for Klebsiella pneumoniae KPPR1." The pneumoniae KPPR1." metabolic model for Klebsiella iKp1289 diseases 215.suppl_1 (2017): S37-S43.Journal of infectious E-mail: [2] Thiele, Ines, and Bernhard Ø. Palsson. "A protocol for generating a generating for protocol "A Palsson. Bernhard Ø. Thiele, Ines, and [2] high-quality genome-scale metabolic reconstruction." Nature protocols 5.1 (2010): 93. for metabolic A versatile toolbox 2.0: al. "RAVEN et Hao, [3] Wang, and a case study on Streptomyces coelicolor." network reconstruction 14.10 (2018): e1006541. computational biology PLoS et al. "Generation and validation of the Christopher S., [4] Henry, Klebsiella Pneumoniae Klebsiella References: the carbapenem-resistant secretion system in VI type an antibacterial strain Klebsiella infection microbiology and in cellular Frontiers pneumoniae HS11286." 7 (2017): 442. bacteria and the mechanisms mechanisms and the bacteria financially was of infections. study This 316S005). Code: (Project by TUBITAK a grant through supported Keywords: SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 48 1. PolymerResearchCenterandChemicalEngineeringDepartment,BogaziciUniversity Türkan Haliloğlu Özge Duman in G-protein Coupled Receptors Intrinsic Allosteric Dynamics ANM Keywords link. evolutionary their and family GPCRs of landscape dynamic of pa activation the modulate plausibly DRY and NPxxY motifs the allostericconnection from extracellular to intracellularside active crystal structures of which are shown in Figure 1, is seen to disclose certain modes of motion that profile dissecting the motion underlying the activation of Muscarinic acetylcholine M2 rec transition pathw simulations (LD) [3 vital functions of the central and peripheral nervous systems and regulating a variety of physiological functions Rhodopsin performed to date,conformational dynamics in u C A classical the to according classes six G [email protected] Polymer address author’s Corresponding Jul;14(7):715 2018 Biol. Chem Lewinson [ 531(7594):335 17; Mar 2016 Nature. receptors. acetylcholine muscarinic Bachhawat, TS Kobilka, PM Sexton, BK Kobilka, A Christopoul [3] Thal DM, Sun,B D Feng, V Nawaratne, Leach,K CC Felder, MG Bures,DA Evans, WI Weis,P 46(D1): D440 GPCR adding in 2018: GPCRdb Gloriam. Pándy [2] Reviews. 2017 Jan 11;117(1):139 [1] Latorraca NR, AJ Venkatakrishnan, RO Dror References: during the activation.(b) Intracellular side viewofstructures. the Figure 1. 4 nderstanding lass A (Rhodopsin A lass - ] Yang M Yang ] ]. Protein Özge Duman Özge the the

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– of of G D446. PubMed PMID: 29155946. PMID: PubMed D446. le side viewofthe structures. arrows show displacement of TM6 during the activation. (b) Intracellular 1. Figure acetylcholine M2Receptor FunctionalBiased Signaling; ANM-LD; Muscarinic Selectivity; Keywords: of GPCRsfamilyandtheirevolutionarylink. will provide a means towards the conformational dynamiclandscape The comparisonof the dynamicspectrumsacrossGPCRssubtypes signaling. bindingandallosteric activation pathway and thusG-protein motion, which suggest these sitesasmeansto plausibly modulate the such asDRY and NPxxY motifs alignwiththe hingesoftheactivation switches side. Knownfunctionalmolecular to intracellular extracellular from connection modes ofmotionthatprofiletheallosteric certain Figureare shownin structures ofwhich crystal to disclose seen 1,is of M2 receptor,Muscarinic acetylcholine the inactiveand active As an initial casestudy, dissecting themotion underlying theactivation interactions foreachmemberofthisfamily.pathways and allosteric and hiddeninconformationaltransition of dynamicmodesaccessible atom Langevin(LD) simulations,weexplorethespectrum Dynamics ANM-LD [4],theAnisotropicNetworkModel(ANM)combinedwithall- systems and regulating avarietyofphysiologicalfunctions[3].Using regulating many vital functionsof the centraland peripheral nervous (Class A)that includes fivesubtypes from M1 to M5 responsible from active states remain elusive.We plan to study Rhodopsin-like family been performedtodate, conformational dynamicsin-betweeninactive/ in drugdesign.Althoughmanystudieshave their functionisessential 800 GPCRs existinhumans[2].Theunderstandingofhowtheyactfor [1]. Morethan the largestclass is receptors) A (Rhodopsin-like Class and BandCformthemainclasses A, system.Class A-F classification according to the classical proteins. GPCRsaredividedintosixclasses are thelargestfamilyofsignaling CoupledReceptors (GPCRs) G-Protein - ays and allosteric interactions interactions allosteric and ays LD LD how they act for their function is essential in drug design. Although many studies have been - , we explore t 1 molecule probing oftheconformational homogeneit Center, , BurcuÖzdenYücel 1 - - [

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SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 49 Polymer Research Center, Research Center, Polymer [1] Latorraca NR, AJ Venkatakrishnan, RO Dror. GPCR RO Dror. NR, AJ Venkatakrishnan, [1] Latorraca Chem Biol. 2018 Jul;14(7):715-722. PubMed PMID: 29915236. Chem Biol. 2018 Jul;14(7):715-722. Author’sCorresponding Address: Istanbul, Turkey Bebek 34342, Bogazici University, [email protected] Bachhawat, TS Kobilka, PM Sexton, BK Kobilka, A Christopoulos. Sexton, BK Kobilka, PM TS Kobilka, Bachhawat, the M1 and M4 Crystal structures of Mar 17; muscarinic 2016 Nature. acetylcholine receptors. PubMed PMID: 26958838. 531(7594):335-40. J Rose, Masrati, G B Aykac Fas, Acar, B Levanon, Livnat N M, Yang [4] Y Zhao, O T Haliloglu, N Ben-Tal, Single-moleculeLewinson. homogeneity probing of the conformational Nat BtuCD. of the ABC transporter [2] Pándy-Szekeres G, C Munk, TM Tsonkov, S Mordalski, K Harpsøe, S Mordalski, C Munk, TM Tsonkov, [2] Pándy-Szekeres G, DE AJ Bojarski, AS Hauser, and models structure GPCR adding in 2018: GPCRdb Gloriam. Jan 4; Acids Res. 2018 ligands. Nucleic PMID: 29155946. PubMed 46(D1): D440–D446. MG CC Felder, Leach, K V Nawaratne, B Sun, D Feng, DM, [3] Thal P WI Weis, Evans, Bures, DA References: Chemical in Motion. Structures Dynamics: PMID: 27622975. PubMed Jan 11;117(1):139-155. Reviews. 2017 SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 50 4.Department ofComputerEngineering,MiddleEastTechnicalAnkara, Turkey University,06800 1SD Hinxton,Cambridge,UK (EMBL-EBI), CB10 3. EuropeanMolecularBiologyLaboratory, European Bioinformatics Institute Ankara, Turkey of Informatics/DepartmentComputerEngineering,Hacettepe University, 06800 2. Institute Technical University,06800Ankara,Turkey 1. Cancer SystemsBiologyLaboratoryMiddle East (Kansil),GraduateSchoolofInformatics, Volkan Atalay Hermann Zellner Vishal Joshi Tunca Doğan Representations andDeep Learning Biomedical Relations withNetwork CROssBAR: Comprehensive Resource of 3 KEGG, OMIM,EFO, OpenTargets and HPOhasbeendeveloped. UniProt,like PubChem, DrugBank,Reactome, InterPro, ChEMBL, pipelines, which doestheheavyliftingof data from varied data sources data with its components.CROssBAR network generated on-the-fly protein, disease,drug its relatedbiological or apathway) to visualize (i.e., agene/ to browsewithanentityofinterest be possible it will where 5. pathway, interms of livercancermechanisms; the biologicalnetworksofintegratedinformation,onPI3K/AKT/mTOR based predictorandfrom computational resultsfromthedeeplearning 4. cansyl/CROssBAR-Networks); (https://github.com/ in-between and predictedpairwiserelations the known andtheedgesrepresent proteins, pathways anddiseases, compounds/drugs, genes/ types ofnodesrepresent different where 3. representations (https://github.com/cansyl/DEEPScreen); predicts drug-target interactions based on 2D structural compound called DEEPScreenusingdeepconvolutionalneuralnetworks which Foron andoff-targeteffects. novel this,wedevelopedamethod of unknowndrug/compoundtoreveal - targetproteininteractions prediction computationalmethodforthecomprehensive a novel 2. data fromvariousresources; of theCROssBARdatabase 1. Construction contains 5modules(Figure 1): The proposedcomputationalsystem medicine. and precision discovery of drug focusing onthefields various biomedicalresources, connecting by toaddresstheseshortcomings resource, CROssBAR, comprehensive their application to real-world problems.Herewe aim to develop a in termsof data especially connectivity,have shortcomings whichlimits However,biomedical research. that assisttheexperimental theyalso in theliterature computational toolsandservices There areseveral , AndrewNightingale 1,2,3

1,4 Generation of the biomedical networks Generation of the biomedical Large-scale prediction of unknown drug-target interactions: of unknowndrug-target prediction Large-scale Construction of the open access CROssBAR web-service access open of the Construction Biological evaluation (experimentalBiological evaluation validation) , AhmetSureyyaRifaioglu 3 , RengulCetin-Atalay 3 , RabieSaidi 1 , MariaJesusMartin 1,4 , HevalAtas 3 , VladimirVolynkin ofintegratedinformation byintegratingbiological 1 , EsraSinoplu 3 , ofselected 3 , 1 , ,

SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 51 Biological Data Integration, Computational Drug Computational Biological Data Integration, Overall schematic representation of the CROssBAR project. Overall schematic representation of the Figure 1. Corresponding Authors' Addresses: [email protected], [email protected], [email protected], flow from drugs/compounds to diseases flow from drugs/compounds with network representations and will be utilized to aid experimental and computational work in biomedical research. Keywords: Prediction, Interaction Drug-Target Discovery Repurposing, And Biomedical Network Analysis, Networks, Biological Deep Learning, Biomedical Entity Relationships. Databases/Services, provide training data for the deep learning based drug discovery drug learning based for the deep data provide training biological of construction the for source data DEEPScreen, and method publicly available will be and EMBL-EBI is maintained by networks, the service. researchers that, is expected to API through a REST It which resources data the biological system will bridge CROssBAR fairly disconnected but biomedical information, highly related provide new system displays a continuous data The from each other currently. The data is downloaded and processed by applying different rules different applying by processed and downloaded is data The in is hosted database The noise. filter out to logic in implementation which CROssBAR database, self-sufficient DB. collections Mongo in SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 52 CB10 1SDHinxton,Cambridge,UK 4. EuropeanMolecularBiologyLaboratory,BioinformaticsInstitute(EMBL-EBI), 3. InstituteofInformatics,HacettepeUniversity,06800Ankara,Turkey Middle EastTechnical University,06800Ankara,Turkey 2. CancerSystemsBiologyLaboratory(Kansil),GraduateSchoolofInformatics, Ankara, Turkey 1. DepartmentofComputerEngineering,MiddleEastTechnical University,06800 Rengül Çetin-Atalay Ahmet SureyyaRifaioglu CHEMOGENOMIC MODELING PREDICTION VIAMULTI-CHANNEL DEEP RECEPTOR-LIGAND BINDING AFFINITY protein sequence where rows and columns represent amino acids in represent rows andcolumns where protein sequence unique integer. Subsequently, we created an encoding matrix for each function suchthat each unordered amino acidpair was mapped to a convolutional part of thesystem.Wemapping firstdefineda surjective is represented as a matrix whichconstitute the base channel of the We describeanewprotein encodingmethodwhereeachprotein protein-ligand complexes. from PDB.and 3D structuresof values binding affinity Itcontains measured binding affinities data for protein-ligand complexes,derived of experimentally resource a comprehensive dataset. (3)PDBBind:is points having ambiguous bioactivity values and created a more reliable these data points ina regression problem. We these data filtered-out of correspondingligand-target pairs thereforeitisnotsuitabletouse whole data points. They do not represent theactual binding affinities Thesedata points constitutemore than two in primaryscreen. third of observed to ligand-targetpairs,ifnobioactivitywere 10μM assigned (2) Filteredkinome. of dataset, Kdvalue Davis In theoriginal Davis: human catalyticprotein proteins in against 442kinase inhibitors kinase experiments todeterminethebindingaffinityvaluesof72 screening et al.performed Davis methods:(1)Davis: with thestate-of-the-art We usedthreedatasetstotrainoursystemandcompareresults does nothaveanydatapointinthetrainingset. receptor-ligand pair,if thecorresponding ligandand/orreceptor even features isthatofanygiven the systemcanpredictbindingaffinities inputs. Oneadvantage of incorporatingbothligandandreceptor where theaimistousebothreceptor(target)andligandfeaturesas modeling approach method. Oursystememploysachemogenomic receptor-ligandpropose amultichannel bindingaffinityprediction we targets (i.e.,receptors).Here, validation againsttheir experimental (i.e. strongbinders)canbeusedasdrug candidates forfurther affinities predicted binding with thedesired of proteins) (i.e., ligands molecules The bioactivesmall by providingbindingaffinitypredictions. processes drug discovery methods havebeendevelopedtoaidexperimental computational compound andproteinspace.Therefore,several throughput experiments isnot screening feasible forthemassive I n the field of drug discovery and development,conductinghigh- n thefieldofdrugdiscovery 2 , VolkanAtalay 1 , Tunca Doğan 1 2,3 , MariaMartin 4 , SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 53 [email protected] & [email protected] [1] H. Öztürk, A. Özgür, and E. Ozkirimli, “DeepDTA: E.and Ozkirimli, “DeepDTA: Öztürk, A. H. [1] Özgür, Binding Affinity Prediction, Chemogenomics, Receptor, Chemogenomics, Receptor, Affinity Prediction, Binding learning,” Chem. Sci., vol. 9, no. 2, pp. 513–530, 2018. learning,” Authors' Addresses: Corresponding [email protected] References: References: binding affinity prediction,” Bioinformatics, vol. 34, Deep drug-target no. 17, pp. i821–i829, 2018. “SimBoost: Ester, M. and A.Ban, Cherkasov, F. Heidemeyer, He, M. T. [2] binding a read-across approach for predicting drug-target Cheminform., vol. 9, affinities using gradient boosting machines,” J. no. 1, pp. 1–14, 2017. et al., “MoleculeNet: A benchmark for molecular machine [3] Z. Wu better than the other methods in majority of the cases. better than the other methods in majority Pursuing this additional incorporating constructed by new models can be approach, types of input protein channels. Keywords: Input Pairwise Networks, Convolutional Neural Deep Learning, Ligand, Encoding. 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SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 55 1 , Ugur Sezerman 1 , Umut Gerlevik 1 residues were monitored in the mutant structure as supporting MD structure as supporting residues were monitored in the mutant results. In conclusion, a novel the patient who has missense variant in the PRKAR1A gene was diagnosed with acrodysostosis 1 via our mechanism pathogenicity enlightened the We strategy. prioritization of binding studies as disrupted and MD ANM by mutation G171E of prevents the catalytic subunit of that subunit to the regulatory cAMP many downstream substrates in numerous from phosphorylating PKA and one from Robetta [12] was chosen after quality check. Then, cAMP check. Then, cAMP quality after chosen was [12] Robetta one from and [13] with molecules to the known positions in UniProtKB were docked structures in RCSB PDB [14]. in PRKAR1A the binding poses of cAMPs K under systems 310 for 40 ns at were simulated Wild-type and mutant of cAMP molecules from the binding ensemble NPT with 3 repeats. Loss mutation whereas they were pockets were observed upon the G171E stable in the wild-type. The equilibrated structures were used for ANM analyses. An increase in the mobility of binding pocket surrounding impact predictors such as SIFT [8], PolyPhen2 [9], MutationTaster MutationTaster [9], PolyPhen2 [8], SIFT such as predictors impact estimations. were considered for pathogenicity REVEL [11] and [10], assessments, With the combination of whole computational de novo heterozygous mutation, c.G512A, in PRKAR1A gene was determined investigate the impact of G171E, prominent variant. To as the most dynamics (MD) molecular anisotropic network modeling (ANM) and coverage homology models were built, simulations Full were performed. on genomic DNA of the patient. After WES analysis, homozygous After patient. the of genomic DNA on variants with minor allele i.e. ExAC frequency <0.1% in the databases, in [7] were kept. Heterozygous variants which are found 1KGP [6] and were excluded. The intronic variants least one of these databases at exon-intron boundaries were eliminated. from ±10 bases at far away collecting the symptoms after on Then, we prioritized variants based evidence MGI and literature search. from OMIM, Next, several mutation regulatory subunits of protein kinase regulatory subunits (PKA). PKA holoenzyme A has the phosphorylating by signaling pathways critical roles in several by catalytic subunits after leaving downstream substrates regulatory subunits [5]. Here, regulatory subunits upon cAMP binding to these 6-years-olda report we has the clinical distinctive symptoms: girl who developmental global facial features, skeletal system abnormalities, hypothyroidism. Whole exome sequencing (WES) was performed delay, Acrodysostosis refers Acrodysostosis refers to a rare type of skeletal dysplasias accompanied such as facial dysostosis, nasal common phenotypic features by retardation [1,2]. short stature, and mental hypoplasia, brachydactyly, have in Heterozygous mutations *188830) PRKAR1A gene (OMIM of the subset of acrodysostosis which as the genetic cause been found resistance is characterized with several acrodysostosis hormones, to cAMP-dependent gene encodes PRKAR1A [3,4]. #101800) (OMIM 1 ITS PATHOGENICITY MECHANISM ITS PATHOGENICITY Bozkurt Tugce of Medicine, Informatics, School of Biostatics and Medical 1. Department Istanbul, Turkey Ali Aydinlar University, Acibadem Mehmet IDENTIFICATION OF A NOVEL MISSENSE A NOVEL OF IDENTIFICATION AND GENE THE PRKAR1A IN VARIANT SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 56 242. PMCID:PMC102472 et al.TheProtein2000 Jan1;28(1):235– Res. Acids DataBank.Nucleic [14] Berman HM, Westbrook J, FengGilliland G, Z, Bhat TN, Weissig H, PMCID: PMC6323992 knowledge. NucleicAcidsRes. 2019 Jan 8;47(D1):D506–D515. [13] UniProt Consortium. UniProt: a worldwidehub of protein Server issue):W526–W531.PMCID:PMC441606 using theRobettaserver.analysis 1;32(Web2004 Jul Res. Acids Nucleic D, Chivian [12] KimDE, BakerD. Protein structurepredictionand 6;99(4):877–885. PMCID:PMC5065685 PathogenicityVariants. ofRareMissense AmJHumGenet.2016 Oct Method An EnsembleforPredicting BahetiS, the etal.REVEL: SK, [11] Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell Methods. 2010Aug;7(8):575–576.PMID:20676075 potential of sequence alterations. Nat evaluates disease-causing M, Seelow D.Schwarz JM,RödelspergerC,Schuelke MutationTaster PMC2855889 [10] mutations. Nat missense Methods. 2010 Apr;7(4):248–249. PMCID: BorkP,A, for predictingdamaging etal.Amethodandserver SchmidtS, Peshkin[9] AdzhubeiIA, Gerasimova VE, Ramensky L, Protoc. 2009;4(7):1073–1081. PMID:19561590 synonymous variantsonproteinfunctionusingtheSIFTalgorithm.Nat [8] Kumar P,S, Henikoff NgPC.Predictingof codingnon- theeffects PMC4750478 genetic variation.Nature. 2015 Oct 1;526(7571):68–74. PMCID: RM, GarrisonEP, Kangfor human HM,etal.Aglobalreference BrooksLD,[7] 1000 Genomes Project Consortium,Auton A, Durbin Nature. 201618;536(7616):285–291.PMCID:PMC5018207 variation in60,706 genetic humans. of protein-coding et al.Analysis [6] Lek M,KarczewskiKJ, EV, Minikel FennellT, BanksE, Samocha KE, 15331577 functions. Endocrinology. 2004 Dec;145(12):5452–5458. PMID: normal and abnormal Minireview: PRKAR1A: [5] Bossis I, Stratakis CA. PMID: 23043190 Endocrinol Metab. 2012 J Clin Dec;97(12):E2328-2338. resistance. but twodistinctsyndromeswithorwithoutGPCR-signaling hormone Argente J, etal.PRKAR1A and PDE4D mutations cause acrodysostosis Fryssira H,[4] Linglart A, Hiort O, Holterhus P-M, Perez de Nanclares G, 21651393 resistance. N Engl JMed. 2011 Jun 9;364(23):2218–2226. PMID: PRKAR1A mutationinacrodysostosiswithhormone M, etal.Recurrent AuzanC,Gunes Y, MenguyC,CouvineauA, [3] LinglartA, Cancel Mar;121(3):195–203. PMID:5551869 nasal hypoplasia, and mental retardation. Am JDisChild.1971 Johnson GF, et al.Acrodysostosis.Asyndromeofperipheraldysostosis, RJ, Gorlin RA, [2] RobinowM,Pfeiffer McKusick RenuartAW,VA, Med. 1968Nov27;76(46):2189–2192.PMID:5305130 References: Keywords: signaling pathways. PRKAR1A; WholeExomeSequencing;MolecularDynamics PRKAR1A; [1] Maroteaux P, Malamut G. [Acrodysostosis].Presse SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 57

2 2

values. Stars indicate indicate Stars values. - risk groups in multiple in multiple groups risk - of genes and the mutual the mutual and genes of

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Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey Antalya, University, Bilim Antalya Engineering, Computer of Department 1

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cancer driver modules, mutual exclusivity, random walk random exclusivity, mutual modules, driver cancer A) MEXCOwalk output modules when total_genes = 100. Diamond shaped nodes correspond to correspond nodes shaped Diamond 100. = total_genes when modules output MEXCOwalk A) :

, Ilyes Baali We present a novel edge novel a We present Rafsan Ahmed

1 types. MEXCOwalk identifies modules containing both well both containing modules identifies MEXCOwalk types.

MEXCOWalk: Mutual Exclusion Mutual Exclusion MEXCOWalk: to Identify Cancer Driver Modules Driver Cancer to Identify Biorxiv link: https://github.com/abu and Coverage Based Random Walk Walk Random Based Coverage and Diamond shaped nodes correspond to CGC genes. Sizes of the nodes Figure 1. source code, and useful scripts are available at: https://github.com/ useful scripts are available at: source code, and abu-compbio/MEXCOwalk [1]. Keywords: recovering cancer known genes, providing modules that are capable of classifying and tumor samples, normal and that are enriched the risk scores in specific Furthermore, cancer types. for mutations low-riskinto patients modules can stratify determined with output identifies MEXCOwalk in multiple cancer types. high-risk groups and cancer putative well-knowncancer genes and modules containing both the pan-cancerin rarely mutated are genes that the The data, data. the mutual exclusivity increase the mutual to of cancer mutations can be utilized cancer driver modules. the accuracy of identifying Results: incorporates connectivity in the form of that information approach and coverage to identify protein-protein interactions, mutual exclusivity, outperforms several state-of-cancer driver modules. MEXCOwalk in terms of pancancer data the-art methods on TCGA computational Motivation which hinders the identification heterogeneity problem the mutational of driver genes based only on mutation profiles. One way to tackle this in functional together genes act that the fact incorporate problem is to modules. The connectivity existing protein-protein knowledge present in frequencies together with mutation interaction networks of genes and

nalyses. Rows correspond to modules and columns correspond to cancer types. Colors of the matrix entries entries matrix the of Colors types. cancer to correspond columns and modules to correspond Rows nalyses. [email protected] [email protected] reflected in the thicknesses of the line segments. Color of a module denotes t denotes a module of Color segments. line Each the of right. the thicknesses the in on reflected shown are codes color the for legend The module. that of genes in mutations for enrichment and B)survival type in of the specificity gene degree Results iscancer largest module. named the with module a indicate the significance of enrichment forcancer types interms log of of Fisher's significance the exact testp References: [1] Bioinformatics Corresponding author’s address Figure 1 nodes the of Sizes genes. CGC are Edge weights are colored. the modules between edges the whereas black, are colored module the within normal and tumor samples, and low into that are patients enriched stratify can for mutations modules output in specific with determined cancerscores types. Furthermore, the risk cancer pan the in mutated rarely are that genes at: Keywords Motivation genes driver of identification the hinders which in present knowledge The connectivity modules. in functional together act genes that the fact incorporate to is protein existing modules. driver cancer identifying of accuracy the increase to utilized be can mutations cancer of exclusivity Results information in the fo state several outperforms MEXCOwalk modules. driver pancancer datain terms recovering of known cancer genes,providing modules

to Identify Cancer Driver Modules Driver Cancer Identify to Rafsan Ahmed Antalya Bilim University, Graduate Program, and Computer Engineering 1. Electrical Antalya, Turkey Turkey Engineering, Antalya Bilim University, Antalya, 2. Department of Computer MEXCOWalk: Mutual Exclusion Mutual MEXCOWalk: Walk Random Based Coverage and SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 58 [email protected], [email protected] Address Corresponding Author’s content/10.1101/547653v2 (currentlyinpressBioinformatics) References: insurvivalanalyses. test p-values of log-rank Starsindicatethesignificance Fisher's exact testp-values. terms of types in for cancer of enrichment the significance indicate types. Colorsofthematrixentries correspond tocancer and columns analyses. Rowscorrespondtomodules and survival type specificity named withthelargestdegreegeneinmodule.B)Resultsofcancer legend forthecolorcodesareshownonright.Eachmoduleis of thatmodule.The genes for mutationsin the strongestenrichment of thelinesegments.Coloramoduledenotescancertype with in thethicknesses the modulesarecolored.Edgeweightsreflected Edges withinthemodulearecoloredblack,whereasedgesbetween are proportionalof correspondinggenes. withmutation frequencies [1] Biorxivlink:https://www.biorxiv.org/ SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 59 , Mert Gür 1 , Sema Zeynep Yılmaz , Sema Zeynep 1 Dynein, Molecular Dynamics, Advanced Molecular [1] Roberts AJ, Kon T, Knight PJ, Sutoh K, Burgess SA. Knight PJ, T, Kon [1] Roberts AJ, , Elhan Taka 1 Molecular cell biology. 2013;14(11):713. Molecular cell biology. The cytoplasmic Carter AP. RD, Vale SL, Redwine WB, [2] Reck-Peterson Reviews machinery and its many cargoes. Nature dynein transport 2018;19(6):382. Molecular Cell Biology. Insights into dynein motor domain Carter AP. Gleave ES, [3] Schmidt H, function from a 3.3-Å crystal 2012;19(5):492. structure. Nature structural & molecular biology. simulations the linker domain is guided towards its post-power stroke its post-power simulations the linker towards is guided domain the transition is constructed. structure and free energy surface along Keywords: Dynamics, Mechanochemical, Thermodynamics References: and mechanics of dynein motor proteins. Nature reviews Functions mechanism remains to be elucidated. For the full length dynein motor mechanism remains to be elucidated. For domain only a very limited of all-atom MD simulations number studies simulations MD performed have recently (4), We literature. exists in the human dynein-2 in its pre-of in the presence of stroke state power MD simulations of engineered ions. Furthermore, and explicit water domain full length motor Using the were performed. dynein constructs atoms), an extensive set of (~800,000 system of our earlier study new simulations in length were performed. In these totalling 500ns of the linker straight conformation to a bend conformation. from a This is conformational change referred to as the priming stroke. Upon the power stroke of dynein takes place during microtubule binding, the best of our which the linker returns to a straight conformation. To the of Molecular Dynamics (MD) simulations knowledge, all-atom been stroke have not the priming/power linker movement during the energetics and structural performed in the literature. Moreover, polypeptides (Perrone et al. 2003a). DHC contains a C-terminal et al. 2003a). motor (Perrone polypeptides contains The head domain. N-terminal tail an (head) and domain ATP hexameric ring. a into organized modules (AAA1-AAA6) six AAA where AAA1 is the main hydrolysis occurs in AAA1- AAA4 domains, the AAA+ domain, site and necessary to for motility (3). In addition ATP the C-terminal the stalk, the buttress, and dynein also contains the linker, in a conformational change binding and hydrolysis results domain. ATP of the microtubule (MT) (1). The dysfunctionality of dyneins has been dyneins has been The dysfunctionality of (1). the microtubule (MT) of disorders. neurodegenerative diseases and associated with numerous cell the of motility in cell division and roles important Also, dyneins play on MTs how dynein generates motility and force (1, 2). Understanding is essential to develop novel chemical inhibitors/modifiers dynein of two of Cytoplasmic dynein is composed treatment. the function for chains identical dynein heavy (DHCs) and several smaller associated Dyneins are AAA+ (AAA: ATPase associated with various cellular associated with various cellular Dyneins are AAA+ (AAA: ATPase activities) motors that move progressively straight to the minus end Mert Gölcük Istanbul, Turkey University (ITU), Istanbul Technical of Mechanical Engineering, 1. Department MOLECULAR DYNAMICS SIMULATIONS OF MOLECULARDYNAMICS SIMULATIONS MOVEMENT LINKER DYNEIN THE SELECTED ABSTRACTS FOR ORAL PRESENTATIONS 60 üüsy, nn C. o6, 43 Byğuİtnu, uky / Turkey Beyoğlu/İstanbul, 34437 [email protected] website:gurlab.itu.edu.tr No:65, Cd. İnönü Gümüşsuyu, Address: Corresponding Author’s length ofitsstalk.Nature.2019;566(7744):407. is controlledbytheangleand [4] CanS, Lacey S, Gur M, Carter AP, Directionality of dynein Yildiz A. POSTER PRESENTATIONS POSTER PRESENTATIONS 62 1. MarmaraUniversity,FacultyofEngineering,BioengineeringDepartment,Istanbul Meltem NurErdöl DIAGNOSTIC ANDTHERAPEUTIC TRIALS TOCANCERS SYSTEMSBIOMARKERS REVEAL FOR DIFFERENTIAL CO-EXPRESSION ANALYSIS OF HUMAN Corresponding Author’s Address Corresponding Author’s 2016;32(5):690–6 method: a novel approach for differentialcorrelation.Bioinformatics. References: Keywords: cancer. for diagnosticandtherapeuticapplicationsof they aresignificant assigned asmodulesand these moduleswereinvestigatedwhether of these networkswere individuals. Highlyinteractingsub-clusters tumor andhealthy in differently expressed indicate whethertheywere type of cancer examinedand genetic networkswereestablishedto in each that differintheirexpression constructed fromgeneclusters profiles were Co-expression analyses wereperformedbyLIMMA. andstatistical packageof“R/Bioconductor” in the“affy” implemented as measure of theRobustMulti-ArrayAverage(RMA)expression means genes, eachdata expressed setwasnormalized by the differentially andTCGA databases.ToNCBI-GEO databases, including characterize in cancer.data Therelevantgeneexpression were obtained from public of diagnosticandtherapeuticapplications could beusedforresearch and itwasdiscussed how theseresults their histologicaldifferences cancer, lungcancer,were examinedaccordingto pancreaticcancer) study,In this and healthyconditions.[1] (gastric types ofcancer some diseased in of genes theinteractions important toreveal it is diseases, analysis. Inorderto understand thegeneticbackground expression of method totraditionaldifferentialgene as acomplementary has emerged analysis, anewapproach insystemsbiology,co-expression Differential 1 , KazımYalçınArğa Differential Co-Expression Analysis,Biomarker, DifferentialCo-Expression Cancer [1] Siska C, Bowler R, Kechris K. Thediscordant [1]SiskaC,BowlerR,KechrisK. 1 : [email protected] POSTER PRESENTATIONS 63 and 2 , Sharmila Velusamy 1 [1] Barry. L, and Hainer.MD. Dermatophyte Infections. L, and Hainer.MD. [1] Barry. Trichophyton Rubrum, Putative Targets, Balanites Targets, Putative Rubrum, Trichophyton [2] Saboo, SS, Chawan.RW, Tapadiya.GG and Khadabadi.SS. An Khadabadi.SS. and Tapadiya.GG Chawan.RW, SS, Saboo, [2] Del. International Balanite aegyptiaca ethnomedicinal plant important 2014. 4(3):75-78. journal of Phytopharmacy. [3] Hasan. MA, Rahman.MA, Ullah.MR, Rahman.MH, Noore.MS, Hossain.MA,targets Identification of potential drug Ali.Y and Islam.MS. An in silico genome analysis of Bacillus by subtractive anthracis A0248: multi-targeting potential against Trichophyton rubrum which has to be has to which rubrum Trichophyton against multi-targeting potential experiments. further confirmed by in vitro and in vivo Keywords: Aegyptiaca, Gu Flora, Docking. References: Physician. 2003. 67(1): 101-108. American Family of the selected functional classification protein, of hypothetical protein, 7 novel Finally of the targets. protein network analysis druggability and 3D structures of these were identified from T.rubrum, targets drug LC-MSwith docked and derived were modelled using I-TASSER targets B.aegyptiaca of fruit pulp of fractioned methanol extract of compounds Cyanidin-3-O- The compound using GLIDE module of Schrodinger. rhamnoside with all the targets and may have a good gave better result identify the proteins unique to T.rubrum by sequential elimination of by T.rubrum identify the proteins unique to find to microbiota and of human as well as gut proteins similar that to aegyptiaca from the edible fruit pulp of Balanites the phytocompounds without side these proteins which can lead to better treatment to target was whole the proteome of T.rubrum effects this in human. By way, which are non- identify the essentialanalysed to proteins of T.rubrum flora, non-homologous gut proteins of human and homologous to find sub-cellularto architecture and human domain against localization called Dermatophytes causing superficial called Dermatophytes infections in skin, nail and is mainly causing tinea rubrum all living organisms. Trichophyton hair of animals. Currently, in human and tinea corporis pedis, tinea cruris, and various the fungal proteins to inhibit drugs are targeting the growth of some side effects dermatophytes. But, these drugs are producing in human and human proteome interactions with drug human because of to methods computational the used The present study microbiome. gut Trichophyton, Microsporum and Epidermophyton are group of fungi are group Microsporum and Epidermophyton Trichophyton, 1&2. Research scholar, Biochematics lab, Department of Bioinformatics, Bharathiar University Biochematics lab, Department of Bioinformatics, 1&2. Research scholar, Bharathiar University, lab, Department of Bioinformatics, *Assistant Professor, Biochematics India. Nadu, Coimbatore,Tamil phytocompounds from Balanites aegyptiaca aegyptiaca Balanites from phytocompounds A Computational – targets the identified against Approach Husain Syed Abuthakir Mohamed Jeyam Muthusamy Putative drug target identification in identification target drug Putative sequential using rubrum Trichophyton the evaluating and method elimination POSTER PRESENTATIONS 64 URL: http://cdn.b-u.ac.in/faculty_data/bioinfo_dr_jeyam.pdf URL: [email protected] E.mail: 046. Coimbatore-641 Bharathiar University, Department ofBioinformatics, Assistant Professor, Dr.M.JEYAM M.Sc.,M.Phil.,Ph.D.,PGDBI Address Corresponding Author’s approach. ComputationalBiologyandChemistry. 2014.52:66-72. : POSTER PRESENTATIONS 65 , Izzie Jacques , Izzie Jacques 2 , Hassiba Outilaft , Hassiba 1 1,3 , Baris Coskun 1 these samples that regression to reconstruct the 2D model can be used predict to able are we shows that 1 well. Figure HSQC experimentas the presence of creatine even though its peak is overlapping with lysine A shows the actual HSQC experiment. The in 1H dimension. Panel C) shows that HSQC was able distinguish lysine zoomed square (Panel ) is top x axis, on peaks ( signal on their 1H that creatine. Note and overlapping; thus, one cannot distinguish at the those by just looking STOCSY for a blind prediction of a single sample. Using a set of 1H-13C single sample. Using for a blind prediction of a STOCSY dimension in 1H HSQC experiments,each peak learn how methods affects each peak in 13C dimension. With only 1H HRMAS NMR for 14 human brain tissue samples and predicting their corresponding 13C spectra, we show that we can successfully identify presence and methods Both metabolites. 39 belonging groups 104 absence of also show on one of second. We accuracy in less than a achieve 97.1% Heteronuclear Coherence Single Quantum Spectroscopy (HSQC)- NMR is performed. This analysis generates a 2D correlation plot for this analysis requires 15 hours around 1H and 13C spectra. However, we In this study, of the time frame of surgery. is outside complete and to predict 13C spectra in the HSQC experiment, methods to propose two are all. These methods performing the HSQC experiment at without (i) performing multivariate multiple regression and (ii) repurposing surgeries. In this system, the surgeon gets samples from the excision tumor cells. After HRMAS cavity for identifying possible left-over spectrum are reported have tumor-like of the cavity that analysis, parts for further resection. is possible because the feedback is This pipeline 1H is commonly used due minutes. Even though available within 20 to high sensitivity natural abundance in samples, identification and overlapping to impossible due biomarker metabolites can be of experiment case, a second peaks in 1H-NMR spectra. In that called response enables giving feedback to surgeons during an ongoing surgeons during an ongoing feedback to response enables giving HRMAS-NMRusing proposed al. et Battini for Recently, surgery. surgeries pancreatic adenocarcinoma [1]. Even if it might seem like cells tumor residual that the tumor is completely removed, it is possible Then there is the trade-off between are left over in the excision cavity. removing healthy tissue, which risks the well being of the patient and which risks recurrence further and cells in the body, leaving tumor High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Spinning (HRMAS) Nuclear Magnetic Angle High Resolution Magic can efficiently that technology is a spectroscopy Resonance (NMR) metabolites in solid tissues. detect and quantify HRMAS-NMR does need any chemical not is a must for MS extraction procedure, which technologies used in biopsy and liquid state NMR. Thus, it is frequently minutes. Rapid 20 in < obtained the results can be analyses and A. Ercument Cicek A. Ercument , 2 E. Onur Karakaslar Namer of Ankara 06800 2. University Dept. Cankaya, Computer Engineering 1.Bilkent University, Strasbourg 67081, France. Strasbourg, PA 15213 Computational Biology Dept., Pittsburgh 3. Carnegie Mellon University, PREDICTING CARBON SPECTRUM IN HSQC FOR FOR HSQC IN SPECTRUM CARBON PREDICTING SURGERY DURING FEEDBACK ONLINE POSTER PRESENTATIONS 66 of hypoxia and possibly drug resistant tumor tissue [ are distinguished in dimension 13C can and both and predict STOCSY NLSPR those peaks accurately. Creatine is an indicator prediction. reconstructed The signal 13C 1D is also provided in Panel E, which also that shows the peaks of creatine and lysi without Clearly, sample. same one cannot distinguish those byjust looking at the dimension. 1H Panel and Panel the B show D prediction for the of NLSPR distin able was HSQC that overlapping with lysine in dimension. 1H Panel the shows A actual experiment. HSQC square zoomed The (Panel shows C) HSQ to reconstruct the 2D used be can that model regression these samples of one on also show We in less asecond. accuracy than can successfully identify presence and absence of 104groups belonging 39metabolites. Both methods achieve 97.1% for brain 14human tissue NMR samplesonly and HRMAS predicting 1H their corresponding spectra, 13C we a set 1H of (i) performing multivariate multiple regression and (ii) for repurposing a blind STOCSY pr methods to predict spectra 13C experiment, in thewithout HSQC experiment performing at theall. HSQC These methods are this analysis requires around 15hours to complete and is outside of the time fr (HSQC) Spectroscopy Keywords at available is and TCBB IEEE in publication for accepted dimensions NMR HSQC in this study, all methods can be used with any other 2D spectra as well. to the surgeon during the surgery even if 1H overlapping peaks in 1H due to high sensitivity and natural abundance in samples, identification of biomarker metabolites can be impossible due to resection. This pipeline is possible because th left recurrence and further surgeries. In between removing healthy tissue, which risks the well being of the patient and leaving tumor cells in the body, which risks it is removed, possi completely al. proposed using HRMAS obtained in 20minutes. < Rapid response enables giving feedback to surgeons during an ongoing surgery. Recently, Battini et which is a must technologiesfor MS and liquid state Thus, it NMR. is frequently used in biopsy analyses and the results can efficie High Resolution Nuclear AngleMagic Spinning spectroscopyMagnetic Resonance (HRMAS) (NMR) is a technology that can http://ercumentcicek.com Corresponding author’s address a single dose,” Theranostics, vol. 8, no. 13, 2018, 501Art. no. 3584. pr [3] [ vol. 15, no. 1, 2 Med., profiling predicts patients,” clinicalof metabolism BMC tumor outcome in pancreatic adenocarcinoma: approaches [1] References: aswell. metabolites are shown both STOCSY, and NSPLR using sample spectra ofthesame NMR 13C version. inthereconstructed (E)Reconstructed are shown Lysine) yet dif NMR, dimension of HSQC version(C) Zoomed of sample in (A), this figure two metabolites shows Creatine and Lysine overlapping on1H captured with Bruker TopSpin 3.5. (B) Reconstructed version of spectra same in (A) obtained from only 1H F - 2 over tumor cells. After HRMAS analysis, parts the of cavity that tumor have cells. tumor over After HRMAS This figure shows the 1H the shows igure figure 1. This Ankara 06800–[email protected] -http://ercumentcicek.com Address Corresponding Author’s 13, 2018,501Art.no.3584. vol. 8,no. dose,” Theranostics, 500 photodynamic therapyatasingle chemotherapyand hypoxia-dampened to 499simultaneouslyenhance M. Zheng,Cai, “Tumor-targeted and L. hybrid protein oxygen carrier Wu, Ma, 498 Chen, Chen, Z. R. Liang, Luo, Z. A. Liu,Z. H.[3] Z. Tian,L. Physiological reviews.2000Jul1;80(3):1107-213. [2] Wyss M, Kaddurah-Daouk R. Creatineand creatinine metabolism. 1, 2017,Art.no.56. outcome of profiling predictsclinical patients,” BMC Med., vol. 15, no. approaches inpancreaticadenocarcinoma:tumormetabolism Heimburger, G. Averous,P. Bachellier, andI.Namer, “Metabolomics References: as well. same sampleusingNSPLRand STOCSY, both metabolites areshown in thereconstructedversion.(E)Reconstructed13C NMR spectraofthe Zoomed version of (B), samepeaks(Creatineand Lysine) areshown (D) on 13Cdimension. NMR, yet differentiable of HSQC dimension and Lysinetwo metabolitesCreatine figure shows overlapping on1H from only 1H-NMR sample.(C)Zoomed version of sample in(A), this TopSpin 3.5. (B) Reconstructedversionof same spectrain(A)obtained spectra captured with Bruker (A) Original and itsreconstructedversion. 1. Figure Nmr; Hsqc;SurgeryFeedback Keywords: ieeexplore.ieee.org/document/8730423 been acceptedforpublicationinIEEE TCBB and is availableat https:// be usedwithanyother2Dspectraaswell.Thisworkhasrecently in thisstudy,on 1H-13CHSQCNMRdimensions allmethodscan though weexperiment Even are inconclusive. 1H HRMASNMRresults to provide accurate feedback to the surgeonduring the surgery evenif resistant tumor tissue [2,3]. Thus, our approach can makeitpossible peaks accurately.an indicatorofhypoxiaandpossiblydrug is Creatine andbothNLSPRSTOCSYin 13Cdimension canpredictthose also showsthat the peaksof creatine and lysine aredistinguished Panelalso providedin is The reconstructed1D13Csignal which E, by ourprediction. and lysine we wouldbeabledistinguishcreatine the samesample.Clearly, without performing theHSQCexperiment 1H dimension.Panel BandPanel DshowthepredictionofNLSPRfor otein oxygen carrier to 499simultaneously enhance hypoxia ] C experimentC as well. Figure

Z. Luo, H. Tian, L. Liu, Z. Chen, R. Liang, Z. Chen, Z. Zheng, A. Ma,498M. Wu, and L. Cai, “Tumor Wyss M, Kaddurah M, Wyss “Metabolomics Namer, I. and Bachellier, P. Averous, G. Heimburger, C. Cicek, A. Imperiale, A. Faitot, F. Battini, S. ntly detect and quantify HSQC for Online : 1 E. Karakaslar Onur -

Bilkent University Bilkent M 13C HSQC experiments, HSQC 13C methods learn each how peakdimension in 1H affects eachdimension. peak in 13C Predicting Spectrum Carbon ultiple multivariate regression; multivariate ultiple

017, Art. no. 56. 67081, France. This figureshowsthe 1H-13C HSQCNMR of Sample 3 - NMR is performed.NMR This analysis generates correlation a 2D plot for and 13C spectra. 1H However, Multiple Multivariate Regression; Stocsy; Peak MultipleMultivariateRegression; Prediction; - NMR spectra.NMR In that case, a secon - [1]S. Battini,F. C. Cicek, Imperiale,A. Faitot, A. guish lysine and creatine. Note that their peaks 1H ( signal onxaxis, ontop ) is overlapping; thus, Daouk R. Daouk Creatine and creatinine metabolism. Physiological reviews. 2000 Jul 1;80(3):1107

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POSTER PRESENTATIONS 67 1,2 A. Ercument Cicek A. Ercument , 1 SNPs selected by SPADIS are likely to be epistatic, since the algorithm selected by SPADIS SNPs We diversify select is designed to the set and complementary SNPs. to generate its candidate set created a pipeline that first uses SPADIS of using this set for all-pairs epistasis testing for epistasis test. Instead the guide to use it we FPs, of number large a stillreturn which would let it only these algorithms: Linden. We example of the art of state and a small regions (selected SNPs form LD trees over SPADIS-selected a small set of loci that is the most predictive (i.e., 100) of a continuous from each other further away selectingSNPs study, in GWA phenotype is designed for This (SPADIS) method results in predictive better power. SNP set it generates as the and feature selection for multiple regression it results better R2 values. in contains diverse SNPs and complementary we conjecture FDR can be controlled by guiding the that In this study, the set of The hypothesis is that prioritization algorithms using SPADIS. 10 TP, ~1800 FP). FPs are SNPs that are being tested not crossing not tested are being that are SNPs FPs FP). ~1800 TP, 10 the Bonferroni-corrected significance threshold. While these algorithms aim at minimizing the number of predictions and the number of tests, none has aimed at controlling for the false positive rate, which is an indication for life scientists important using these algorithms. In an different manner avoids LD in a et al. (2018) Yilmaz study, orthogonal for looking while that show They prediction problem. phenotype for and first generates trees that represent genomic regions (LD forest). Then, represent genomic regions first generates trees that genomic of decides if this pair these trees it of the roots comparing by are parsed Trees epistasis test. promising candidate for regions is a tested only if the estimation in depth first manner and leaf pairs are at higher levels provides a value larger than a threshold. All three The fundamental are in LD. avoiding testing pairs that methods aim at problem in all of the above-mentioned is the high number algorithms (FDR) is ~0.99, false discovery rate Linden’s (i.e., false positives (FP) of rather than discarding pairs from the search space and control for type control for type pairs from the search space and rather than discarding can keep performing tests, in the user the this approach, In error. I until a desired number of significant the algorithm, order specified by provide is to idea arise, the While false negatives may found. pairs are minimum number of true positives with the user a tractable number Koyutürk and Cowman ensure statistical power. tests performed to of (2017), introduced the state-of-the-art LINDEN algorithm. The method valuable insights but they alone do not account for the full heritability. full heritability. the for account not alone do they insightsbut valuable Statistically significant two or more loci interactions between is called epistasis to complex and it has been shown to contribute genetic traits. Given a million in variants a genome, a trillion tests are required to process all SNP pairs. Thus, this procedure is not only computationally multiple hypothesis to due also lacks statistical power prohibitive, but prioritize the tests to be performed approach is to A popular testing. Analyzing single loci associations in GWAS have many provided Analyzing single loci associations in GWAS Gizem Caylak Ankara 06800 Dept. Cankaya, Computer Engineering 1. Bilkent University, Pittsburgh PA 15213 Biology Dept., Mellon University, Computational 2. Carnegie CONTROLLING FDR IN EPISTASIS TEST TEST EPISTASIS IN FDR CONTROLLING PRIORITIZATION POSTER PRESENTATIONS 68 Keywords: 3501Career Grant to 116E148 A.Ercument Cicek.TUBITAK r (k=1000). This figure that shows get we only a FPs, few given TPs,our more pipeline when is used. Moreover, the total Complete results in are shown Figure 1A epistatic pairs, only 10passing the significance threshold 0.1 (Bonferonni corrected). This resulted in a precision of 0.005 used and Koyutürk, in (Cowman 2017). First, run we Linden onthis dataset and, it returned 1792recipr approach onthe Wellcome Trust data, Case Control 2Diabetes Type Consortium which also was GWAS (T2D) (WTCCC) likely of space search epistatic measu apruned but pairs. We the genome cover Thus, set. this from pairs epistatic likely pick to neighbors) 1 - all toits generate candi created apipeline that first We SPADIS uses SNPs. are likely epistatic, to be selected SPADIS since to theis diversify by algorithm designed the select set and complementary that can FDR be controlled byguiding the prioritization algor setthe it SNP generates it contains diverse SNPs complementary results and in better values. R2 In this study, conjecture we other results in better predictive power. This method (SPADI loci (i.e., 100) that is the predictive study, most selecting of acontinuous in phenotype further GWA SNPs from each away (2018) avoidsin a different LD manner and for pheno positive rate, which is an important indication for life scientists using these algorithms. In an orthogonal study, Yilmaz et predict of number the minimizing at aim algorithms algorithms: Linden. We let it only form LD trees over SPADIS over trees LD form let it only We Linden. algorithms: TP, ~1800FP). arethat FPs SNPs are being tested not crossing the Bonferroni above threshold. All three aim at methods avoiding testing pairs that are in fundamental The LD. problem in all of the parsed in depth first manne Tr test. epistasis for candidate promising a is regions genomic of pair if this it decides trees these of roots the comparing Analyzing single state positives of with number tests minimum performed to ensure statistical and Koyutürk power. (2017), Cowman introduced the of significant type I error. In this approach, the performing user keep tests, can in the order specified bythe algorithm, until a desired popularA approach Thus, this procedure is not only computationally prohibitive, but also lacks statistical powerdue to multiple hypothesis tes contr S heritability. untime of the pipeline is only one forth of the Linden B [2] [2] significance threshold (0.1, green line). X [1] [1] References: clearlySPADIS minimizes byguiding FPs Linden. and LINDEN http://ercumentcicek.com SPADIS increases the precision substantially SPADIS (0.1) with Bonferroni correction based onthe of number tests performed byeach method. Table that shows the guidance of neighbors are also input to Lind

and without the for guidance varying of SPADIS number of selected For each SNPs. SPADIS F Corresponding author’s address

pairs epistasis testing which would still return a large number of FPs, we use itpairs use epistasis still the to state we guide FPs, th testing of would the of art return which example alarge number shows the significance pairs of lineshows detected, green the significance denotes where approach level for each passed to be igure 1. http://ercumentcicek.com Bilkent University, Cankaya, Ankara 06800 - – [email protected] Address Corresponding Author’s [2] Yilmaz, S. et al.(2018).bioRxivhttps://doi.org/10.1101/256677 research, 45(14),e131–e131 References: SPADIS clearlyminimizesFPs byguidingLinden. k=1000. to pairsforvisualization. is justrandomlyassignedvalues threshold(0.1, Bonferonni correctedgreenline). X significance – axis (y-axis) levels the significance ofeachreported pair (dots) given the Panel B:Eachapproach, SPADIS+LINDEN and LINDEN-only, we show the precision substantiallyascompared increases to LINDEN only. performed by each method. Table shows that the guidanceof SPADIS threshold (0.1) with Bonferronicorrectionbased on thenumberoftests pairs passingsignificance number inparenthesesdenotesthesignificant SPADIS-selectedneighbors arealsoinputtoLinden.The SNP 18closest guidance ofSPADISSNPs. forvaryingnumberofselected For each significant pairsreturnedbyLINDENwithandwithout the reciprocally 1. Figure Optimization, Fdr Keywords: by TUBITAK ErcumentCicek. 3501CareerGrant116E148toA. being supported project is 1+ hour).This vs run(~15min Linden-only used. Moreover, thetotal runtime ofthepipelineisonlyoneforth shows that we getonlyafewFPs, givenmoreTPs, whenourpipelineis level to be significance passed for each approach (k=1000). This figure of pairsdetected, wheregreenlinedenotesthe shows thesignificance SPADIS theprecisionsubstantially, improves up to ~55%. Figure 1B Theguidanceof Figureare shownin 0.0055. Complete results 1A. of Thisresultedinaprecision corrected). threshold 0.1(Bonferonni reciprocally significantepistaticpairs,only10 passing thesignificance 2017). First, we run Lindenonthisdataset and, it returned 1792 (T2D) GWASwas alsousedin(CowmanandKoyutürk, data, which Wellcome Trust Case ControlConsortium(WTCCC) Type 2 Diabetes using thisapproachonthe in precision the improvement measure epistatic pairs.Weof likely but aprunedsearchspace the genome number ofteststhat cover LINDEN doesnothaveperformstillasheer set. Thus, pairs fromthis epistatic likely to pick number ofneighbors) - ibute to complex gene of SPADIS LINDEN + - LINDEN Yilmaz, S. et al. (2018). al. (2018). et S. Yilmaz, Cowman, T. andKoyutürk, (2017).Cowman, M. mentioned algorithms is the high numbe

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POSTER PRESENTATIONS 69 , 1 , Tugrulcan Elmas , Tugrulcan 1 1,3 ,, Necmi Acarsoy ,, Necmi 1 A. Ercument Cicek A. Ercument , 2 , Metin Can Siper , Metin Can 1 , Xinjian Qi 2 reaction, and finally, can list the compartments associated with the reaction, and finally, selected metabolites. The browser interface with KEGG is integrated and can be used to navigate whenever or KEGG data KEGG pathway the model. External databases available by is made molecule data ChEBI and KEGG are linked when the information such as UniProt, graph queries is provided in the reconstruction. DORMAN’s built-in can be used to search related entities for topologically in the graph tool can render the full network (not just pathways) with compartments pathways) tool can render the full network (not just add zoom in/out; move, and lets users the model (e.g., to interact with Unlike 1 panels A and B. or remove nodes in – See the network) Figure its counterparts, hierarchical navigation feature of DORMAN allows users to efficiently browse through connected entities in the model, browsing the reactions of instance, users can start For level-by-level. select the metabolites of a selected then can list and a model, and the literature and provides a user friendly the literature and provides a user friendly and efficient platform for visualizing the models and querying with multiple interfaces. accessing, While above mentioned systems are mostly focused on the integration provides novel complementary DORMAN process as explained above, the networks. First of topology analyze the features, which lets users to static of all, current systems provide either no or only pathway-level visualization (i.e., Escher of BiGG) while DORMAN’s visualization maps research interest lead to the development of online systems/repositories that store existing reconstructions and enable new model generation, analyses. While support features that integration and constraint-based available, current systems lack the widely reconstruction are model in analyzing the topology means to help users who are interested of the Database the reconstructed networks. Here, we present of Reconstructed (DORMAN); a central Metabolic Networks that database collects available genome-scale from reconstructed metabolic networks spectrum of applications such as: (i) metabolic engineering, (ii) such as: (i) metabolic engineering, spectrum of applications (iii) interpretations of phenotypic screens, model-directed discovery, (v) studies of evolutionary properties, and (iv) analysis of network processes. of the studies have made use of the topology Also many networks for understanding disease mechanisms. Ongoing interest in network reconstruction and analyses comes with the need for This everlasting tools to work on the resulting models. computational With the advancements in the omics platforms and the availability in the omics platforms and With the advancements researchers data, throughput of affordable high been able to have capture the genome-scale chemical of the cell composition and integrate this knowledge into genome-scale reconstructed metabolic be models have proven to organisms. Reconstructednetworks of diverse metabolism in a the understanding for indispensable tools Furkan Ozden Bryan Marty Ankara 06800, Dept. Cankaya, Computer Engineering 1. Bilkent University, Mellon OH 44106 3 Carnegie EECS Dept., Cleveland Reserve University, 2. Case Western Biology Dept., Pittsburgh PA 15213 University, Computational DORMAN: DATABASE OF RECONSTRUCTED RECONSTRUCTED OF DATABASE DORMAN: NETWORKS METABOLIC POSTER PRESENTATIONS 70 http://ercumentcicek.com Bilkent University, Cankaya, Ankara 06800 - – [email protected] Address Corresponding Author’s Bioinformatics 29(6):815-816, 2013 for accessing, analysingand manipulatingmeta-bolicnetworks”, [6] Ganter, org: a websiteandreposi-tory M.etal.,“MetaNetX. Bioinformatics, 13:6,2012 models andda-tabases”,BMC and reactionsspanningmetabolic [5] Kumar, etal.,“MetRxn:aknowledgebaseofmetabo-lites A. scale metabolicmodels”,SystBiol,5:20,2011 [4] Pabinger, S. etal., “MEMOSys: Bioinformaticsplatform for genome- 28(9):977-82, 2010 metabolicmodels”,Nat Biotechnol, and analysis of genome-scale [3] Henry, C.S. etal., “High-throughput generation, optimization Research, 44(D1):D515-D522,2016 models”, NucleicAcids standardizing, andsharinggenome-scale etal.,“BiGGModels:Aplatformforintegrating, [2] King, Z.A. reconstructions”, BMCBioinformatics,11:213,2010 metabolic knowledgebase oflargescale and Genomic Genetic References: matchesareshown. andtop-5 top-3 top-1, D: q=5,k3andPanelfor Results q=3,k5(mostrelaxed). E: Panel matches: similar less versus q=7,k1(moststrict);Panel C: to of tune the tool the strictness in termsofreturningexactmatches are obtained from theMetaNetX system. Threeconfigurationsareused iJR904and H. sapiensRecon1. The ground E.coli truth ma matches iAF1260, Networks tool on thefollowing4models:IRC1080, E.coli model (B).Parts C,DandEshowtheperformanceofCompare Figure 1. Workbench, OnlineRepository Keywords: http://ciceklab.cs.bilkent.edu.tr/dorman literature and available repositories.Systemisonlineand available at the continuously updated and is by screening repositories from external Currently, the database contains 199 SBML-based modelsobtained the performance of the tool with respect to a ground truth matching. See Figurename matchingoftheentities. 1,panelsC,DandEfor approximate using nomenclature, that donotfollowthesame models or metabolites. DORMAN also provides an interface to compare such as searching for entities ink-hop neighborhoods of reactions Keywords: available repositories.and the database contains 199 match also providesmetabolites. aninterface to DORMAN models that compare donot follow the nomenclature, same using can be used to search for topologically r such are as UniProt, linked and KEGG the ChEBI when information is provided in the reconstruction. built DORMAN’s can data and reaction, level counterparts, hierarchical nav interact with the model (e.g., in/out; zoom move, add or remove nodes in the network) maps analyze the topology of the networks. First of all, current systems provide either noor only pathway mostly focused ont interfaces. multiple with models the querying and visualizing accessing, for platform efficient and a central that database collects av analyzing the topology of the reconstructed networks. While features that support model reconstruction are widely available, current systems lack the to means help users are who systems/repositories that store existing reconstructions and enable model generation, new integration and constraint computational tools to onthe work resulting models. networks for u analysis of network properties, and (v) studies of evolutionary processes applications of as: (i) such spectrum engineering, metabolic (ii) model networks of organisms capture the genome W

A ith the in advancements the platforms omics the and availability of affordable high throughput data, researchers havebeenab

BMC Bioinformatics,BMC 11:213, 2010 [1] References: q terms of returning exact matches versus less similar matches: Panel C: q = 7, k = 1 (most strict); Panel D: q = 5, k = 3 and truth matchesma are obtained from the MetaNetX system. T tool Networks Compare F Corresponding author’s address Bioinformatics 29(6): 815 [6] Bioinformatics, 13:6, 2012 [5] [4] 28(9):977 [3] Research, 44(D1):D515 [2] Furkan Ozden

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by of BiGG) while DORMAN’s ing of the entities. the of ing Ganter, M. et al., “MetaNetX. org: a website and reposi and website a org: al., et “MetaNetX. M. Ganter, Kumar, A. et al., “MetRxn: a knowledgebase of metabo genome for platform Bioinformatics et S. al.,Pabinger, “MEMOSys: C.S. Henry, al., et Z.A. King, reconstructions”, large scale of metabolic knowledgebase Genomic and Genetic aBiochemical J. etSchellenberger, al., “BiGG: DORMAN: - level. For instance For level. and f k 1

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POSTER PRESENTATIONS 71 1 and Matthias Schmidt and Matthias 2 Bdellovibrio Bacteriovorus; Biofilm; Pseudomonas Putida; Bdellovibrio Biofilm; Bacteriovorus; Pseudomonas , Antonis Chatzinotas , Antonis S., Tribedi P. (2016). Biofilm, pathogenesis and prevention – a journey P. Tribedi S., [3]https:// Arch. Microbiol.198(1):1–15. break the wall: a review. to publishes-list-of- www.who.int/news-room/detail/27-02-2017-who bacteria-for-which-new-antibiotics-are-urgently-needed. [4] Dashiff, A.,the predatory of human pathogens by Predation et al., (2011). bacteria Micavibrio aeruginosavorus and Bdellovibrio bacteriovorus. E., Strauch [5] 431-44. Microbiol. 110(2): Appl Sebastian Beck S., J. agent capable to remove biofilms of pathogenic. Keywords: Biological Control Pathogens; References: Chatzinotas A., The Life on It: Have an Ion (2019). SchmidtM. [1] Said N., Cycle of Bdellovibrio bacteriovorus Viewed by Helium-Ion Microscopy; Bhattacharjee Das B., Sarkar S., P., Gupta [2] 1800250. Biosys. 3, Adv. on the P.putida biofilms we analyzed the predator-prey system during biofilms we analyzed the predator-prey on the P.putida hours. 24 and 8 6, 4, 2, i.e. inoculation, after points different-time on PVC coverslip old, grown in LB media were 2 days biofilms P.putida conclude that the microscopic we data From shaking. 28°C without at biofilm delay the the extracellular polymeric substances in the P.putida life-cyclepredator's the prey from being attacked. protect does not but be a suitable biological promise to might hold Thus B.bacteriovorus P.aeruginosa and Enterobacteriaceae, all of which are Gram-negative P.aeruginosa of B.bacteriovorus strong biofilm formers[3].The capability and to remove biofilms[4]bacterial makes it an interesting candidate B. we examined the ability of for “living antibiotics”[5].In this study, biofilms. Scanning electron bacteriovorus HD100 to prey on P.putida biofilms putida are removed microscopy to reveal if P. was employed HD100 see the effect of B.bacteriovorus bacteriovorus HD100. To by B. In our previous study we showed that B.bacteriovorus HD100 attached HD100 B.bacteriovorus we showed that In our previous study prey cells .putida within 10 min of co-incubation;to Pseudomonas first in early stage were seen bdelloplasts after 20 min and bdelloplast biofilms of h[1].The formation approximately 2 lysis occurred after by pathogenic bacteria can increase their resistance to therapeutic substances [2]. The 2017 WHO list of the most critical bacteria with antibiotic-resistance respect to comprises Acinetobacter baumannii, negative bacteria and that has therefore been considered as promising has therefore been considered that negative bacteria and control of unwanted bacteria in an environmental, biological agent for the space of inters the periplasmic it medical context biotechnological or spherical-shaped which is transform into a The bdelloplast. the prey, prey consuming host by divides within the and Bdellovibrio elongates the offspring are the Bdelloplast lyses and cell components. Finally released. This life-cycle is three to four hours. to last for about known Bdellovibrio bacteriovorus is a bacterial predators that attacks Gram- that is a bacterial predators Bdellovibrio bacteriovorus 1 Nedal Said for Chemical Microscopy; ProVIS - Centre of Isotope Biogeochemistry, 1. Department Research – Centre for Environmental Helmholtz of Environmental Microbiology 2. Department UFZ, Leipzig/Germany ABILITY OF BDELLOVIBRIO BACTERIOVORUS TO TO BACTERIOVORUS BDELLOVIBRIO OF ABILITY BIOFILMS BACTERIAL REMOVE POSTER PRESENTATIONS 72 Corresponding Author’s Address Corresponding Author’s (pp. 131-152).. and Therapeutic Agents?,InPredatoryNew Biochemicals Prokaryotes Appel B.PotentialOrganisms: (2007). Bdellovibrio andLike Sourcesfor : E-mail: [email protected] E-mail: POSTER PRESENTATIONS 73 Ege University, Faculty of Science, Faculty Ege University, [1] Sainsbury S, Bernecky C, Cramer P. Structural basis P. C, Cramer Bernecky S, Sainsbury [1] REST; DNA; Modelling Molecular REST; , Cenk Selçuki 1 [3] S.C.C. James Stewart, MOPAC2016, (2016). http://openmopac.net/. James Stewart, MOPAC2016, [3] S.C.C. Gaussian multiplicative chaos and V. [4] Rhodes R. and Vargas ISBN: 315–392. (11): 2014; Surveys, Probability review. a applications: 978-1-935522-03-4 Corresponding Author’sAddress: Department of Biochemistry [email protected] References: II. Nat Rev Mol Cell Biol.of transcription initiation by RNA polymerase 2015;16(3):129–143. PubMed PMID: 25693126 Santana, Mangueira, S.R. G.A. Maia, Urquiza Carvalho, C.P. [2] J.D.C. Rocha, GPU Linear Algebra Libraries and GPGPU Cabral, G.B. L.A.F. Semiempirical Quantum for Accelerating MOPAC Programming 3072–3081.(2012) 8 Comput. Theory Chem. J. Calculations, Chemistry doi:10.1021/ct3004645. The results revealed that serine residues in the active DNA binding regionThe results revealed that serine residues in bonds with DNA.of REST protein can form hydrogen (–H) non- Other observed.not were forces Waals der van as such interactions covalent Truba on TUBITAK-ULAKBIM Most of the calculations were performed resources. Keywords: software [2,3] with PM6- D3H4 method and Gaussian09 software [4] withsoftware [2,3] with PM6- D3H4 method and and frequency level were used for optimizations B3LYP-6-311++G(d,p) Using the calculated results, thecalculations for each investigated system. relative energies.on the determined based structures were most stable Studio Visualizer 2019Discovery presented by Molecular structures were software. In this study, the non-covalent CC nucleotide and interactions between AG In this study, the single-dimer sequences in DNA, stranded known to be effectively of the DNA binding region and the triple serine structure bound to REST, transcription factor (REST) were silencer of RE-1 (Suppressor Element-1) 14 Spartan purpose, this For methods. computational by investigated for the serine trimersprogram was used to determine initial structures analysis. The MOPAC2016 and nucleotide dimers by conformational Transcription factors (TF) are proteins which are consisted of combinations factors (TF) are proteins which are Transcription structure acids. The amino acid sequences in the of many different amino provide three-dimensional of the external structure due to the entalpy value non-covalentthe and environment itsin be must protein A interactions. effectivelyfunction: its perform to structure conformational 3D optimal binding to DNA [1]. MODELLING Fulya Çağlar Program of Health Sciences, Health Bioinformatics 1. Ege University, Institute of Science, Department of Biochemistry 2. Ege University, Faculty INVESTIGATION OF INTERACTIONS BETWEEN BETWEEN INTERACTIONS OF INVESTIGATION ACIDS NUCLEIC AND POLYPEPTIDES OF SUBUNITS MOLECULAR BY (MONOMERIC-TETRAMERIC) POSTER PRESENTATIONS 74 1. YeditepeUniversity,DepartmentofGeneticsandBioengineering Ezgi Tanıl BIOMOLECULESSIGNIFICANT PRODUCTION OF COMMERCIALLY DATA TO LEARNABOUT PHYSIOLOGY FOR MAKING MOST FROM FERMENTATION 1 , EmrahNikerel from microorganisms: from production topurification. BrazJMicrobiol. from microorganisms: CO, MagalhãesPO, PessoaMA, Oliveira AJr. Biopharmaceuticals Mazzola PG, Rangel-Yagui Cardoso SL, Nascimento L, Oliveira- CA, References: Keywords: exometabolome levelsofchemostat experiments. from relativelylesslaboriousbatch fermentationcanbeusedtopredict than onmATP. Itwasalsoshownthat, parameters obtained using data It was alsofound that effects ofP/OchangeonKxwasmuchlower 2.3±1.7 and 5.25±2.75 for publishedP/Oratioof1.45. respectively Kx andmATPproblem. in asingle various sources werefoundtobe and partialleastsquaresbutalsointegrationofdatafrom collinearities the application of regression diagnostic methods such as detection of ( the ATP balance canbe reformulated as a linear regressionproblem resulting fromdifferent fermentationmodes[3-5].Interestingly,sources oleophila ATCC20177data frompublishedfermentationofdiverse of citric acidproduction model todescribethekinetics by Candida constants (ii)setup and analysis of unstructured black-box kinetic associated (Kx)andnon-growth(mATP) maintenance parameters related to of energetics growth and production as growth The aimofthisstudyistwo fold: (i)presentaframeworktoestimatethe decrease productioncosts. thereby substrate useefficiency improve energetics, understand cellular to better crucial parameters is of physiological data forestimation use ofthesefermentation datasets availableinliterature.Efficient However, obtaining theseparametersischallenging, despite several organisms forgrowth, cellular maintenanceand product formation [2]. e.g. ATP maintenance parametersto determine theenergeticneedsof of physiological parametersspecificto the production host of interest, a number calls monitor andcontrolqualityduringproduction,which ATP balances, notbut only foroverallproduction also to efficiency as as well andotherelemental carbon,electron investigation careful activity and productivity manner.in themostefficient This requires with highandspecific biomolecules to producethese in designing therapy applications [1].Suchbioprocesseshavetheirown challenges various in based productsforusing DNA-RNA proteins, andeven methods such ashormones, impractical toproduceusingchemical production of tailor-made, specificdrugs that are impossibleor importance duetoitslowerproduction cost andallowing increasing 1980’s, Since microbial production of biopharmaceuticalshasgained α =Y . β ) to estimate these parameters fromdata, ) toestimatethese allowing notonly Energetic Parameters, Black-Box KineticModel,AtpBalance [1] JozalaAF, GeraldesDC,Tundisi Feitosa Breyer VA, LL, 1 POSTER PRESENTATIONS 75 Yeditepe University, Department Department University, Yeditepe : 34755 Ataşehir, İstanbul e-mail: [email protected] high specific pH dependent active transport system in yeast Candida high specific active transport pH dependent 146–161. Aug;8: Biotechnol. 2005 Electron. J. 20177. oleophila ATCC glucose by from production Citric acid HJ. Rehm [5] Anastassiadis S, continuous and under batch, 20177 oleophila ATCC yeast Candida 26-39. Jan;9: Biotechnol. 2006 J. cultivation. Electron. batch repeated Author’sCorresponding Address Cad, Kayışdağı 26 Ağustos Yerleşimi, of Genetics and Bioengineering, of Penicillium chrysogenum. Biotechnol Bioeng. 2001 Jan;72(2):185- 2001 Bioeng. Biotechnol chrysogenum. of Penicillium PMID: 11114656 93. PubMed citric Continuous acid C, Rehm HJ. Wandrey S, [3] Anastassiadis limitation at under nitrogen by Candida oleophila fermentation 695– Jul;21: Microbiol. Biotechnol. 2005 J. ratio. World constant C/N 705. a citric acid secretion by Continuous Rehm HJ. Anastassiadis S, [4] 2016 Dec;47 Suppl 1(Suppl 1): 51-63. PubMed PMID: 27838289 PMID: PubMed 1): 51-63. 1(Suppl Dec;47 Suppl 2016 Vinke JL, Heijnen JJ. WT, [2] vanGulik WM, Antoniewicz deLaat MR, high-producing strain in a penicillin production and growth of Energetics POSTER PRESENTATIONS 76 Turkey 1. EgeUniversity,FacultyofScience,DepartmentBiology,MolecularBiologySection,İzmir/ Muhammet Uslupehlivan ST3GAL SIALYLTRANSFERASES GLYCOSYLATION ANDCMPBINDING OF HUMAN COMPUTATIONAL ANALYSIS OF THESTRUCTURE, Biological Chemistry. 1994269(2),1394-1401. that sialylatesglycoprotein andglycolipidcarbohydrate groups.Journalof [6] KitagawaH, Paulson, JC.Cloningofanovelalpha 2,3-sialyltransferase approach. Glycobiology. 200515:805-817. genes: a phylogenetic sialyltransferases and sialyltransferase-related MolliconeR,DelannoyP,[5] Harduin-Lepers A, OriolR.Theanimal 83(8), 727-737. Julien S, Delannoy P. The human sialyltransferase family, Biochimie. 2001 Vallejo-Ruiz[4] Harduin-Lepers A, V, Samyn-Petit Krzewinski-Recchi MA, B, phenotype bysialylatedglycans.CancerMetastasisRev. 200231:501. [3] SchultzMJ, SwindallAF, Regulationofthemetastaticcell BellisSL. acids. Cell.Mol.LifeSci.199854,1330e1349 [2] Traving C, Schauer R. Structure, function and metabolism of sialic sialic-acid-recognizing proteins,Nature.2007446(7139),1023. References: Binding, MolecularDocking, 3DMolecularModelling Keywords: an effectiveapproach. may havebeen in preventionofmetastasisdifferenttypescancer the controlofSTactivity therapeutic targetsformetastasissince specific and generatingposition inhibitors of selective contribute todesign we suggestthat VI subfamilies.Inconclusion,predictedpositionsmay the sialylmotifsrelationwith the CMP binding positions inST3GalIII- dimensional structureusingmoleculardockingand first demonstrated on three- family of humanST3GALenzyme CMP bindingpositions ST3Gal V and their relationwithsialylmotifs.In addition, we predicted in VI, mucintypeglycosylationinST3GalIII and O-GlcNAcylation WeST3Gal IVand positions in N-glycosylation time foundforthefirst binding site of human ST3GAL family usingcomputational methods. family.we investigatedthestructure,glycosylationandCMP Herein, structure, glycosylation,andCMPinteractionofhumanST3GAL but, there isapaucity of informationabout the three-dimensional There are many reports on the ST3GAL function inmammals[10,11] through an galactose residuesofglycochains acid to the terminal The membersoftheST3GALfamilytransfersialic ST3Gal I,II,III, ST3Gal IV, ST3GalV, andST3GalVI. STsare subfamily inmouseandhumanswhich anddividedintothesix to the glycanunits[4,5]. ST3GAL familyisoneof the mostimportant 3]. All STs catalyzetheterminalsialicacidaddition from CMP donor and host-pathogencell-cell interactions and metastasis of cancer [1- signalling,as cell such to manybiologicalprocesses recognition, cellular (STs)Sialyltransferases which arerelated arethefundamentalenzymes HumanSt3galFamily, Sialylmotif, Glycosylation, CmpLigand [1] Varki Glycan-based interactions involvingvertebrate A. 1 , EcemŞener 1 , Savaşİzzetoğlu 1 α 2,3-linkage [6-9]. POSTER PRESENTATIONS 77 2, 3- α 2, 3-sialyltransferase α 1, 3GalNAc 1, 3GalNAc β Ege University, Faculty of Science, Faculty Ege University, : substrate specificity in mammalian glycosyltransferases: porcine ST3Gal-I. mammalian glycosyltransferases: porcine substrate specificity in 2013 23(5), 536-545. Glycobiology. Corresponding Author’s Address Molecular Biology Section, Department 35100, of Bornova/İzmirBiology, E-mail: [email protected] ... & Marth JD. The ST3Gal-I sialyltransferase controls CD8+ T lymphocyte ST3Gal-I sialyltransferase controls CD8+ The ... & Marth JD. 2000 12(3), O-glycanhomeostasis by modulating Immunity. biosynthesis. 273-283. [10] Rao Rich FV, JR, Rakić B, Buddai S, Schwartz Johnson MF, K, insight into mammalian sialyltransferases.Strynadka NC. Structural ... & 2009 16(11), 1186. Molecular Biology. Nature Structural and [11] Rakić B, Rao FV, Freimann Structure-based and mutagenic analysis of mechanism Withers SG. K, Wakarchuk W, Strynadka NC, & communications. 1996 228(2), 324-327. 1996 228(2), communications. K., Kiso M, Furukawa Ishida H, H, Miyazaki S, Fukumoto T, [8] Okajima K.... & Furukawa of a novel Molecular cloning on glycoproteinsII lactosamine structures that sialylates type (ST3Gal VI) 274(17), 11479- 1999 Chemistry. Journal of Biological and glycolipids. 11486. DM, Page KB, Richardson CJ, Simmons Hiraoka N, Chui D, JJ, [9] Priatel [7] Kim YJ, Kim KS, Kim SH, Kim CH, Ko JH, Choe IS, ... & Lee YC. Lee ... & IS, Choe JH, Ko CH, Kim Kim SH, Kim KS, YJ, [7] Kim human Gal of and expression cloning Molecular research biophysical and Biochemical II). (hST3Gal sialyltransferase POSTER PRESENTATIONS 78 1. EgeUniversity,Dept.ofBiology,İzmir,Türkiye Youchahou PoutougnigniMatenchi Population inCameroon structure of Domestic Cavies (Cavia porcellus) A Molecular Characterization andgenetic genetic improvement schemefordomesticcavy inCameroon. ecological zone inCameroon.Thesepreliminary resultspavetheway to up of animals from both region of individual inthe bimodal agro- group made widespread group, and amixture more closedacenter is distinct groups: an easterngroup 3genetically which revealed analysis The phylogenetictreeof individuals and the population structure the totalpopulation. the lowerdifferentiationandtendencyofhomogeneity in reinforces flow, whichisfoundhigh(20.866 ± 5.309), between thepopulations to inhomozygotes at an excess the locuslevel.Inaddition, the gene estimations suggestahighinbreedingandlowdifferentiation leading varied from-0.154 (locus cavy4)to 0.739 (locus cavy16). All these varied from-0.150 (locus cavy4)to 0.742 (locus cavy5);and the FIS FIT varied from0.004 (locus cavy5) to 0.043 (locus cavy6); the FST in thepopulation. Moreover,per locuswereestimated:the theFindices and the Eastregionrespectively, suggestingadeficitinheterozygotes heterozygosity (0.317 ± 0.047 and 0.295 ± 0.052) in both the Center (0.504 ± 0.061 was higher and 0.442 ± 0.070) than theobserved and Centre populations, respectively. The expectedheterozygosity i.e.28.5%and33.3%fortheEast wassignificant, inbreeding level mixing through interchanging breedingindividuals.Theestimated ongoing genetic separation orstill recent and suggestedarelatively The geneticdistancebetween the two populations was small (0.041) between individualswithinthetotal due to population. differences within populations, and 62%was wereduetodifferences the diversity 35% of among populations,while due todifferences were diversity Cavy 4marker. AMOVAgenetic that only3%oftheobserved revealed deviation fromHardy-Weinberg’s equilibriumwasobserved,exceptfor relationship betweendifferentcavypopulations A at molecular level. of thegenetic high PICestimationcanfurtherbeusedfortheassessment between 0.05 and 0.86 with ameanof0.45 such that; markers witha 16 with a mean of 0.37. The estimated PIC value for each locusranged (FIS) variedfrom-0.14at locus cavy4to0.77 coefficient at locus cavy 6), with a mean of6.69 per locus.Theestimatedinbreeding alleles 87 wereobservedat alleles 13 loci; varyingfrom4 (cavy 3) to 13 (cavy while therestwashighlypolymorphic.Atotal of were non-informative markers, 3 Among the16 (Cavy 1, selected microsatellites 13 and 14) East. or the the Centre origin -either account twogroupsaccordingtotheir farms inthebimodal agroecological zone,takinginto from accessible of 110unrelatedanimals DNA wasextractedfrombloodsamples in Cameroon,a panel of 16 microsatellite markerswas used. Genomic Topopulations (Cavia porcellus) studythevariabilityofdomesticcavies 1 and EvrenKobanBaştanlar 1 POSTER PRESENTATIONS 79 [email protected] Numbela ER, Valencia CR, 2003. Guinea pig Guinea 2003. CR, Valencia ER, Numbela Genetic Diversity, Guinea Pigs, Microsatellites, Microsatellites, Guinea Pigs, Cameroon Diversity, Genetic UT, USA.: 54p. Salganik MJ, Heckathorn DD, 2004. Sampling and 2004. DD, Heckathorn USA.: MJ, Salganik 54p. UT, sampling. using respondent driven populations in hidden estimation 193-239. methodology 34(1): Sociological Author’sCorresponding Address: Keywords: Keywords: References: Institute. Provo, manual. Benson management and Food Agriculture POSTER PRESENTATIONS 80 1. DepartmentofBioengineering,MarmaraUniversity,Istanbul,Turkey Medi Kori, TARGETS INCERVICAL CANCER POTENTIAL BIOMARKERS ANDTHERAPEUTIC META-ANALYSIS OF TRANSCRIPTOMIC DATA REVEALS KazimYalcinArga [3] AgarwalSM, RaghavD, SinghH, RaghavaGP. CCDB:a curated RadiotherOncol. 2013;108(3):397-402. J,[2] Haedicke Iftner T.and cancer. Human papillomaviruses 12–21. diseases: implicationsforprevention strategies.Prev Med. 2011; 53(1): M, FrancoEpidemiology and burden of HPV infectionand related EL. References: Keywords: for furtherexperimentalandclinicaltrialscervicalcancer. as diagnostic/prognosticbiomarkersor potential therapeutic targets systematic studythat reports candidatebiomolecules can beconsidered candidates andpotential therapeutic targets. Consequently, this and metabolites(particularly, arachidonicacids)asnovelbiomarker proteins (e.g., KAT2B, PARP1, CDK1,GSK3B, WNK1,andCRYAB), 5p), transcription factors(particularlyE2F4, ETS1, and CUTL1), other and NR2C2), miRNAs (e.g., miR-192-5p, miR-193b-3p, and miR-215- receptors EDNRAandEDNRB;nuclearNCOA3, NR2C1, receptors (e.g. ephrinreceptorsEPHA4,EPHA5,andEPHB2;endothelin as wellvarious cancer suppressors andoncogenesincervical datasets. This approach revealed already-known biomarkers,tumor usingindependentgeneexpression was identifiedviacross-validation performed and the prognostic power of selectedreporterbiomolecules factor,Moreover, etc.),and metabolite levels. analyses were survival miRNA),protein(receptor,were identifiedat RNA (mRNA, transcription transcriptional regulatory network. As a result of that, biomolecules and post-human metabolicmodelandtranscriptional genome-scale network, i.e. protein-proteininteraction human biomolecularnetworks statistical analyses.DEGswerefurtherintegrated with genome-scale and the core differentiallyexpressedgenes(DEGs)was obtained by was performed by taking into consideration fiveindependentstudies cancer associatedtranscriptomicdatasets of thecervical meta-analysis diagnosis and treatment for thedisease.Accordingly, inthisstudy, a are needed to elucidation of potential biomarkers for the screening, approaches methods, systems-level screening of present inaccuracy and the cancer etiology ofcervical the unclear context, considering this deaths anddescribedasafourthleadingcauseofdeathat2018 [4]. In cancer causes570.000 approximately cervical new casesand311.000 tests, to develop [2,3].Also,despitethepresentlyavailablescreening disease related riskfactorsarerequiredforthis other cancer therefore, ; for cervicalcancerdevelopment,italoneisnotsufficient essential oncogenic”HumanPapillomavirusinfection by “highly (HPV)is death amongwomenworldwide[1].Although of cancer leading causes and oneofthe cancer most common the second is cancer Cervical Cervical Cancer; Meta-Analysis;Biomarker CervicalCancer; [1]Tota Devries M, RichardsonLA, Chevarie-Davis JE, POSTER PRESENTATIONS 81 Medi Kori Department of Medi Kori Global cancer statistics 2018: GLOBOCAN estimates of incidence incidence estimates of and GLOBOCAN 2018: cancer statistics Global for 36 cancersmortality worldwide in 185 countries. CA Cancer J Clin. 68(6):394-424. 2018 Nov; Author’sCorresponding Address: Building University, Marmara Engineering, of Faculty Bioengineering, E-mail: [email protected] Istanbul, Turkey. Kadıkoy, D, database of genes involved in cervix cancer. Nucl Acids Res. 2011; 39: Res. 2011; Nucl Acids in cervix involved of genes cancer. database 975-9. LA,A. Jemal Siegel RL,I, Soerjomataram Torre J, Ferlay [4] Bray F, POSTER PRESENTATIONS 82 2.Ege UniversityFacultyofScienceBiochemistryDepartment 1.Ege UniversityInstituteofHealthSciencesBioinformaticsProgram Seda Yüzeren INVOLVED INRESPIRATORY DISEASES STRUCTURES OF PROTEINS ANDNUCLEIC ACIDS COMPUTATIONAL ANALYSIS OF SEQUENCES AND Biochemistry [email protected] Address: Corresponding Author’s Lyase; S-Ribosylhomocysteine Structures ofProteins. ThreeDimensional Keywords: programinthis study.the 2210-C under scholarships TÜBİTAK-BİDEBproviding thank for to like would I solutions topreventbiofilmformation. It is thought that these resultscancontribute to the production of influenzae-K. pneumoniae-N.meningitidis pneumoniae, B.anthracis-S.pyogenes-pneumoniae andH. K.pneumoniae,S.pyogenes- N. meningitidis- S. N. meningitidis, triple comparisonsof Common sequencesand structures was observed indouble and As a result, commonsequencesand structures wereobtained. Square Deviation(RMSD)byusingUCSFChimera. calculated byRootMean of proteinswere the similarities comparisons, software UCSF Chimeraand common structureswerefound. In binary Model/ Expasydatabase,modelswerecomparedusingthefree these structuresofproteinswerecreatedintheSwiss- Three dimensional sequences byusingMEGA7.0.26freesoftware. acid andamino was performedtofindcommonnucleic Alignment from theNCBIandUniprotdatabases. obtained were microorganisms meningitidis pyogenes andNeisseria Staphylococcus aureus,Streptococcuspneumoniae, pneumoniae, Klebsiella influenzae, anthracis, Haemophilus The nucleicacid and amino acidsequencesof proteins of Bacillus comparison. this protein were investigatedby molecular modelingand sequence acids encoding that andnucleic play aroleinrespiratorydiseases, microorganisms in biofilmformation of7selected important mechanism from theluxSgene inQuorumSensing,synthesized lyase enzymes an cause many infectious diseases.In this study, S-ribosylhomocysteine bacteriathat of exopolysaccharide-producing is anecosystem Biofilm 1 , CenkSelçuki Biofilm; Quorum Respiratory Sensing; Diseases; 2 H. influenzae-K.pneumoniae, Ege University, Faculty of Science, comparisons. POSTER PRESENTATIONS 83 1 [1] Sekhon A, Singh R, Qi Y. DeepDiff: DEEP-learning for DeepDiff: DEEP-learning [1] Sekhon A, Singh R, Qi Y. Epigenomics, Transcriptomics, Ngs Data Analysis, DeepAnalysis, Data Ngs Transcriptomics, Epigenomics, , Pınar PİR 1 References: gene expression from predicting DIFFerential histone modifications. Bioinformatics. 2018 Sep 1;34(17):i891-900. Epigenetic AS. Beedle FM, Low MA,Hanson Buklijas T, [2] Gluckman PD, mechanisms that underpin metabolic and cardiovascular diseases. Nature Reviews Endocrinology. 2009 Jul;5(7):401. repeating time will be optimized. Number of hidden nodes and layers, nodes and of hidden repeating time will be optimized. Number determined for of different activation functions will be and addition be validates using test from recurrent neural networks. The model will various cell types. Keywords: Neural Networks The pipeline includes of low-qualitytrimming on the reads, mapping reference gene expression levels identification of genome, finally and heights gene expression levels peak and heights. The obtained peak neural networks. Convolutional neural deep training the for used will be with several networks and recurrent neural networks hyperparameters networks, the parameters will be created. In convolutional neural and size, window size, such as filter size, number of filters, pooling gene expression levels in the target cell. Several computational cell. Several computational gene expression levels target the in predict gene expression models have been released to from histone machine, modification profiles using support vector random forest, the aforementioned studies and deep learning [6]. However, methods conditions focus on differential gene expression levels need two that expression for comparison instead of absolute gene values. Here we perform analysis of next generation sequenced on RNA-seqdata results of undifferentiated cells analysis iPSCs. and/or and ChIP-seq epigenomics, level transcriptomics and also proteomics of stem cells [5]. The impact on current treatment methods may also have huge effects of epigenetic modifications (epigenomic level) such as histone evaluating the gene investigated by can be marks on gene regulation expression levels (transcriptomic level). The computational methods such as machine learning techniques may lead construction of mathematical capabale of predicting the models based on epigenetic factors and that aberrant epigenetic that aberrant epigenetic several marks are related to diseases such diseases and diseases, cardiovascular autoimmune as cancer, schizophrenia [2-4]. Several epigenetic studies showed that marks targetting hence drugs mutations, DNA are reversible to in contrast on treatments of the cells epigenetic profile of potential may have big use of stem cells diseases [1]. In addition, treatment of diseases, for mechanisms cellular to respect of which requires investigation with Connecting epigenetic modifications to cellular can be modifications to functions Connecting epigenetic performed by utilizing the gene expression levels [1]. It is well known Gülben AVŞAR Çayırova/Kocaeli of Bioengineering, University, Department 1. Gebze Technical PREDICTION OF TRANSCRIPT PROFILE OF CELLS VIA VIA CELLS OF PROFILE TRANSCRIPT OF PREDICTION METHODS COMPUTATIONAL POSTER PRESENTATIONS 84 ([email protected]) Department ofBioengineering, Gebze/KOCAELİ/TURKEY Address: Corresponding Author’s histone modifications.Bioinformatics.2016Aug29;32(17):i639-48. predicting geneexpressionfrom [6] SinghR,Lanchantin J, RobinsG, QiY. for DeepChrome:deep-learning reviews Molecularcellbiology. 2016Mar;17(3):170. and drugdiscovery. Nature [5] AviorY, SagiI,BenvenistyN. Pluripotentstemcellsindiseasemodelling 2011 May;7(5):263. Nature ReviewsRheumatology. Epigenetic alterations in autoimmune rheumatic diseases. [4] Ballestar E. 72. syndromes, andtherapies.TheLancet Neurology. 2009Nov1;8(11):1056- neurological diseases:genes, [3] UrdinguioRG, Sanchez-MutJV, EstellerM.Epigenetic mechanismsin Gebze Technical University, POSTER PRESENTATIONS 85 - is cell enet. - very high high very scRNAseq cell level level cell -

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] 4 Corresponding author’s address: Pınar Pir Turkey Çayırova/Kocaeli, Bioengineering,41400, of Department University, Technical Gebze [email protected] Figure 1: References: [1] [2] [3] [ transcriptomes. Using single transcriptome cell time a of branching the show plots inference 1, trajectory in Figure As shown data resolution cells neuron into cells stem Keywords: transcriptomics Induced pluripotent stem cells (iPSCs) resemble embryonic stem cells and hassomatic the potency cells to candifferentiate be reprogrammed into all into types iPSCs of m by embryonic stem cells (ESCs) iPSC can be used in medical research areasapplications such as disease mode targets. measured critical to better understand the heterogeneity, cellular With time insights biological gain to Figure 1. progress of these transformation processes. Keywords: Inference; Single-Cell Single-Cell Rnaseq; Differentiation; Trajectory Scrnaseq Transcriptomics; in Figure 1, trajectory inference of a time-plots show the branching in Figure dependent process scRNAseqby Analysis data. of scRNAseq data changes the understanding of diseases or biological processes, it and very high resolution reveals intracellulartissue at within a heterogeneity [3,4]. The aim of this is study the analysis of single cell omics data of fibroblasts and stem to investigate and model the transformation generate new theories on the cells expected to into neuron cells, and more accurately than ever before. One useful biological way to gain insights from single-cell RNA-seqsort the computationally is to data their transcriptomes. Using transition of the gradual cells according to single-cell RNA-seq of sequence ordered an construct one may data, cells to describe the gradual transition of the single-cell transcriptome inferenceusing trajectory (TI) by done Ordering cells can be [2]. shown ordering methods).As methods (also known as pseudotemporal discovery of new drug targets. To understand the reprogramming the reprogramming understand To targets. discovery of new drug processes is crucial. of iPSCs in detail, analysis of high-resolution data Gene expression instead the cell level at profiles are measured bulk samples with single-cellof transcriptomics (SCT). Measuring gene expression in single-cellunderstand better to level is critical cellular composition in behaviour and molecular the heterogeneity, adult and pathological tissues. With time-coursedeveloping, single- cell dynamics of biological systems RNAseq data, can be predicted and has the potency to differentiate into all types of mature cells. into all types of mature cells. differentiate to and has the potency Mature somatic cells into iPSCs by inducing can be reprogrammed essential for maintaining the high expression of the genes important stem properties of embryonic cells (ESCs) iPSC can be used in toxicity testing or medical researchmodelling, areas such as disease [1]. Their transplantation applications to transform into any ability therapies and new treatment development of to can lead cell type Induced pluripotent stem cells Induced pluripotent (iPSCs) resemble embryonic cells stem Batuhan Çakır University, Kocaeli, Gebze Technical Faculty of Engineering, of Bioengineering, 1. Department Turkey SINGLE-CELL TRANSCRIPTOME ANALYSIS OF OF ANALYSIS TRANSCRIPTOME SINGLE-CELL PROCESSES DIFFERENTIATION CELL NEURONAL POSTER PRESENTATIONS 86 nvriy Dprmn o Boniern,10, Çayırova/Kocaeli, Bioengineering,41400,Turkey [email protected] of Department University, Address: Corresponding Author’s content/biorxiv/early/2018/03/05/276907.full.pdf and robust tools. 2018; Available from:https://www.biorxiv.org/ trajectoryinferencemethods:towardsmoreaccurate single-cell [4] Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of 2016;7(SEP). bioinformatics andcomputationalchallenges.Front Genet. [3] Poirion OB, ChingT, ZhuX, transcriptomics Single-cell L. Garmire 2016;44(13):e117. analysis.NucleicAcidsRes. RNA-seq single-cell JiH. TSCAN:[2] JiZ, reconstructionand evaluation in Pseudo-time 2006 [Internet].NationalInstitutesofHealth.2006. References: [1] National Institute of Health. RegenerativeMedicine ıa Pr ez Technical Gebze Pir Pınar POSTER PRESENTATIONS 87 uzla/İstanbul Gebze Technical University, University, Gebze Technical 2 , Devrim Gözüaçık , Devrim 1 Single-Cell Cell-Cell Rna Sequencing, Communication, [1] A. Masoudi-Nejad, G. Bidkhori, S. Hosseini Ashtiani, A. [1] A. Bidkhori, S. Masoudi-Nejad, G. , Pınar Pir , Pınar Pir 1

vol. 50, no. 8, p. 96. Pubmed PMID: 30089861 of Single-Cell RNA-Seq Identifies Cell-Cell et al., “Analysis Kumar [3] M. P. 2018, Characteristics.,” Cell Rep., Communication Associated with Tumor vol. 25, no. 6, p. 1458–1468.e4. Pubmed PMID: 30404002 Nov. Corresponding Author’s Address: Room 210. Email: [email protected] Department of Bioengineering, Receptor-Ligand Interactions Receptor-Ligand References: “Cancer systems biology and E. and Wang, Bozorgmehr, H. Najafi, J. CancerSemin. approaches,” multiscale and scale Microscopic modeling: vol. 30, pp. 60–69. Pubmed PMID: 24657638 Biol., 2015, Feb. “Single-cell Bang, sequencingRNA D. and Lee, H. J. Hwang, B. [2] technologies and bioinformatics pipelines,” Exp. Mol. Med., 2018, Aug. taken from GEO and analyzed with bioinformatics software packages bioinformatics software with analyzed from GEO and taken interactions receptor-ligand After identifying developed in MATLAB. are found active signaling pathways cell types, between detected receptor-ligand from related predicted characteristics are tumor and interactions. Keywords: uncover regulatory relationships between cells. [2] Cells communicate relationships between cells. [2] Cells communicate uncover regulatory interactions in tumor microenvironment each other by receptor-ligand that include extracellular matrix components and other cells. Identifying interactions between cell right treatment an types could lead to individual patient. [3] cell-cell communication in cancer tissues is identified and In this study, is data Transcriptome signaling pathways. results are associated with metastasis can be supported via proteins that are present in their with their microenvironment their survival and their growth, and proteins that are present in their via supported metastasis can be microenvironment. [1] a their environment, heterogeneous because of also are Tumors such one cell type includes more than taken from patient sample that cells. as B cells, T cells, Endothelial cells, Fibroblast Macrophages, Single-cell can reveal complex cell and populations RNA sequencing, mutations cause abnormality in cell mutations cause abnormality migration proliferation, growth, secondary tumors eventually and formation tumor leads to that burden cells are composed of with mutation (metastasis) which origin. There are several different from their cancer types and all of them have common progression profiles such as single somatic between cells complex interaction extracellular matrix mutations, and in (ECM) components the host tissue. Cancerous cells communicate Cancer is mainly caused by mutations in single somatic cells. in mutations The by Cancer is mainly caused Cemal Yıldız Çayırova/Kocaeli of Bioengineering, University, Department 1. Gebze Technical T of Genetics and Bioengineering, University, Department 2. Sabancı IDENTIFICATION CELL-CELL COMMUNICATION BY COMMUNICATION CELL-CELL IDENTIFICATION IN CANCER INTERACTIONS RECEPTOR-LIGAND POSTER PRESENTATIONS 88 2. GebzeTechnical University,DepartmentofBioengineering 1. GebzeTechnical University,DepartmentofMolecularBiologyandGenetics Simge Sengul KIST612 MODEL INTEGRATION OF TRANSCRIPTOME DATA ANDA OF EUBACTERIUM LIMOSUM (ATCC 8486)BY RECONSTRUCTION MODEL OF AGENOME-SCALE Bioengineering 41070 Gebze/Kocaeli Turkey [email protected] Address: Corresponding Author’s biotechnology, 2017,35.1:81. reconstructions for773members ofthehumangutmicrobiota.Nature [5] Magnúsdóttir, metabolic Stefanía,etal. Generationofgenome-scale 295.5560: 1662-1664. [4] Kitano,Hiroaki.Systemsbiology: abriefoverview. science,2002, Proteins andProteomics, 2008,1784.12:1873-1898. Ljungdahl pathwayofCO2 fixation. Biochimica etBiophysicaActa(BBA)- [3] Ragsdale,StephenW.; Elizabeth.AcetogenesisandtheWood– PIERCE, bacteriology, 2011,193.1:307-308. acetogen,EubacteriumlimosumKIST612.Journalof monoxide-utilizing [2] Roh,Hanseong, etal.Completegenomesequenceofacarbon journal ofgastroenterology:WJG, 2006,12.7:1071. colonic inflammatoryactionwithincreaseofmucosalintegrity. World ameliorates experimentalcolitisand metabolite ofmicrobe attenuates References: Acetogen; GutMicrobiata;FluxBalanceAnalysis. Keywords: activity. syngas fixationandaceticacidproduction in addition to itsprobiotic reactions and missing metabolitesinthemodelto simulate potential allow ustofind et.al [5]will of KIST612 by Thiele metabolic model 8486 transcriptome data (GSE97613) from GEODatabase and adraft ATCCfor E.limosum 8486Integration of ATCCmodel reconstruction. and KEGG pathwaysby BioModels Database is usedasatemplate limosum KIST612 generated automatically from genomedata of E. to simulations.Inthisstudy,Metabolic Model(GSMM) Scale Genome data and manualcurationprior to betunedbyusingexperimental models areconstructedbased on genomicinformation,thentheynee in certaincultureconditions[4].Draft processes some sub-metabolic metabolic modelstosimulatewholemetabolismor genome-scale Biology approachisusedingenerating [3]. Systems process conversion Wood-Ljungdahlmetabolical stepsofthis the main is pathway which are takentothemetabolismthrough [2]. Thesecarbonsources and Hydrogen carbon sources biomass aceticacidbyutilizingin-air also have industrialimportance as they can convertsyngasto produce probiotic activity suchasreducinginflammationincolitis[1].Acetogens theraputic was showntohave limosum gut microbiata.E. found in Eubacterium limosum isan acetogenic bacteria which isa species 1 , EmreTaylan Duman [1] Kanauchi, Osamu, et al. Eubacterium limosum [1]Kanauchi, Osamu,etal.Eubacteriumlimosum Genome Scale Metabolic Model; Eubacterium Limosum; Genome ScaleMetabolicModel;EubacteriumLimosum; 2 , PınarPir 2 GebzeTechnical University POSTER PRESENTATIONS 89 1 , 1 , Pınar Pir 1 , Gülben Avşar , Gülben 3 Induced Pluripotent Stem Cell,Rod Photoreceptor , Hamza Umut Karakurt , Hamza 1 [1] Attwood, S., & Edel, M. (2019). iPS-Cell Technology & Edel, M. (2019). iPS-Cell[1] Attwood, S., Technology , Şükrü Öztürk 2 Cell,Transcription Factor,Signalling Pathway, RNAseq Pathway, Factor,Signalling Cell,Transcription References: Clinicalfor Safe Be Ever It Instability—Can Genetic of Problem the and Use? Journal of Clinical Medicine, 8(3), 288. https://doi.org/10.3390/ jcm8030288 M. M., dos, Dourado, P. F. [2] Gomes, K. Costa, I. C., Santos, J. M. S., (2017). Induced pluripotent stem cells C. B. J. & Ferreira, M. F., Forni, TFs and their expression trends which affect the regulatory networks their expression trends which affect the regulatory and TFs aim to propose We data. during differentiation process using RNAseq new strategies for efficient cell differentiation and reprogramming processes, dysregulation of these processes to shed light on in addition in health and disease states. Keywords: as RNAseq and ChipSeq. In this study two transcriptome (RNAseq) transcriptome (RNAseq) two as RNAseq and ChipSeq. In this study (Chen et GSE86790 was used, dataset datasets from GEO database al.) [5] was used for profiling the differentiation Mus musculus retinal organoids from iPSCs (Aldiri GSE87064 and dataset et al.) [6] was Mus musculus the reprogramming of iPSCs from used for profiling to is comparison of the of this study retinal organoids. Main target differentiation and reprogramming processes of cells and determine of reprogramming mechanisms and their signaling pathways [4]. of reprogramming mechanismstheir signaling pathways and affect these transcription factors (TFs) repression of Expression or factors affecting networks. The identification of transcription regulatory is an important step in revealing signaling pathways cell differentiation mechanisms, and further insights cell about differentiation mechanisms the time-dependentstudying by obtained can be these of activities marks and Time dependent change in the epigenetic pathways. expression TFs can be profiled using omics of the technologies such medicine, medicine especially personalized regenerative and [1]. iPSCs cells originate from somatic into pluripotency which are reprogrammed such via directed methods as expression of specific transcription factors [2]. iPSC-associated technologies provide tools to understand disease develop new therapeutic contents and mechanisms, drug discover interventions Reprogramming of iPSC from somatic cells [3]. and activation differentiation of somatic cells from iPSC takes place by Use of induced pluripotent stem cells (iPSCs) holds great promise in stem cells (iPSCs) holds great pluripotent induced Use of , Batuhan Çakır , Batuhan 1 Kocaeli, Turkey Computing and Engineering, Indiana University, 2. School of Informatics, Bloomington, IN, USA Hacettepe University, Basic Sciences, Faculty of Pharmacy, 3. Department of Pharmaceutical Ankara, Turkey RNA-SEQ DATA RNA-SEQ Enes Ak Furkan Kurtoğlu University, Faculty of Engineering, Gebze Technical 1. Department of Bioengineering, IDENTIFICATION OF TF-MEDIATED DYNAMICS TF-MEDIATED OF IDENTIFICATION CELL STEM IN PATHWAYS SIGNALLING OF USING REPROGRAMMING DIFFERENTIATION AND POSTER PRESENTATIONS 90 University, Department of Bioengineering,41400, Çayırova/Kocaeli, Çayırova/Kocaeli, Bioengineering,41400, Turkey [email protected] E-Mail: of Department University, Address: Corresponding Author’s 568.e10. https://doi.org/10.1016/j.neuron.2017.04.022 Development, Reprogramming, andTumorigenesis. Neuron,94(3),550- (2017).TheDynamicEpigeneticLandscape oftheRetinaDuring M. A. [6] Aldiri,I.,Xu,B., Wang, Hiler, Chen,X., L., D., ...Dyer, Griffiths,L., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017542/ differentiation. MolecularVision,22(May),1077–1094.Retrievedfrom in vivodevelopmentwithenhancedstratificationandrodphotoreceptor dimensional retinalorganoidsfrommousepluripotentstemcellsmimic [5] Chen, H. Y., Kaya,D., K. (2016). Three- Dong,& Swaroop, A. L., cells viatheWnt1/ miR-140-5p regulatestheodontoblasticdifferentiationofdentalpulpstem Xing, J., Chen,X., [4] Lu, X., Lian,M.,Huang, D., Lu, Y., ...Feng, (2019). X. org/10.1038/nrn.2016.46 neuroscience. NatureReviewsNeuroscience,17(7),424–437.https://doi. cell reprogramming, differentiationandconversiontechnologiesin [3] Mertens, J., Marchetto, M. C., Bardy, C.,& Gage, F. H. (2016). Evaluating 180–189. 63(2), Brasileira, https://doi.org/10.1590/1806-9282.63.02.180 Médica Associação Da Revista medicine. reprogramming: Epigeneticsandapplicationsintheregenerative β -catenin signalingpathway, -catenin 4–11. ıa PR ez Technical Gebze PİR Pınar t t 9 –

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sentially metabolites produced by the metabolism, metabolic modelling approaches in mood disorders disorders mood in approaches modelling metabolic metabolism, the by produced metabolites sentially , Pınar Pir Foreseen Metabolic Alterations in Bipolar Disorder in Metabolic Foreseen Bipolar Disorder; Transcriptomics; Genome-Scale Bipolar Disorder; Transcriptomics; 1 ated mood which is also called as mania or hypomania [1]. Frequent change of moods causes disturbances 317 (2012). 317 – 346 (2003). 346

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. Foreseen Metabolic Metabolic Foreseen . Genome-Scale Metabolic Model Approach Figure 1. fact that brain functions via neurotransmitters, which are essentially brain that fact metabolic modelling metabolites produced by the metabolism, approaches in mood disorders such as bipolar disorder are promising. Keywords: Metabolic Models [6,7] and certain metabolites such brain-related as dopamin [8] and disorder. glutamate [9] are associated with bipolar disorder patients and from bipolar transcriptome data In this study, healthy samples combined with brain specific genome-scale metabolic reporter pathways and highly model to identify the reporter metabolites, altered biochemical in the dorsolateral prefrontal cortex. Due to fluxes presence symptoms can be of mania or hypomania. Some psychotic disorders and overlap between other schizophrenia. The mistaken for health record of the patient may lead to previous examination without missed diagnosis of the condition. Previous The exact mechanism of bipolar disorder is still unclear. studies suggested that specific polymorphisms [3], alterations in Wnt and Notch signalling glucose and phosphorus metabolisms [4,5], disabling and recurrent major psychiatric condition. Bipolar disorder Bipolar psychiatric condition. recurrent major and disabling also which is mood elevated and depression of causes mental periods change of moods causes [1]. Frequent called as mania or hypomania suicide is one of the frequent lives. disturbances on daily to Attemp patients results.60% of the results in bipolar disorder to At least 25% attemp suicide at least once in their lifetime. to 19% or patients 4% is the history or disorder bipolar diagnose to Key complete suicide [2]. Bipolar Disorder, formerly known as manic depression, is a common, as manic depression, is a common, formerly known Bipolar Disorder, 168,

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In this study, transcriptome data from bipolar disorder patients and healthy samples com samples and healthy patients disorder bipolar from data transcriptome study, this In The exact mechanism of bipolar disorder is still unclear. Previous studies suggested that specific polymorphisms [3], alterat [3], polymorphisms specific that suggested studies Previous unclear. still is disorder bipolar of mechanism exact The Bipolar Disorder, formerly known as manic depression, is a common, disabling and recurrent major psychiatric condition. Bipol condition. psychiatric major recurrent and disabling a common, is depression, manic as known formerly Disorder, Bipolar

Dorsolateral Prefrontal Cortex: A Genome A Cortex: Prefrontal Dorsolateral

Gebze Technical University, Department of Bioengineering, Kocaeli/Turkey Bioengineering, of Department University, Technical Gebze [email protected] Corresponding author [9] Michael, N. (Berl). Psychopharmacology magnetic resonance spectroscopy. spectroscopy. resonance magnetic [8] Salvadore, G. euthymic states. [7] Kato,Takahashi, T., S., Shioiri, &T. Inubushi,Alterations T. in brain phosphorous metabolism in bipolar disorder detec [6] Hosokawa, T., Momose, T. & Kasai, K. Brain glucose metabolism difference between bipolar and unipolar mood disorders in d in disorders mood unipolar and Psychiatrybipolar between difference metabolism glucose Brain K. Kasai, & T. Momose, T., Hosokawa, [6] [4] Pedroso, I. Meta A. E. genes. pathway Youngstrom, & R. L. A. Moreira, R., A. Meter, Van [5] [3] Gao, J. (2016). [2] Novick, D. M., Swartz,A. H. & Frank, E. Suicide attempts in bipolar I a (2015). [1] Anderson, I. M., Haddad, P. M. & Scott, J. Bipolar disorder. disorder. References: Bipolar J. Scott, & M. P. Haddad, M., I. Anderson, [1]

Keywords: model to identify the reporter metabolites, reporter pathways and highly altered biochemical fluxes in the dorsolateral prefr dorsolateral the in fluxes biochemical altered highly and pathways reporter metabolites, reporter the identify to model es are which neurotransmitters, via functions brain such as bipolar disorder are promising. associated with bipolar disorder. Notch signalli Some psychotic symptoms can be mistaken for schizophrenia. The overlap between other disorders and and exam disorders other between overlap The schizophrenia. for mistaken be can symptoms psychotic Some condition. the of diagnosis missed to lead may patient the on daily lives. Attemp to suicide is one of the frequent results in bipolar disorder results. At least 25% to least 60% the of At pat results. disorder in bipolar results is one of tothe frequent suicide Attemp lives. on daily once in their lifetime. 4% to 19% or patients complete suicide [2]. Key to diagnose bipolar disorder is the history or presen causes mental periods of depression and elev and depression of periods mental causes

POSTER PRESENTATIONS 92 Department ofBioengineering, Kocaeli/Turkey [email protected] Address: Corresponding Author’s Psychopharmacology (Berl).168,344–346(2003). within theleftdorsolateralprefrontalcortex. glutamate/glutamine levels N.[9] Michael, accompanied byelevated etal.Acutemaniais 1501 (2010). bipolar disorder:Areview.Psychiatry JournalofClinical 71, 1488– [8] Salvadore, G. etal.Theneurobiology of theswitchprocessin (1993). and 7Li magneticresonancespectroscopy. J. Affect.Disord.27, 53–59 31P vivo phosphorous metabolisminbipolardisorderdetectedby [7] Kato, T., Takahashi, S., T.Shioiri, & Inubushi, T. Alterations inbrain 243–250 (2009). states. Prog.and euthymic Neuropsychopharmacol.Biol.Psychiatry33, between bipolarandunipolarmooddisordersin depressed difference [6] Hosokawa,T., Momose,T. Brainglucosemetabolism &Kasai, K. Psychiatry 72, 1250–1256(2011). Analysis ofEpidemiologicStudiesPediatric BipolarDisorder. J. Clin. [5] Van Meter, R. & Youngstrom, L. R., Moreira, A. Meta- A. A. E. signaling pathwaygenes.Biol.Psychiatry 72,311–317(2012). changes associatedwithbipolar disorder areover-represented inbrain [4] Pedroso, variants andgene-expression I.etal.Commongenetic 145–152 (2016). meta-analysis. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 171, [3] Gao,J.and bipolardisorder:A etal.TPH2genepolymorphisms 12, 1–9(2015). of theevidence. andmeta-analysis I andbipolarII disorder: areview D.[2] Novick, attempts inbipolar Suicide &Frank, M.,Swartz,H. E. A. disorder. Bmj345,e8508–e8508(2012). References: [1] Anderson, I. M.,Haddad, [1]Anderson,P. M.&Scott,J. Bipolar Gebze TechnicalUniversity, POSTER PRESENTATIONS 93 [email protected] 2 2, Serap Erkek 2, Serap 1, [1] Flavahan WA, Gaskell E, Bernstein BE. Epigenetic 1, Gülden Özden Yilmaz2, Aleyna Eray1, Özden Yilmaz2, 1, Gülden Epigenetics, BLCA, ChIP-Seq, Invasion Epigenetics, BLCA, ChIP-Seq, Corresponding Author’s Address: provide important insights about transcriptional regulatory pathways transcriptional regulatory provide important insights about cancer. implicated in invasive characteristics of bladder Keywords: References: Science. Jul 21;357(6348). 2017 plasticity and the hallmarks of cancer. doi: 10.1126/science.aal2380. of severalCDH1 and VIM, which might be involved genes such as in experiments for invasive After completing the ChIP-seq characteristics. analysis, we will other three cell our computational lines and extending roles in respective regulatory identify which transcription factors have these with which chromatin modifiers are associated regions and Selective the assay “ChIP Isolation performing transcription factors by Our findings will (ChIP-SICAP)”. of Chromatin Associated Proteins sequencing (ChIP-seq) to define active regions regulatory in 3 non- sequencing (ChIP-seq) muscle invasive bladder cancer cell lines and 3 (RT4, RT112 and 5637) Until HT1376). and T24 cancer cell lines (J82, muscle invasive bladder experiments two MIBC cell of lines completed ChIP-seq we have now, (J82 and T24) and one NMIBC cell line (RT4) and we called H3K27ac peaks on these cell lines. Our preliminary findings suggest differential around promoter regions H3K27ac occupancy in MIBC and NMIBC major cancer types for which chromatin regulatory mechanisms mechanisms which chromatin regulatory should for major cancer types we investigate the transcriptional regulatory In this study, be studied. regulated between muscle invasive networks differentially bladder non-muscle cell cancer (NMIBC) cancer (MIBC) and invasive bladder lines the active chromatin regions and transcription .via identifying factors and their interacting partners specific to the respective cell chromatin immunoprecipitation followed by use H3K27ac lines. We It is known that epigenetic regulations lead to changes in gene expression regulations lead to changes in gene It is known that epigenetic cell programs thus affecting in chromatin fate. Mutations modifier genes in chromatin modifier rate states[1]. Mutation can readjust chromatin to high (~20-25%) compared cancer is especially genes in bladder many other cancer cancer types. Therefore, bladder is one of the 2.Izmir Biomedicine and Genome Center, Izmir, Turkey 2.Izmir Biomedicine and CELL LINES Güneri Perihan Yağmur Şerif Şentürk Neşe Atabey2, Genome Institute, Izmir, Turkey 1.Izmir Biomedicine and IDENTIFICATION OF TRANSCRIPTIONAL TRANSCRIPTIONAL OF IDENTIFICATION CHARACTERIZING NETWORKS REGULATORY CANCER BLADDER AND NON-INVASIVE INVASIVE POSTER PRESENTATIONS 94 Balcova, Izmir,Turkey 2. IzmirInternationalBiomedicineandGenomeInstitute,DokuzEylülUniversity,35340 1. IzmirBiomedicineandGenomeCenter,35340Balcova,Izmir,Turkey, Işil Takan FUNCTIONAL IMPLICATIONS PHYLOGENETIC ANALYSIS OF KERATINS: 1,2 , HaniAlotaibi [4] Hesse M, Zimek A, Weber M,ZimekA, [4] Hesse analysis MaginTM. Comprehensive K, 20890307. markers. Oncogene.2011 cell Jan 13;30(2):127-38. PubMed PMID: [3] Karantza V. Keratins inhealthand cancer: morethanmereepithelial PMID: 18461349. pathology.Biol. 2008 Cell Jun;129(6):705-33. Histochem PubMed Thehuman keratins:biology [2] MollR,DivoM, and Langbein L. PMID: 11792552. filaments. CurrOpinCellBiol. 2002 Feb;14(1):110-22. PubMed the structure,function andregulationofkeratinintermediate defining References: Keywords: is preservedinthespeciesunderinvestigation. was found that their position, transcriptionalorientation,and number the chromosomal arrangementof KRT genes was examined, and it length keratinhomologousproteinswerereconstructed.Furthermore, based ontheentire trees homologs. Phylogenetic for keratin extensively those thathave beencompletedrecently,including weresearched Towardmultiple genomes. end, thepubliclyavailablegenomes, this of KRThomologousproteinsin analyses phylogenetic comprehensive evolutionary historyofthetype I KRTgenefamily, wehaveperformed the In ordertoreconstruct duplication events. and timingofgene the direction them idealforevolutionarystudiesaimingtoinfer makes of KRTscharacteristic exceptional 12 and17[4].This chromosomes pseudogenes whicharearrangedintwo uninterrupted clusterson and nonfunctionalKRT both functionalKRTgenes genome contains keratins incancerbiology among tumor-bearingThe human species. enhance our understanding regarding the functionalimplicationsof phylogeneticanalysisof a keratinswhichwould comprehensive as diagnostic or prognostic markers [3]. However, there isa lack of and members oftheKRTfamilyserve metastasis, and invasion several pathophysiology,aspects ofcancer in several cell cancer including of these propertiessoft keratins areknownto have a prominent role response to wound healing, cell growth and cell death [1, 2]. Because membrane traffickingand signaling pathways that regulate cell polarity,plasma membrane motility,cell size, cell proteinsynthesis, stressors, andalsoplayaregulatory role inapical–basal mechanical and non- highly involvedinepithelialcellprotectionfrommechanical birds and hair and nails inmammals.Soft bepithelial keratinsare in and feathers claws such asscales, up morphologicalstructures make properties. Hardkeratins physicochemical of their on thebasis keratins, and“hard” They arealsogrouped as “soft” cells. epithelial Keratins (KRTs) constitutetheintermediatefilament-forming proteinsof Keratins; Phylogeny;GeneDuplication [1]CoulombePA, OmaryMB. 'Hard'and'soft'principles 1,2 , AthanasiaPavlopoulou 1,2 POSTER PRESENTATIONS 95 athanasia.pavlopoulou@ibg. edu.tr of keratin gene clusters gene of keratin Eur J Cell rodents. humans and in Biol. 2004 15085952. PubMed PMID: Feb;83(1):19-26. Author’s Corresponding Address: POSTER PRESENTATIONS 96 3.Microsoft Research,14820NE36thStreet,Building99,Redmond,WA,98052,USA 2. SchoolofLifeSciences,PharmacyandChemistry,KingstonUniversityLondon,UK, 1. MersinÜniversitesiTıpFakültesi,BiyoistatistikveTıbbiBilişimAnabilimDalı, Erdal COŞGUN IR. OnurÖZTORNACI CHILDHOOD LEUKEMIADISEASE CASE-CONTROL STATUS OF INDIVIDUALS INTHE LEARNING ON SNPDATA TO PREDICT THE THE USEOF LEARNING MACHINE ANDDEEP Deep Learning. Keywords: avoid forbiaseffect andover-fitting [3]. methods. Weto conventional methodto used10-foldcross-validation probably best suitedforGWASthat SVMis as analternative analysis (Table1).view inthefield Our resultsconcordant withtheconsensus and NPV=0.96, sensitivity=0.97, specificity=1,Accuracy=0.98) time (7 Min). SVM appeared to be the most accurate method (PPV=1 it canbesaid that DL hasreportedtheresults inaveryshort Besides, PPV toRF.reach %65accuracyrateandalsoitshownthat quite similar thence; itmaynotappropriate for usingGWAS data. DL wasable to individuals onourGWAS data. The accuracyvalueofMLPisverylow, using RFmay not giving a good solution forchoosingnon-disease classifying. However,means %43 which RF shownalowspecificity as high and sensitivity specificity results. RF has reached%73 accurate shown that SVM isthebestmethod with a high accuracyrate as well identify to SNPs withassociatedto diseases inGWAS. Theresultsare control studiesintheGWAS, Logisticis usedfrequentlyto regression was performed by the data which was passed QCprocedure. In case- of gender-specificin childhood[2].Inthisstudy, differences analysis published GWAS data that study designed to identify geneticmarkers [1]. Weclue aboutdiseases given thecomplex an already re-analysed can be Minoralleles with thediseasewhenusingMLmethodsand DL. methods anditmayallowtokeeptheSNPs thatmaybeassociated on minor allelefrequency;through this method, it can combinetwo approach for handlingthisproblem.Themethoddepends selection We forfeatureselection. Chi-square to proposeafeature wouldlike ii)Using ML methodsandDLaswell.i)UsingclumpingonPLINK, two different methodscanbeusedonthepreparingdata for using for MLalgorithms.Basically,one oftheproblems computation timeis for the best model between ML and DL on SNP data. Nevertheless, situation allowstocomparison as this patterns aswell heterogeneous of data may takeanadvantage forusingMLandDLbecauseof and also falsediscoveryrate corrections. Besides,a large amount other hand,MLandDLdon't require anyassumptionsuchasnormality On the terms ofusingforhighthresholds. in a problemforresearchers methodology is usedwithalargeamount of data, this casemayhave (GWAS) thatdetectingtoSNPsGWAS withassociatedtothediseases. may haveasalternativesforthegenomewideassociationstudies Using machinelearning(ML)and deep learning (DL)approaches 3 1 , BaharTAŞDELEN Genome-WideLearning,Machine AssociationStudies, 1 , T.MehmetDORAK 2 , POSTER PRESENTATIONS 97

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Multi Predict The Case ] Machine Support Vector Vector Support 2 Mersin Mersin Üniversitesi Tıp Random Forest Random Deep Learning Deep References: [1] Kido, Sikora T., J. (2018). minor Are allelesmore likely to be riskalleles?. [ (2016) [3] Han, J., Pei, J., Kamber, & M. (2011). Data mining: conceptsand techniques. Elsevier. Corresponding author’s address [email protected] Öztornacı, R. Onur best suited for GWAS analysis as an alternative to conventional methods. We used 10 used We methods. conventional to alternative an as analysis GWAS for suited best over and effect bias for avoid to method Keywords Table1. markers of gender case In procedure. QC passed was which method isthe best SVM that are shown The results in GWAS. diseases to associated with SNPs to identify to accurate %73 reached has RF results. specificity and sensitivity high as well as rate accuracy a high with RF shown However, classifying. non choosing for accuracy %65 reach to able was DL data. GWAS using for appropriate not may Min). (7 time short very a in results the reported has DL that said be can it Besides, RF. to PPV similar quite specificity=1, sensitivity=0.97, NPV=0.96, and (PPV=1 method accurate most the be to appeared SVM (Ta Accuracy=0.98) discovery rate corrections. Besides, a large amount of data may take an advantage for using M using for advantage an take may data of amount a large Besides, corrections. rate discovery between model best the for comparison to allows situation this as well as patterns heterogeneous of because problem the of one is time computation Nevertheless, data. SNP on DL and ML use be can methods different two Basically, Chi ii) Using on PLINK, well. i) Using clumping fre allele minor on depends method The problem. this handling for approach selection feature associated be may that the SNPs keep to allow it may and methods two combine it can method, this through with the disease when using ML methods and DL. Minor allelescan be given clueabout the complex re We [1]. diseases 1 UK, London, University Kingston Chemistry, and Pharmacy Sciences, Street, Building99,Redmond, 98052, WA, USA genome the for alternatives as may have approaches (DL) learning deep and (ML) learning machine Using methodology GWAS diseases. the to associated with SNPs to detecting that (GWAS) studies association wide ma case this data, of amount a large with is used false also and normality as such assumption any require don't DL and ML hand, other the On thresholds.

POSTER PRESENTATIONS 98 and NaturalSciences,BiruniUniversity,3.AYAR&DBiotechnologyInc. Sciences, BiruniUniversity,2.DepartmentofBiomedicalEngineering,FacultyEngineering 1. DepartmentofMolecularBiologyandGenetics,FacultyEngineeringNatural Aslı Yenenler Mete EmirÖzgürses CANCER ROLE OF MITOCHONDRIAL GENESINPROSTATE GENE ENRICHMENT ANALYSIS TO ELUCIDATE THE enriched genes will enable us to enhance the power ofdiagnosis genes willenableusto enhance enriched and the cell better understand howhighenergyissustained incancer events with cancer metabolismand stress conditions willenableusto and prostatecancer,with mitochondrial understandingtherelationship and hypoxic conditions in a cell and between the breast similarities due to the ROSability of both effecting themitochondrialdynamics cancer prognose kit can be valid to use inboth cancers. In our project, two different cancersare highly common,enrichingthe PAM50 breast of the similarities Since identify 4differentsubtypesofbreastcancers. (PAM50a PCRbasedtestfor50gene is them helps to signature)which kits intheworldandoneof have beendifferenttypesofprognosis prognostic testwhichappliesbothforprostate and breastcancer. There may beusedtogeneratea similarities [6]. These biological similarities organs arehormonedependentandhave tumors generatedfromthese hormonal growth with breastcancerand the factors ishighlysimilar studied, it hasbeenfound that the receptors forsteroidhormonesand is background ofprostatecancer the molecular the mortality(5].When and increases of prostatecancer the earlydiagnosis properties limit bybody also beincreased weightanddailycarbohydrate intake. These is generallyconsidered.However, it has beenfound that the PSA can [4]. Tocancer diagnoseprostatecancer,antigen level prostatespecific prostatic intraepithelial neoplasia(PIN) may betheinitiatorsofprostate studies haveshownthat proliferative inflammatoryatrophy (PIA) and rate, the initiationof prostate cancer isnot clearly known but recent though it effectsvastmajorityofthepopulation and hasahighmortality year.every new cases with a1million cancer progressive highly Even in adapting tumors to extreme hypoxicconditions.Prostate cancer a ROS isinvolvedinstabilizingtheHypoxia Inducing Factor (HIF1-alpha) production. Accordingtotheliterature, (ROS) oxygen species reactive to the the lactateproduction, generated hypoxicconditionincreases as Warburgreferred is which in cells, product [3].Due Effect,asafinal lactate formation dependent energyproduction andincrease glycolysis DNA damageincreases and mitochondrial signals to oncogenic function due mitochondrial their lose cells because mostcancer cells metabolic activitydiffersfromnormal and [2]. Cancercells, tumor cells between normal quite significant is difference other andthis from each differs cell of each fate determination[1].Themetabolism and cell balance, oxidation-reductionreactions calcium metabolism, role incell organelleandplaysanimportant Mitochondria isadouble-membrane Abstract 2 * 1 , AhmetMelihÖten 2 , ÖyküİrigülSönmez 3 , POSTER PRESENTATIONS 99 1. Wallace DC. Mitochondria and cancer. Nat Rev Nat Mitochondria and cancer. DC. 1. Wallace Mitochondrial Dynamics; Prostate Cancer; Gene Cancer; Prostate Dynamics; Mitochondrial 6. Risbridger, G. P., Davis, I. D., Birrell, S. N., & Tilley, W. D. (2010). D. W. Tilley, & N., Birrell, S. D., Davis, I. P., G. Risbridger, 6. Breast and prostate cancer: more similar than different. Nature Reviews 205–212. doi:10.1038/nrc2795 10(3), Cancer, Corresponding Author’s Address: Faculty Engineering, Biomedical [email protected] Yenenler Aslı University of Engineering and Natural Sciences, Biruni 10.4155/fmc.12.190. PubMed PMID: 23256813 10.4155/fmc.12.190. M. Picard Hajnóczky G, Eisner V, DM, Vincent AE,4. Turnbull Cell Biol. 2017 Nov;27(11):787- Mitochondrial Nanotunnels. Trends Epub 2017 Sep 19. PubMed 799. doi: 10.1016/j.tcb.2017.08.009. PMID: 28935166 et al. Urinary microRNAs for O, Tudoran B, Petrut 5. Balacescu O, prostate cancer prognosis, and treatment response: are we diagnosis, there yet? Wiley Interdiscip Rev RNA. 2017. doi:10.1002/wrna.1438 References: PubMed doi: 10.1038/nrc3365. Oct;12(10):685-98. 2012 Cancer. PMID: 23001348 Annibaldi, A.,2. metabolism in cancer cells Widmann, C. Glucose Metab Care. 2010. 13 (4), 466-470. PMID: Curr Opin Clin Nutr as a cancer cell mitochondria Targeting P. Huang Zhu D, S, Wen 3. doi: Jan;5(1):53-67. Chem. 2013 Med Future therapeutic approach. dissected prostate cancer cancer tissue as in-patient reciprocal healthy prostate and dissected STRING database, network analysis from the PPI to control. According and genes PAM50 genes and between the mitochondrial hub genes scores has been identified. interaction Keywords: Enrichment PAM50 prognose kit for prostate cancer cancer for prostate kit prognose Before patients. a developing PAM50 the expression check to are going we work, further kit, as a prognose level of critical from nCounter technology with nanostring genes POSTER PRESENTATIONS 100 Mumbai 400098,India 2. DepartmentofLifeSciennces,UniversityMumbai,Vidyanagari,Santacruz(East), 1. Dept.ofBioinformatics,GNIRD,G.N.KhalsaCollege,Matunga,Mumbai-400019,India Saba ShaikhAmir SILICO APPROACH ON HEMOGLOBINPHYTOCHEMICALS USING IN- COMPARISON OF ANTIGLYCATING PROPERTIES OF 098, India of Mumbai,Vidyanagari,Santacruz(East),Mumbai 400 University Address: Corresponding Author’s Modeling of Proteins, 365–382. doi:10.1007/978-1-59745-177-2-19 [2] Morris,G. M.,&Lim-Wilby, M.(2008). Molecular Docking. Molecular http://dx.doi.org/10.1139/apnm-2017-0119. Applied Physiology, NutritionandMetabolism.42(10):1054-1063. sweetener.potassium: Anartificial Antiglycating potentialofAcesulfame References: Phytochemicals, Keywords: identifying antiglycatingagents. It can be concluded that molecular dockingtools can be used for Hemoglobin. Otherphenolicacidswerealsohadhighbindingaffinity. highest binding affinity and most common interactingresidueswith Ferulicthat amongphytochemicals indicate results These acid hadthe artificial sweetenersshowed higher binding affinity than the sugars. and and Autodockwasusedfordockingstudies.Phytochemicals the proteinstructure was usedtooptimize approach. Growmacs on Hemoglobinwereanalyzedwiththehelpofmoleculardocking study the bindingsitesof different glycating and antiglycating agents number ofthesebasicaminoacidsand their accessibility. Inthepresent interact. Theamountofglycationproductsgenerateddependsonthe carbonyl groupsofthesugars the proteinstructuretowhich acids in concentrations. Lysine and arginine aretwo the mostimportant amino of sugarsvaryaccordingto their interaction,bindingaffinityand as advancedglycationendproducts(AGEs). Theglycatingpotential to generation of a group of called harmful compounds collectively leads interaction group ofsugarsandaminogroupsproteins.This between thecarbonyl of interaction process a noneznymatic Glycation is 1 , AparnaPatil Glycation, Glycated Hemoglobin, MolecularDocking, [1] Ali, A., More,T., [1]Ali,A., Sivakami,S. 2017. K., Hoonjan,A. 1 , Ahmad Ali 2 DepartmentofLifeSciennces, POSTER PRESENTATIONS 101 2 Department of Bioengineering, , Saliha Ece Acuner-Ozbabacan , Saliha Ece 2 [1] Khosravi A, Goliaei B et al. Active repurposing Jayaram B, Drug Repositioning, Docking, Melanoma Docking, Drug Repositioning, , Beste Turanli 1 here is a rapid increase in the melanoma incidences globally with high in the melanoma incidences globally here is a rapid increase Istanbul Medeniyet University, Istanbul 34700, Turkey, E-mail:ece. Istanbul 34700, Turkey, Istanbul Medeniyet University, [email protected] range of omics data. Molecular Medicine. 2019; 25:30. doi: 10.1186/ doi: 25:30. 2019; Medicine. Molecular data. of omics range s10020-019-0098-x Drug Transcriptomic-Guided KY. Arga G, Gulfidan B, [2] Turanli Tool: Search Bioinformatics New a by Supported Repositioning 2017;21(10):584- biology. geneXpharma. Omics : a journal of integrative PMID: 29049014 91. doi: 10.1089/omi.2017.0127. PubMed Corresponding Author’s Address: using a combinatorial approach including drug repositioning and dockingusing a combinatorial approach including in the optimization of drugand the ability of computational tools to help development efforts. Keywords: References: widea and PheWAS GWAS, on based melanoma for candidates drug of melanoma, we categorized them into four subgroups based on theirthem melanoma, we categorized used in cancer treatment; already being namely, relevance to cancer; to cancerrelated in cancer treatment; not being used as complementary performedwe gene-drugand random treatment yet; Lastly, pairs. by the listed significant genesdocking studies for the proteins encoded structural details for importantand their ligand drugs and analyzed the ofthe importance reveals study detail. This representative case studies in T challenges in the treatment makes developmentmortality rates and the is methods crucial [1]. Drug repositioning of efficient drug discovery drugs. providing new usage areas for existing an innovative approach search and used a gene expression-guided focused on melanoma We expressions and theirprovides gene [2], which tool, geneXpharma determining the significant gene-drugdrug interactions. After pairs in Nevin Betul Imat Nevin Betul Program, Istanbul and Nanoengineering Graduate Studies, Nanosicence 1. Institute of Turkey, Istanbul 34700, Medeniyet University, Turkey Istanbul 34700, Istanbul Medeniyet University, of Bioengineering, 2. Department DRUG REPOSITIONING AND DOCKING STUDIES ON ON STUDIES DOCKING AND REPOSITIONING DRUG MELANOMA POSTER PRESENTATIONS 102 2. EgeUniversityFacultyofMedicineDepartmentParasitology,35100-İzmir 35040-İzmir 1. EgeUniversityFacultyofScienceDepartmentBiology,MolecularBiologySection, Cemal Ün Hüseyin Can PROTEINS POTENTIAL OF TOXOPLASMA GONDII APICOPLAST AN INSILICO DETECTION FOR ANTIGENIC 1 and post-translationalglycosylation, as N-linked modificationssuch alleles, indicating a strong binding among epitopes and MHC-I/II a lot of epitopeswhichhavelowIC50 rank value and percentile ribosomal proteinS5,L11, S2, L36, and S17 werepredictedto have high antigenicity value(P<0.05). In addition, to haveasignificantly signal peptide whereas ribosomalproteinL36andS17were predicted proteins, ribosomalproteinS5, L11andS2werepredictedtohave predicted asprobableantigen. Among antigenic proteins, 19were post-translational modificationsmotifs.Among the 28 apicoplast epitope sites aswell and Tcell(MHC-I/II) conformation, Bcell or a highantigenicityvalue,werefurtheranalyzedforstructural proteins thathaveasignalpeptide transmembrane domain.Antigenic parameters, subcellularlocalization,and to analyze physico-chemical analyses wereappliedforallpredictedantigenicapicoplast proteins to reveal antigenicprobability and then, severalbioinformatics analysis. Forsilico thispurpose, proteins wereprimarilypredicted encoded proteins(n:28)ofT.of apicoplastgenome gondiiusingin ClpC gene.Inthisstudy,potential the antigenic weaimedtoinvestigate ORFs ofunknownfunction,sufBgene(formerlynamedycf24), and (transfer RNA)genes,aputativeribosomalproteingeneoperon, five genes, RNApolymerasetheelongationfactortufAgene,tRNA rRNAs both largeandsmall-subunit the apicoplastgenomeincludes and event originated from asecondaryendosymbiotic is length which nuclear genome,T. gondii contains an apicoplast genome in35 kb antigenhasnotbeendiscoveredyet.Apart from its vaccine effective againstT.used indevelopmentofvaccine gondiiinfectionbut, an heat shockproteinBAG1protein MIC1, have been andmicroneme proteins suchasdensegranuleproteinGRA1, rhoptry protein ROP2, worldwide. economic losses To date, wide range of nuclear genome abortion in livestocksuchassheepand goat, and leads to considerable toxoplasmosis isa common causeof In termsofveterinarymedicine, manifestations. clinical itleadstolife-threatening transplant recipients, syndrome, immunosuppressedcancerpatientsand immuno-deficiency patientssuchasthosewithacquired but, in immuno-compromised symptoms clinical does notleadtoserious toxoplasmosis generally also beacquiredby transplacental infection.Inhealthyindividuals, containing bradyzoites. Duringprenatalperiod,toxoplasmosiscan by sporulated oocysts or by consuming raw or undercooked meat Toxoplasmosisthe foodorwatercontaminated is acquiredbyingesting toxoplasmosis, isan obligate intracellular apicomplexanparasite. Toxoplasma gondii, causative agent of the diseaseknownas 1 , Sedef ErkuntAlak 1 , AhmetEfeKöseoğlu 1 , MertDöşkaya 2 , POSTER PRESENTATIONS 103 [email protected] T. Gondii; Antigen; Apicoplast; Epitope; Ribosomal Protein Ribosomal Epitope; Apicoplast; Gondii; Antigen; T. Keywords : Keywords Author’sCorresponding Address: acetylation and phosphorylation. To the best of authors’ knowledge, knowledge, authors’ of the best To phosphorylation. and acetylation properties other and potential antigenic the show to first study the this is gondii. of T. proteins of apicoplast-derived POSTER PRESENTATIONS 104 2. EgeUniversityFacultyofScienceDepartmentBiology,ZoologySection,35040-İzmir 35040-İzmir 1. EgeUniversityFacultyofScienceDepartmentBiology,MolecularBiologySection, Cemal Ün Ahmet EfeKöseoğlu THEIR EFFECTS ON OMPB PROTEIN ISOLATED AND FROM INTURKEY TICK SAMPLES GENE OF RICKETTSIA STRAINS AESCHLIMANNII INVESTIGATION OF POLYMORPHISMS ON OMPB 1 com Address: Corresponding Author’s Epitope; Post-Translational Modification : Keywords changes incharacteristicsofprotein. on OmpB genecausesalot of Overall, thepresenceofpolymorphisms methylation andacetylationsites. and phosphorylationsites,except glycosylation as N/O- such in posttranslationalmodificationregions alterations regions werealteredbypolymorphisms.Thereseveral epitope and Tcell It wasdetectedthatBcell number ofalphahelices. in and thedecrease in numberofrandomcoil and providetheincrease parametersassociatedwithstabilityandsolubility the physico-chemical that to antigenicity,polymorphisms onOmpBgeneincreased changed strains of Ri. aeschlimannii.Accordingto in silicoanalysis,it was found detected forthefirsttimeinthisstudy when compared to 42 reference change revealed.ObtainedNCBIdata showed that, 2 oftheSNPs were though codonalteration, noaminoacid variations, three remaining codon alteration causedaminoacid changes at 7 positions. For with accession number, AF123705.1. Among 10 nucleotide variations, strain strains werefoundwhen comparedto reference aeschlimannii total of 10 nucleotide polymorphismsinompB single gene of three Ri. tools. Asaresult, bioinformatics protein werepredictedbyseveral translational modifications,secondaryand tertiary structure of OmpB surface accessibility, allergenicity, B and T cell epitope regions, post- effects of detected polymorphisms on antigenicity, stability, solubility, Information (NCBI) and translated into protein sequence. Thereafter, strains inNational Center forBiotechnology them withreference identity bycomparing software andanalyzedfornucleotidesequence Later,by ABI3730XL. were alignedby generatedsequencesMEGA7 Turkeyin samples tick detected in usingPCRmethodsandsequenced aims, OmpB gene was amplified fromthreedifferentRickettsiastrains on OmpBprotein.Forof detectedpolymorphisms and theeffects this in Turkeystrains isolatedfromticksamples aeschlimannii Rickettsia it wasaimedtoinvestigatethepolymorphismsonOmpBgeneof the geneisusedindiagnosisandstrainidentification.Inthisstudy, gene and plays remarkablerolesinbacterialpathogenesis.Also, strains, OmpB(outer-membraneprotein B)isahighlyconserved and transmittedbymainlyarthropod vectors suchastick.InRickettsia is anobligate intracellular bacterium Rickettsia causingRickettsiosis 1 , HüseyinCan R. Aeschlimannii; Ompb; Polymorphism; R.Aeschlimannii; Antigen; 1 , SedefErkuntAlak ahmetefekoseoglu@gmail. 1 , SamiyeDemir 2 , POSTER PRESENTATIONS 105 1,2 , Efe SEZGIN , Efe SEZGIN 1 Likewise, OTU tables with or without similar taxonomies and/or samples Likewise, OTU rules, and produced taxonomy can thencan be merged by user-defined be used to reconstruct phylogenetic tree topology from the reference if data have a low-qualitydatabase. Furthermore, taxonomic resolution, it is possible to improve resolution via any reference database or online in Name Resolution Service (TNRS). Finally, services such as Taxonomic order to adjust the phylogenetic tree, it is possible to use tools such as Project (RDP) are under active development. Overall, PhyloMAF can workProject its features include but are notwith heterogeneous microbiome data and merging, contrasting, comparing, manipulating, maintaining, to limited it is example, For exporting and report generation. plotting, analyzing, on the higher taxonomicpossible to aggregate taxonomy data based produceto database reference the to taxonomy rematch then and ranks phylogenetic tree topology with tips that represent merged rank nodes. microbiome analysis framework written in Python. The framework isframework The Python. in written framework analysis microbiome on GitHub (https://git. under active development and will be published PhyloMAF support importingio/fjME1) at the time of article publication. popularand file formats BIOM tables, formatted OTU traditional TSV both supportedCurrently Nexus. or Newick as such file formats tree phylogenetic and Open Tree SILVA reference taxonomy databases are Greengenes, DatabaseRibosomal and NCBI of support while (OTT), Taxonomy life of phylogenetic trees. Traditional and probably all of the currently available phylogenetic trees. Traditional merge two or more OTU tools make practically impossible to properly toolssuch of Lack trees. phylogenetic and taxonomies with along tables abundance and has OTU become particularly bothersome if a researcher data. Given the lack of usefultaxonomy data without any raw sequence address would that framework new a develop to aimed we tools, community phylogenetic a next-generation the discussed shortcomings. PhyloMAF is rRNA or ITS based reference databases for phylogenetic and taxonomic databases for phylogenetic and rRNA or ITS based reference disparity have a considerable level of classification of microorganisms inconsistency poses serious trouble [3] Result of this between each other. comparison or merging, for scholars that perform research that involves which ideally could have beenvalidation of independent studies, each of such as Greengenes, SILVA, processed using different reference databases there is no effective tool etc. Due to such disparity issues, currently, RDP, with different taxonomies and tables that can merge two or more OTU transformed the research potential in metagenomics, microbiome,transformed the research communityDNA fields. [1] Microbiome research and environmental to the new advancements in microbiomeswiftly grows and responds and microbiome research methodologies research. Nevertheless, concepts with frequent introduction of novel approaches evolve rapidly to replace (ASV) that are expected Variants such as Amplicon Sequence 16S/18S Furthermore, [2] Units (OTU). Taxonomic traditional Operational Recent technological leaps in genome sequencing technologies have leaps in genome sequencing technologies Recent technological Farid MUSA Technology, Izmir Institute of of Food Engineering, Program, 2. Department 1. Biotechnology Izmir, Turkey PHYLOMAF: NEXT GENERATION PHYLOGENETIC PHYLOGENETIC GENERATION NEXT PHYLOMAF: FRAMEWORK ANALYSIS MICROBIOME POSTER PRESENTATIONS 106 URL: https://food.iyte.edu.tr/staff/doc-dr-efe-sezgin URL: [email protected], Address: Corresponding Author’s (Robert C.Martin):Prentice HallPTR;2006 [5] MartinRC,M.Agileprinciples,patterns,andpracticesinC# 2006;22(21):2688-90. analyses withthousandsoftaxaandmixedmodels.Bioinformatics. RAxML-VI-HPC:[4] StamatakisA. maximumlikelihood-basedphylogenetic PMID: 28361695;PubMedCentralPMCID:PMCPMC5374703. 2):114. Epub2017/04/01.doi:10.1186/s12864-017-3501-4.PubMed - howdothesetaxonomiescompare?BMCGenomics.2017;18(Suppl [3] Balvociute M, Huson DH. SILVA, RDP, Greengenes, NCBI and OTT PMCPMC5702726. ismej.2017.119. PubMedPMID:28731476;CentralPMCID: ISME J. 2017;11(12):2639-43. Epub 2017/07/22. doi: 10.1038/ replace operationaltaxonomicunitsinmarker-gene dataanalysis. [2] CallahanBJ, McMurdiePJ, HolmesSP. Exactsequencevariantsshould 27785127; PubMedCentralPMCID:PMCPMC5061000. Epub 2016/10/28. doi: 10.3389/fmicb.2016.01611. PubMed PMID: from Genome-Wide AssociationStudies.Front Microbiol.2016;7:1611. Relationships between Host Genome and Microbiome: New Insights References: Keywords : such asscikit-bio orQIIME2. either independent framework or by integrating it into popular frameworks conclusion, PhyloMAFwillbeusefulforsmallandlargescalestudiesas research community that is designed by the S.O.L.I.D principles. [5] In based microbiomedataanalysisPythonframeworktothe development of PhyloMAF is to introduce a comprehensive taxonomy be usedfortreeconstruction/correction.Theprimarymotivationthe distancematrixthatcan associated withtaxaIDs,orapplypre-evaluated import andapplymultiplesequencealignmentwithnames or byfetchingNCBIdatabase.Otheroptionsarethepossibilitytodirectly sequence dataforeverytaxonintreemanuallybyimportingFASTA files obtained viainternalframeworkfeatures.PhyloMAFallowstheusertoadd RAxML [4] and OTU sequence data, which can be added manually or Microbiome;Taxonomy; Phylogenetics [1] Abdul-Aziz MA, Cooper A, Weyrich Cooper A, LS. [1] Abdul-Aziz MA, Exploring Assoc. Prof. EfeSEZGIN: E-mail:

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POSTER PRESENTATIONS 108 (105):2999-3093. [7] Tomasi J., Mennucci B., Reviews. 2005; Cammi R., Chemical (Theochem). 1999;(464):211-226. [6] Tomasi J., MennucciB., CancèsE.J. JournalofMolecularStructures Wallingford CT8985. [5] FrischM. J., etal.Gaussian09VersionC.01, 2009, Gaussian, Inc., [4] Spartan08 for Windows,Wavefunction, Inc.,Irvine,CA92612 USA. Flavonoid Luteolin. Mini-Rev. Med.Chem.2009;(9),31−59. [3] Lopez-Lazaro,of the M.DistributionandBiologicalActivities since 1992.Phytochemistry2000;(55):481−504. [2] Harborne,J.in FlavonoidResearch Advances B.,C. A. Williams, 4432-2. 2014 July.and Genomes Chromosomes 4: DNA, ISBN978-0-8153- Walter P. MolecularBiologyoftheCell(6thed.).Garland. p. Chapter References: [1] Alberts B, Johnson A, Lewis [1]AlbertsB,J, A, Johnson RaffM,RobertsK, POSTER PRESENTATIONS

, 1977. ,

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nstitute of S I 1 This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) (Project no. (Project (TUBITAK) Turkey of Council Research Technological and Scientific by the supported was work This Department of Medical Engineering, Faculty of Engineering, Acibadem University, Istanbul, Turkey Istanbul, University, Acibadem Engineering, of Faculty Engineering, Medical of Department

antibiotics which microorganisms cannot developresistance to haveincreased 2 , “Peptide Antibiotics Developed by Mimicking Natural Antimicrobial Peptides,”

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Dynamics; Peptide Antibiotics. Dynamics; Peptide peptides’ movements much more easily. the 250ns of the end molecular dynamics procedures, at Through simulation, as seen figure 1, it is observed that the newly developed peptide headed towards the bacterial membrane and can penetrate into the membrane from its end parts. Keywords: Figure 1. were visualized green with balls, membrane lipids light blue, peptides CPK water molecules white and red and colour, helix purple alpha representation was chosen b) The water molecules were invisible made to be able to observe methods[3]. The method is quite interactive and involves the following steps; the three-dimensional structures of a newly developed peptide and a pre-equilibrated lipid bilayer are obtained. The protein is Then, the water also inside the water. lipid bilayer and the aligned on are removed. Equilibration molecules overlap with the proteins that as minimization system are carried out and of the newly developed final steps[4]. occurring that bacteria cannot antibiotics with a peptide structure develop resistance project is to develop The primary aim of this to[2]. mimic that antimicrobial peptides Cathelicidin naturally occurring through the application of molecular dynamicstechniques. Molecular dynamicstechniques are used not only to quickly observe newly how also to save time but designed peptides act on the bacterial membrane to observe phenomena that cannot be observed through experimental In recent years, the rapid increase of infectious microorganisms’ resistance insufficiencies against antibiotics has led to in the treatment of many fatal infections. As microorganisms rapidly gained resistance antibiotic, the number of studies of newly developed against each type of antibiotics which microorganisms on the development cannot develop resistance to have increased[1]. naturally There are already

Molecular antibiotics developed by mimicking natural natural mimicking by developed antibiotics words: development N. Unubol D. Andreu and L. Rivas, “Animal Antimicrobial Peptides: An Overview,” proteins,” of pp.folded 415 1977 Dynamics Nature Karplus & Gelin “McCammon, M. and Karplus, Gelin, R. B. McCammon, A. J. Theoretical Biophysics Group, “NAMD Dalke A. Humphrey, W. Corresponding author’s address [email protected] Ozcan: Aslihan Acknowledgements: 217S060 References [1] [2] [3] [4] [5] Figure 1. helix purple colo molecules were Through molecular dynamic and membrane bacterial the towards headed Key In recent years, the rapid increase inf many fatal the antibiotics peptides that mimic naturally occurring to obser also but time to save membrane bacterial act on the peptides designed newly how observe to quickly not only used are methods experimental through observed be cannot that a pre and minimizati and peptide developed newly a of Equilibration structures removed. dimensional are proteins the with overlap that molecules water the Then, water. the inside also and steps final as out carried are system developed

Aslihan Ozcan Acibadem University, of Medical Engineering, Science, Department 1. Institute of Istanbul, Turkey University, Engineering, Faculty of Engineering, Acibadem 2. Department of Medical Istanbul, Turkey MOLECULAR DYNAMICS SIMULATIONS OF PEPTIDE OF MOLECULAR DYNAMICS SIMULATIONS NATURAL MIMICKING BY DEVELOPED ANTIBIOTICS PEPTIDES ANTIMICROBIAL POSTER PRESENTATIONS 110 Aslihan Ozcan:[email protected] Address: Corresponding Author’s dynamics.,” J. Mol. Graph., vol. 14, no. 1, pp. 33–8, 27–8, Feb. 1996 [5] W. Humphrey,molecular visual “VMD: Schulten, and K. Dalke, A. no.April,2017. Urbana, USA, [4] TheoreticalBiophysicsGroup, “NAMD-Tutorial (Win),”Univ. Illinois, & Karplus Nature1977 Dynamicsoffoldedproteins,”Nature,1977. [3] J. McCammon, B. R. Gelin,and M. Karplus, A. “McCammon, Gelin Overview,” pp.415–433. [2] D. Rivas, “Animal Andreu and L. AntimicrobialPeptides: An Access, 2017. NaturalPeptides,” Antimicrobial Mimicking Microbiol. Open Clin. References: 217S060 Technologicalof TurkeyCouncil Research (TUBITAK) (Projectno. Acknowledgements: andProject no. [1] N. Unuboletal.,“Peptide by AntibioticsDeveloped This work was supported by and the Scientific 118Z859 ). POSTER PRESENTATIONS 111 atoms of any two residues are less α 1 Allostery, Protein Interaction, Residue-Residue Interaction, Protein Contact Allostery, , Nurcan Tunçbağ , Nurcan corresponding PDB identifiers. To associate the mutations to allosteric to associate the mutations identifiers. To corresponding PDB from COSMIC collect the disease associated mutations we paths, it may have a allosteric site or path, is in an mutation a If database. significant effect its partners or with in the binding of the protein with drug molecules. Keywords: Network, Allosteric Paths and the best coverage were considered. Additionally, we eliminated we eliminated Additionally, and the best coverage were considered. proteins having 90% sequence similarity to have one represent for we include the homology enrich the structural dataset, each protein. To structural data interactions and Protein-protein models in ModBase. are obtained from PDB and enriched with the predictive approaches Drug binding site information are obtained such as Interactome3D. which from Drugport lists the protein-drug binding regions and their whether the allosteric effect is transmitted through the core or the through whether the allosteric effect is transmitted we will discuss whether these regions 1). Furthermore, surface (Figure are between different regions of proteins or between different regions retrieved protein we this purpose, regions. For in the same binding Bank (PDB) Data structures which have allosteric region from Protein Then the coverage of the and AlloSteric (ASD) database. Database structure (the number of residues present in the structure) were checked we assume that the minimum number of residues connecting allosteric we calculate all Therefore, regions will be located in allosteric paths. residues in allosteric regions. the set of between paths shortest pair with any disease analyze these paths if they overlap additionally We sites in binding drug the sites and causing mutations, phosphorylation we also predict In this study, proteins and protein-protein interactions. the presence location of allosteric and paths in protein structures protein allosteric structure-basedregions from through a network as a each protein and protein complex structure represent We view. residue-residue allosteric paths potential extract and network contact each structure we construct a residue- distant sites. For connecting two e) where v is the set of residues in the protein residue contact graph R (v, these residues e is the set of contacts between or protein complex and where the C if the distance between this work, In then these residues are labelled as contacting. 7Å than functional organization of the proteome. They bind each other through of the proteome. They bind each other functional organization non-covalent structure of a protein is also interactions. The native or van der hydrophobic forces such the non-bonded determined by in one region a perturbation are flexible and forces. Proteins Waals site may affect a distant which is called allosteric regulation. Allosteric activity by protein role in regulating very important a regions play we examine the a non-activebinding a ligand to site. In this study, Protein-protein interaction networks are helpful to understand networks are helpful to understand interaction Protein-protein Melike Çağlayan Technical Informatics, Middle East Department of Health School of Informatics, 1. Graduate Turkey University, Ankara, ALLOSTERIC REGULATION IN PROTEINS THROUGH THROUGH IN PROTEINS REGULATION ALLOSTERIC NETWORKS CONTACT RESIDUE-RESIDUE POSTER PRESENTATIONS 112 ntuncbag/ Ankara, Turkey [email protected]://users.metu.edu.tr/ Middle EastTechnicalDepartment ofHealthInformatics, University, Address: Author’s Corresponding interaction betweentwoallostericsites. interaction betweentwo represents active sites.Yellow lineshows sites and active sitesofproteincomplex(coreorsurface).Greenline arrangement.). Bluelinesareinteractionbetweenallosteric allosteric active site.Thesurfaceareasconnectedto the sectorarehot spots for interactions and sector (sectorsconnectsomesurfacepositionsto the are active sites;gray points are allostericsites.Alllinesrepresent 1. Figure active site. The surface areas connected to the sector are hot spots for allost allostericAll sites. lines represent interactions sectorand (sectors connect some surface positions to the Figure 1. Keywords or path, it may have a significant effect in the binding of the protein with its partners or with drug molec we paths, protein approachessuchInteractome3D as Protein represent foreach protein. To enrich the structural dataset, we coverage Then structures proteins or thesurface allosteric of location and the drug bindingsites additionally analyze paths. we assume that the minimum number of residues connecting allosteric regions will be located in allosteric C the residues these between contacts of set eis the and complex or protein protein F a residue as structure a structure through from regions allosteric to a non a ligand by binding activity protein regulating in role region may affect a distant site which is called allosteric regulation. non bind each other through non Protein Turkey Graduate School ofInformatics, Department of Health Informatics, Middle East Technical University, Ankara, address author’s Corresponding interaction between two active sites. Yellow line shows interaction represents between line Green two allosteric surface). or (core sites. complex protein of sites active and sites allosteric between interaction or each structure we construct a residue 1 - Graduate School of Informatics, Department of Health Informatics, Middle East Middle Informatics, Health of Department Informatics, of School Graduate bondedforces such hydrophobic o α the coverage of the structure (the number of residues present in the structure)

Therefore, we calculate all pair shortest paths between the set of residues in allosteric region allosteric in residues of set the between paths shortest pair all we calculate Therefore, - - - atom A cartoon representation of the slice mappings. Redpoints Acartoonrepresentationoftheslice drug binding regions and their corresponding PDB identifiers. protein interactionsandstructural data protein interaction networks are helpful to understand functional organization of the proteome. They

through residue through Allosteric regulation in proteins regulation Allosteric [email protected] or between different regions in the same binding regions.

were collect : which have allosteric region allosteric have which A cartoon representation allostery, protein interaction, residue interaction, protein allostery, s

of any two residues are less than 7 than less are residues two of any (Figure 1)

considered. Additionally,we eliminate the the - t disease associated he network contact residue in proteinsand se paths . paths Furthermore, Furthermore, - covalent interactions. The native structure of a protein is also determined by the the by determined also is of a protein structure The native interactions. covalent

Graduate School of Informatics, Graduate School Melike Çağlayan Melike in proteinstructures

if they

http://users.metu.edu.tr/ntuncbag/

. of the slice mappings. Red points are active active are points Red mappings. slice of the Drug binding site information

protein from Protein Data Bank (PDB Bank Data Protein from r van der Waals forces. Proteins are flexible and a perturbation in one we mutations mutations overlap networks - -

based network view. We residue contact graph R (v, e) where v is the set of residues in the the in residues of set the is v where e) (v, R graph contact residue will

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Ankara, Turkey discuss with any disease causing - Å then these residues are labelled as as contacting labelled are residues these Å then residue contact network, allosteric paths are are extract potential from COSMICdatabase from whether the the whether , obtained from PDBfrom obtained d

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. POSTER PRESENTATIONS 113 Pınar Pir e-mail: Pir Pınar [email protected]

[1] Sivakumar KC1, Dhanesh SB, Shobana S, James J, James J, Shobana S, Dhanesh SB, [1] Sivakumar KC1, Neural Stem Cells, Differentiation, Signalling Pathways, Pathways, Neural Stem Cells, Differentiation, Signalling 1 Pınar Pir Corresponding Author’s Address: [2] Chelliah V1, Laibe C, Le Novère N. BioModels Database: a BioModels Database: Novère N. C, Le [2] Chelliah V1, Laibe repository of mathematical models of biological processes. Methods Mol Biol. 2013;1021:189-99. Pubmed PMID:23715986 Jürgen [3] Stefan Hoops, Sven Sahle, Ralph Gauges, Christine Lee, Mendes Natalia Simus, Mudita Singhal, Pedro Liang Xu, Pahle, 22, Bioinformatics, Volume SImulator, COmplex PAthway COPASI—a 3067–3074. Issue 24, 15 December 2006, Pages Keywords: Keywords: Mathematical Modeling References: model neural stem cell to A systems approach biology S. Mundayoor OMICS. regulation by notch, shh, wnt, and EGF signaling pathways. 2011 Oct;15(10):729- 37 PubMed PMID: 21978399 new models of these pathways using COPASI [3] tool. Ultimate aim Ultimate tool. [3] using COPASI new models of these pathways of this study is gene regulatory networks and multiple modelling the reprogramming in the differentiation and together signaling pathways neuron stem cells processes fibroblasts and of induced pluripotent (IPS), cells. of neural differentiation and reprogramming Better understanding new drug identification of for new approaches to lead process may diseases. targets and treatment of neurodegenerative differentiation. Some of the pathways which are thought to be effective to be effective which are thought differentiation. Some of the pathways RA (Retinoic Acid Shh, WNT, in neuron differentiation are: Notch, signaling pathways (Bone Morphogenetic Proteins) BMP Signaling) and is [1]. BIOMODELS [2] database of mathematical a a repository models of biological processes and has mathematical models of Notch, WNT plausible model a implemented in SBML.Shh pathways and However, of RA and BMP pathways is not available. Hence we aimed to construct repression of transcription factors. The identification of signaling identification of The transcription factors. repression of cell in essential step is an factors transcription the affecting pathways differentiation mechanism studies, and dynamics of the mechanisms the time-dependentstudying by obtained can be these of activities pathways. There is limited how information in the literature about gene-regulationepigenetic profiles, and pathways, networks, signaling are activated or inhibited in the processes of reprogramming and Induced pluripotent stem cells stem cells Induced pluripotent cells (also known as IPS or iPSCs) are cells. In from adult directly stem cells can be produced pluripotent that recent induced pluripotent stem (IPS) cells years, are being increasingly of in vitro models of neurological diseasesused in development in humans. During differentiation mechanisms, and reprogramming gene regulatory restructuring the by generated new cell identities are these networks rely on expression regulatory or networks. Mostly, REPROGRAMMING PROCESSES Gülçin Selçuk, of Bioengineering, Çayırova, KOCAELI University, Department Gebze Technical RECONSTRUCTION AND INTEGRATION OF OF INTEGRATION AND RECONSTRUCTION SIGNALLING OF MODELS MATHEMATICAL AND CELL DIFFERENTIATION IN STEM PATHWAYS POSTER PRESENTATIONS 114 3.Max PlanckInstituteforBiologyofAging,Cologne,Germany College London,UK 2. DepartmentofGenetics,EvolutionandEnvironment,InstituteHealthyAging,University Genome Campus,Hinxton,Cambridge,CB101SD,UK. 1. EuropeanMolecularBiologyLaboratory,BioinformaticsInstitute,WellcomeTrust Linda Partridge Handan MelikeDönertaş DISEASES ANDWAYS TO IMPROVE HEALTHSPAN GENETIC CHARACTERISTICS OF AGEING-RELATED [email protected], [email protected] Address: Corresponding Author’s Keywords: polypharmacy onelderlywhileretainingthebenefits. the effectof number ofdrugsanddecrease pathologies withalimited offer anopportunitymultiple diseasesprevent totarget or even ageing-relatedIdentifying ashared organisms. among mechanism assays usingmodel andlifespan of senescence profiles expression as gene our resultswithpubliclyavailabledatasetsforageingsuch the linkbetweenageingandthesediseases,wearenowcombining (e.g. cardiovascular,or comorbidities.Inorderto explore endocrine) Moreover, these resultscannot be explained onlyby disease categories not prevalentinotherdiseases. component thatis a genetic share do diseases based ontheirageofonsetprofilessuggestlate-life comparing 116diseases almost 500kparticipants.Ouranalysis data, which includesgenomic,medicaland lifestyle measuresfor that is alsoimplicatedinageing. We performGWAS onUKBiobank shareageneticcomponent age-of-onset with similar whether diseases has not been explored in a systematic manner. In this study, we test however, diseases, between ageingandage-related molecular link expectancy,burden ofageing- overall The increases. relateddiseases in life With therise factor formanydiseases. Ageing isamajorrisk 2,3 , JanetM.Thornton Ageing; GWAS; UKBiobank Ageing; GWAS; 1 , MatiasFuentealbaValenzuela 1 1,2 , POSTER PRESENTATIONS 115

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r i i [1] O'Neill LA, Bowie AG. The family of five: TIR-domain- [1] O'Neill LA, Bowie AG. o o Bioinformatics; Probiotic, TIR Domain; TLR Signaling Bioinformatics; Probiotic, model was found to be be to found was model using bioinformatics model was found to be be to found was model 21392. using bioinformatics 1 receptor 21392. 1 receptor

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Sequence identity of LcTIR with other TIR domains Sequence identity of LcTIR with other TIR Türköz B, Koelblen T, Felix C, Candusso MP, O'Callaghan D, Vergun D, O'Callaghan MP, Candusso C, Felix T, Koelblen B, Türköz Nat Rev Immunol.May;7(5):353 2007 bioinformatics Ege University, Faculty of Engineering, Engineering, of Faculty University, Ege Türköz B, Koelblen T, Felix C, Candusso MP, O'Callaghan D, Vergun D, O'Callaghan MP, Candusso C, Felix T, Koelblen B, Türköz Nat Rev Immunol.May;7(5):353 2007 bioinformatics Ege University, Faculty of Engineering, Engineering, of Faculty University, Ege -

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Revealing Probiotic TIR Revealing Probiotic TIR

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interleukin O'Neill LA,Bowie AG. RMSD valueswere evaluatedbetween the - Kawai T Chan SL, Low LY, Hsu S, Li S, Liu Santelli T, Negrate E, Le G, Reed O'Neill LA,Bowie AG. Salcedo SP, Marchesini MI, Lelouard H, Fugier E, Jolly E, Fugier H, Lelouard MI, Marchesini SP, Salcedo RMSD valueswere evaluatedbetween the - Kawai T Kaplan Chan SL, Low LY, Hsu S, Li S, Liu Santelli T, Negrate E, Le G, Reed Salcedo SP, Marchesini MI, Lelouard H, Fugier E, Jolly E, Fugier H, Lelouard MI, Marchesini SP, Salcedo Kaplan

] ] ] ] ] ] ] ] 3 4 5 2 Ege University, Graduate School o School Graduate Ege University, 3 4 5 2 , Burcu Kaplan Türköz , Burcu Kaplan Ege University, Graduate School o School Graduate Ege University,

Results of multiple sequence alignment between LcTIR and and LcTIR between alignment sequence multiple of Results Lactobacillus case Lactobacillus conserved regions on domains TIR bacterial [ i innate in mimicry Molecular and quality modelswere obtained 284(32): 21386 [ [ Alexopou Corresponding author’s address Engineering, Food of Sciences,Department Applied and Natural of School Graduate University, Ege İzmir,TURKEY structurally similar t Acknowledgements 18266466 bioinformatic [1] 2. Table References: 1. Table LcTIR 1 Toll

TIR domain domain TIR receptors. of the Toll/interleukin 1 receptor (TIR) domain of the immunosuppressive Brucella effector BtpA/Btp1/TcpB. effector Brucella immunosuppressive the of domain (TIR) 1 receptor Toll/interleukin the of FEBS Lett. 2013 Nov 1;587(21):3412 dependent on the TIR the on dependent will focus on investigating the interactio the investigating on focus will system cytopla elucidated. been not has mechanism [ signalling. Keywords been shown to manipulat to shown been domains LcTIR LcTIR

Lactobacillus case Lactobacillus Results of multiple sequence alignment between LcTIR and and LcTIR between alignment sequence multiple of Results conserved regions on domains TIR bacterial TIR domain domain TIR Molecular mimicry in innate i innate in mimicry Molecular [ 284(32): 21386 [ and quality modelswere obtained [ Alexopou system Ege University, Graduate School of Natural and Applied Sciences,Department of Food Engineering, Engineering, Food of Sciences,Department Applied and Natural of School Graduate University, Ege İzmir,TURKEY mechanism has not been elucidated. been not has mechanism Corresponding author’s address structurally similar t Acknowledgements 18266466 bioinformatic [1] 2. Table References: 1. Table LcTIR 1 Toll

of the Toll/interleukin 1 receptor (TIR) domain of the immunosuppressive Brucella effector BtpA/Btp1/TcpB. effector Brucella immunosuppressive the of domain (TIR) 1 receptor Toll/interleukin the of FEBS Lett. 2013 Nov 1;587(21):3412 receptors. dependent on the TIR the on dependent [ been shown to manipulat to shown been domains cytopla interactio the investigating on focus will signalling. Keywords LcTIR LcTIR 1 References: Nat Rev Immunol. receptor signalling. in containing adaptors Toll-like 2007 May;7(5):353-64. PubMed PMID: 17457343. The role of pattern-recognition receptors in Akira S. T, [2] Kawai Immunol. 2010 receptors. Nat on Toll-like innate immunity: update May;11(5):373-84. PubMed PMID: 20404851. Table 1: Table 2: Table other TIR domains probiotics possess TIR domain proteins which probiotics possess TIR domain proteins is structurally similar to TLR signaling. to modulate other TIR domains and thus has the potential studies will focus on investigating the interactions of LcTIR with Future human TLR signaling components. Keywords: with bacterial TIR domains (Table 1). Furthermore, LcTIR was modeled 1). Furthermore, with bacterial TIR domains (Table models were obtained high quality and I-TASSER and with RaptorX using performed alignment was criteria. Structural quality the on based LcTIR between the obtained RMSD values were evaluated and PyMOL model domains structures. LcTIR structural other known TIR model and (Table domains TIR human and bacterial to similar be to found was our hypothesis that 2). The results of bioinformatics analysis supported In this context, our hypothesis is that probiotic bacteria can also hypothesis is that In this context, our this, a gene region encoding proteins. Following produce TIR domain casei was found Lactobacillus TIR domain in the genome of probiotic LcTIR. as named was protein this hypothetical search and using BLAST other TIR between LcTIR and Results of multiple sequence alignment domain proteins showed the presence of conserved regions on LcTIR LcTIR has high sequence identity alignments showed that, Pairwise [5]. Toll-interleukin-1 in is structural domain which is found receptor (TIR) Toll-interleukin-1 domain TIR proteins. adaptor cytoplasmic (TLR) and receptors Toll-like dimerization in immune has a key role in TLR signaling pathway system known to have TIR domain proteins bacteria have been [1,2]. Recently, manipulate TLR signaling have been shown to pathogens and (BTP) via structural mimicry TIR to adaptor BTPs pathways using their can also probiotics evidence that out Research points domains [3,4]. the mechanism but has not been elucidated. manipulate TLR signaling, Bahar Bakar of Food Sciences,Department of Natural and Applied Graduate School 1. Ege University, İzmir,TR Engineering, T Food Engineering, İzmir, Department of Faculty of Engineering, 2. Ege University, REVEALING PROBIOTIC TIR DOMAINS USING DOMAINS TIR USING PROBIOTIC REVEALING TOOLS BIOINFORMATICS POSTER PRESENTATIONS 116 İzmir,TURKEY [email protected] E-mail: of Natural and of Applied FoodSciences,Department Engineering, Address: Author’s Corresponding No:116Z299). Acknowledgements: 8;4(2):e21. PubMedPMID:18266466. dependent ontheTIR-containing proteinBtp1. PLoS Pathog. 2008 Feb PierreP,RA, GorvelJP. Brucellacontrolofdendriticcellmaturationis Lapaque N,Muller A, Demaria O, Comerci DJ, Alexopoulou L, Ugalde [5] Salcedo SP,MI, Marchesini Lelouard H, Jolly G,Fugier E, Balor S, FEBS Lett. 2013Nov1;587(21):3412-6.PubMedPMID:24076024. domain oftheimmunosuppressiveBrucellaeffectorBtpA/Btp1/TcpB. Vergunst AC, Terradot StructureoftheToll/interleukin L. 1 receptor(TIR) [4] Kaplan-Türköz B, Koelblen T, Felix C,CandussoMP, O'CallaghanD, 21386–21392. PubMedPMID:19535337. structure ofa bacterial TIR domain. JBiolChem.2009 Aug 7; 284(32): JC, Woods Pascual VL, J.in innateimmunity:crystal Molecularmimicry Low LY,[3] ChanSL, HsuS, LiS, LiuT, Le NegrateG,E, Santelli Reed, hs ok s upre b TBTK 30 ( 3501 TÜBİTAK- by supported is work This

Ege University, Graduate School POSTER PRESENTATIONS 117 2 , Tunca Doğan , Tunca 1 vector with real values (angstrom). where the domain entry, InterPro/Pfam c. The information about categorical). mutation resides in (1-D, such as residues taking part in disulfide bonding, nucleotide binding, nucleotide binding, in disulfide bonding, such as residues taking part etc. (30-D,binary). zinc fingers, glycosylation, helix, repeats perfectly correspond to the cases where the mutation does not For b. distances (on the 3-D the sequence, the atomic sites on the annotated structure) between the mutated residue and the annotated residue on alpha-carbons)(based 30-D an additional in are incorporated a. Correspondence between the mutated site and different protein different protein site and a. Correspondence between the mutated database) sequence (obtained from the UniProt feature annotations ii. Mutated residue'sstructural information that we deduce from PDB ii. Mutated structures by aligning the protein sequence with the corresponding PDB structure’s sequence residues and by categorizing the mutated on their accessible based surface area as core, surface or interface a sequence variation may have a more significant consequence a sequence variation may have a more and we methodology, In the proposed should be evaluated accordingly. including: make use of a variety of descriptive features i. The studies aiming to predict the effect of sequence alterations in proteins predict the effect of sequence alterations The studies aiming to often exploit sequence information (mostly using alignment) and 3-D different perspective a took we this study, In information. structural using an evaluate these variations’ function altering capabilities and annotation-centric and other sequence focus (i.e., domains, motifs in approaches complementing conventional features), with the aim of where parts, proteins are the regions/sites of Functional the literature. ligand interactions, which is one of the key reasons that many of the which is one of the key reasons that ligand interactions, a we propose fail in clinicalearly drug candidates trials. In this study, to information related organize the collect and to new methodology the effects of sequence variations from various biological databases and to utilize in a machine-learningthis information based system to unknown with mutations function changing capabilities of the predict consequences. Whole-genome have indicated exome sequencing studies and protein effects on cause deleterious genomic variations may that single that has been reported mechanisms.functionality via various It thus, the protein sequence (and alter the that nucleotide variations are highly associated with structure and the function), namely nsSNPs, genetic diseases in human. These the receptor- variations also affect Fatma Cankara Technical Informatics, Middle East Graduate School of of Health Informatics, 1 Department Ankara, Turkey University, 06800 2. Institute University, 06800 Engineering, Hacettepe of Informatics / Department of Computer Ankara, Turkey Prediction of the Effects of Single AminoAcid Single of Effects the of Prediction with Functionality Protein on Variations Modeling Centric Annotation POSTER PRESENTATIONS 118 [email protected] EmailAddress: Corresponding Author’s UniProt, Clinvar, AnnotationCentricModeling, Personalized Medicine. Trees, RandomForest, PredictingCapacity OfMutations, Disease Keywords: based tool. approaches, to maximize thepredictionperformanceinan ensemble- methods,whichemploydifferent featurization with thestate-of-the-art Finally,them withthestate-of-the-art. weplanto combine ourmethod from previousstudiestocalculateourperformance,andcompare process ofrunningourmodelsonwidelyacceptedbenchmarkdatasets of eachvariation,onourpredictionmodels.Currently, weareinthe deleterious or neutral, by querying the finalized65-Dfeature vector as by categorizingthemeither of unknownvariations the effect Clinvar and PMD, a total of 108,072 mutations), and then, to predict from UniProt,models (usingthemutationswithknownconsequences first, totrainour tree (DT)andrandomforest(RF)classifiers, decision of thevariation(3-D,upon theoccurrence We real-values). employed scores: thechangeinpolarity, compositionandmolecularvolume, obtained fromthewidelyacceptedGranthamMatrix’s3distance (1-D,categorical).properties ofthemutation Physicochemical iii. SingleAminoAcidVariations, MachineLearning, Decision POSTER PRESENTATIONS 119 B97XD/6-311++G(d,p) B97XD/6-311++G(d,p) Ege University, Faculty of Faculty Ege University,

ω 2 [1] Sainsbury S, Bernecky C, Cramer P. Structural basis Bernecky C, Cramer P. [1] Sainsbury S, Nucleotide; Molecular Modelling; Density Functional Molecular Modelling; Density Functional Nucleotide; , Cenk Selcuki 1 Methods II. Applications. J. Comput. Methods II. Applications. J. Chem. 10, 221–26410.1002/jcc.540100209. (2016). http://openmopac.net/. MOPAC2016, P., J. [3] Stewart J. et al., (2009). https://gaussian.com/g09citation/ M.J. [4] Frisch, Corresponding Author’s Address: Email Science, Biochemistry Department [email protected] Keywords: Theory References: of transcription by RNA polymerase initiation II. Nat Rev Mol Cell Biol. 2015;16(3):129–143. PubMed PMID: 25693126 for Semiempirical of Parameters 1989,Optimization P. J. [2] Stewart J. structures were determined using the relative energies. Molecular structures were drawn by Discovery Studio Visualizer As a result, 2019. of the between the –OH group we found that the –H bond was formed In addition, of the nucleotide structure. ribose ring and -PO4 group structures tetranucleotide of (A=T) observed nucleobase conjugates we calculations the of Most non-covalentinteraction. as bond, CH−π and resources. ULAKBIM Truba were performed on TUBITAK- biological functions are known and to reveal electronic properties of analysis of the was used to program Spartan’14 these structures. The conformer mono, di, tri and tetramer structures of the structures of the PM6-D3H4 with [2,3] program MOPAC2016 sequence. The TATA-box at method and Gaussian09 program [4] each frequency calculations for and optimizations for level were used Using the calculated results, most stable the investigated conformer. In eukaryotic organisms, the flow of genetic information is regulated by by the flow of genetic information is regulated In eukaryotic organisms, the transfer ending with a functional of information from DNA to mRNA of a some proteins implicated in the transcription protein. In addition, gene binding to short DNA sequences by in region promoter in order [1]. to initiate RNA synthesis we aimed to generate 3D molecular modelling of the In this study, mono-tetramer)nucleotide (primarily structures whose specific Hasip Cirkin Biochemistry Program and Applied Science, Institute for Natural 1.Ege University, Biochemistry Department Faculty of Science, 2.Ege University, INVESTIGATION OF STRUCTURAL AND ELECTRONIC ELECTRONIC AND STRUCTURAL OF INVESTIGATION SMALL IMPORTANT BIOLOGICALLY OF PROPERTIES MOLECULAR MODELING BY NUCLEOTIDES POSTER PRESENTATIONS 120 2.Department ofComputerEngineering,MuğlaSıtkıKoçmanUniversity,Muğla,T 1. SchoolofEngineeringandNaturalScience,IstanbulMedipolUniversity,Istanbul,Turkey Asmaa Samy STUDY IN BREAST SF3B1CASE CANCER: CHARACTERIZING IMPACT OF SOMATIC MUTATIONS Keywords: characterization ofimpactfor other cancersomaticmutations. anticipate our in silico processcanbe improved and employed in the K700E mutation’sbreast cancer.in impactonmRNAsplicing We light onSF3B1 validations, sheds experimental Our work,thoughneed translation ofnon-functionalproteins. affected at a scalethat may leadto aberrant splicing ofmRNAand Furthermore, theSF3B1’s interactionwithmRNAisadversely mRNA. with SF3B1and components, particularlythoseindirectinteraction showed that K700E the stabilityofSF3Bcomplex’s mutation decreases native SF3B1 protein (PDB:5z56) [3] aswellthemutant. Our results structureofthe molecular dynamicssimulationsusingthecryo-EM for branchpointrecognitioninmRNAsplicing. We performedatomistic complex whichisresponsible the corecomponentofSF3Bspliceosome hotspot K700E1 (SF3B1)andits mutationforfurtherstudy. SF3B1is factor 3Bsubunit Splicing weselected and mutationfrequencies, metrics both of centrality and superimposedontothenetwork.Considering [1,2] from COSMIC(March2019release) are collected genes for these of 80genesusingSTRINGdatabase. The somaticmutationfrequencies splicing” and build a network use the biological process of “pre-mRNA gene. Tomutations fortheproteinencodedbythis we testtheprocess, the impactoffrequentsomatic dynamics simulationstoassess showing highmutation rates, andfinally, (5)performingmolecular to the network, (4) identifyingagenewithhighnetworkcentralityand somatic mutationfrequencies cancer gene network,(3)superimposing in cancer(e.g.involved harboringhighlymutant genes), (2)buildinga identification ofgenesparticipatinginabiologicalprocesspotentially of somaticmutationsinbreastcancer.(1) involves Theprocess In thiswork,wedefinedaninsilicoprocessto investigate theimpact cancer deservefurtherinvestigation. mutations is limited to their allelefrequenciesand their relations to breast cancer. Unfortunately, inmanycases,our knowledge onthese 73,000 database reports oversomaticmutationsin (COSMIC) Cancer as ofMarch2019, instance, theCatalogue of SomaticMutations in Forresources. made publiclyavailablethroughbioinformatics and Many somaticmutationshavebeenidentifiedinbreastcancer of suchfactorsthat affect breastcancerdevelopmentand prognosis. The somaticmutationsareone epigenetic aswelltheenvironmental. it iscausedbyacombinationofvariousfactorssuchasgenetic, since among women.Theetiology of thediseasehasremainedelusive is themostcommonandsecondlethaltypeofcancer Breast cancer 1 , BarisSuzek Breastcancer; SomaticMutations;mRNASplicing; SF3B1 2 , MehmetKemalOzdemir 1 andOzgeSensoy urkey 1 POSTER PRESENTATIONS 121 [email protected] [1] Tate, J. G., et al. (2019). "COSMIC: al. (2019). et Of the Catalogue G., J. [1] Tate, [2] COSMIC - Catalogue of Somatic Mutations in Cancer. Retrieved Retrieved in Cancer. of Somatic Mutations - Catalogue [2] COSMIC from https://cancer.sanger.ac.uk/cosmic X., human activated the of "Structure (2018). al. et Zhang, [3] 307-322. Res 28(3): states." Cell in three conformational spliceosome 29360106. PubMed PMID: Corresponding Author’s Address: References: Nucleic Acids Res D941-d947.47(D1): In Cancer." Mutations Somatic 30371878. PubMed PMID: POSTER PRESENTATIONS 122 Israel, E-mail:[email protected] 2. TheDepartmentofCommunityInformationSystems,ZefatAcademicCollege,Zefat,13206, Israel, E-mail:[email protected]). 1. TheDepartmentofInformationSystems,MaxSternYezreelValleyAcademicCollege, Loai Abdallah SPACE APPROACH BASED ON THEENSEMBLE CLUSTERING :ANOVELGRPCLASSIFIEREC CLASSIFICATION values. In future research, we propose several directions: (1) checking directions: we proposeseveral In futureresearch, values. we useonlythek-meansresearch, clustering algorithmwithdifferentk Our algorithmcanbeintegrated withmanyotheralgorithms.Inthis Conclusions the otheralgorithms. confirm that the suggested outperforms new algorithm GrpClassifierEC algorithms on several benchmark datasets. classification The results tree andRandomforest its resultstotheknearestneighbors,Decision In ordertoevaluateoursuggestedmethod,wecompare class. a single all these pointsas point. Ouralgorithmclassifies represented asasingle Different points that were includedinthesameclusterswillbe algorithms. of the pointsovermultiplerunsofclustering membership belong to the samecluster. TheECspaceisdefinedby tracking the objects werenot that these objects isdefinedasthenumberoftimes clustering (EC).Thesimilaritybetweentwo space basedonensemble given data that replacesthe space withcategorical GrpClassifierEC In thisstudy,method named wepropose a newclassification Results Euclidean distancemightberelativelylarge. though their traits even some common to thesameclusterusuallyshare actual relationship between those points. However, objects belonging does notcapturethe number ofattributes,theEuclideandistance where Euclidean distanceisdealing with data that is representedby a large of the between objects.Oneoftheobviousweaknesses similarity over agivendata the actual Distance metrics space shouldreflects clustering processsignificantly. performance and distance metriccouldimprovetheclassification to distance metrics determine similaritybetweendata points. Asuitable learning algorithms dependsconsiderablyon machine unsupervised Additionally,is alsoessential. or of manysupervised theperformance approach thatbasedonspacecapturethestructure classification structure and the hiddenpattern of thedata is required. Moreover, a new data sets, thereforea clustering approach that able to capture the actual Advances inmolecularbiology have resultedinbigandcomplicated Background Abstract 1 andMalikYousef 2 POSTER PRESENTATIONS 123 Decision Trees, Ensemble Clustering, Classification. Clustering, Ensemble Decision Trees, Corresponding author’s address : [email protected] Corresponding Author’s Address: [email protected] on the EC. and implementation Availability implementing GrpClassifierEC, available at is workflow, The KNIME http://malikyousef.com Keywords: the effect of the clustering algorithm to build an ensemble clustering clustering ensemble an build to algorithm clustering the of effectthe clustering poor (2) finding space. data, the training based on results similar combining by points based data the volume of the (3) reducing POSTER PRESENTATIONS 124 Sciences, MülheimanderRuhr,Germany 1. MedicalInformaticsandBioinformatics,HochschuleRuhrWest,UniversityofApplied Jens Allmer TOWARDS ANINTERNETOF SCIENCE 1 Of Things;CodeSmells;Scientific Computing Keywords: in dataanalysis,publication,andmedicaldecisionsupport (Fig. 1). that workflows can beconfidentlyused workflow developmentensures can buildoncorrecttools.Adding proper integration testingtothe computational workflows of versioned the collaborativedevelopment include reporting features leadingto interactive documents.Thereby, in publicandprivateclouds, allow fordevelopmentandanalysis overcome current duplications of effort, introduce proper testing, based ontheInternetof Things,will system, loosely The envisioned for thefieldofbioinformatics[4]. produce manypositivesideeffects the gap and this transitiontothecloud.Acommunityeffortcanfill appears that no currentworkflowmanagementsystemisready for occur.to the cloudwilllikely a shiftofdevelopmentandanalysis It However,on personal computers/workstations/clusters. in thefuture, as medicaldecisionsupport. Data analyses arepresentlyperformed and using themfordata pipelines analysis evenincriticalfieldssuch largely untestedtoolsintocomputational combining is bioinformatics and addressed.Thus,thecurrentstateofworkflowmanagementin actively developedthat potential errorshavealready been reported only alimitednumberofthetoolsareusedfrequentlyand since most efficientonefortheintendedusecase.Thisproblemisaggravated for a single researcherto ensure that all tools work correctlyto pick the points, itisnotfeasible a largeduplicationofeffort.Apartfromthese the availability of morethan 50 tools for a given purpose represents situation. Apartfromthisuncertainty,may workbestinagiven ones for read mapping are availableand it isnot easily discernablewhich data step.Foranalysis example,morethan50computational tools from theavailabilityofmanyoptionsforeach workflows stems resilient creating reproducibleand [1], Galaxy[2],andKNIME[3].Adifficultyin Tavernainclude bioinformatics used in systems workflow management of A fewexamples pipelines. of computationaltoolsintodata analysis is trueforworkflow management systems,whichallow the combining data analyses havebeendeveloped in thepast decades. Thesame computational tools. Many computational tools for bioinformatics of many Such workflowsmayconsist workflows (pipelines). analysis data big dataandcomplex in invested is heavily Bioinformatics Workflow Management;ComputationalInternet Pipelines;

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beschaeftigte/prof-dr-jens-allmer/. May 30. PubMed PMID: 31145694. Corresponding Author’s Address: University of Applied Hochschule Ruhr West, Jens Allmer, Dr. Prof. Mülheim an Sciences, Bioinformatics, 45479 Informatics and Medical https://www.hochschule-ruhr-west. [email protected], Germany, der Ruhr, de/forschung/forschung-in-den-instituten/institut naturwissenschaften/ al. The Galaxy platform for accessible, reproducible and collaborative Acids Res. 2018 Jul biomedical analyses: 2018 update. Nucleic 2;46(W1):W537–44. PMID: 29790989 al. et Meinl T, T, Kötter TR, Gabriel Dill F, Cebron N, MR, Berthold [3] editors. C, et al., Preisach In: Information Miner. KNIME:The Konstanz and Applications. Springer; 2008. Data Analysis, Machine Learning an Internet of Science. J Integr Bioinform. 2019 Towards [4] 1. Allmer J. and publication. All and publication. parts fit together such that figures and tables within raw data. a document can be traced to their supporting References: tool for building and running workflows of a et al. Taverna: MR, Li P, Server issue):W729-32. services. Nucleic Acids Res. 2006 Jul 1;34(Web Pubmed PMID: 16845108. Cech M, et van den Beek Bouvier D, M, B, Batut [2] Afgan E, Baker D, Figure 1. Medical Informatics and Bioinformatics, Hochschule Ruhr West, University of Applied Sciences, Mülheim Mülheim Sciences, Applied of University West, Ruhr Hochschule Bioinformatics, and Informatics Medical ] 1 4 suchthat figuresand tableswithin document a can be tracedto their supporting data. raw References: [1] workflows running accessible, reproducible and collaborative biomedical analyses: [3] C, Preisach In: Miner. 2;46(W1):W537 Figure 1. 1. Figure collaborative development of versioned computational workflowscan build correct on tools. Addingproper in used confidently be can workflows that ensures development workflow the to testing integration will overcome current duplications of effort, introduce proper testing, allow for development and analysis in in analysis and development for allow testing, proper introduce effort, of duplications current overcome will interacti to leading features reporting include and clouds, private and public Bioinformatics is heavily investedbig in dataand complex dataanalysis workflowsmay consist of computational many tools. Many computational toolsfor bioinformaticsdata which systems, management workflow for true is same The decades. past the in developed been have analyses allow the combining of analysis,publication

Taverna include bioinformatics in used systems management computing Keywords creating reproducible and resilient workflows stems from the availabilit the from stems workflows resilient and reproducible creating easily not is it and available are mapping read for tools computational 50 than more example, For step. mor of availability the uncertainty, this from Apart situation. given a in best work may ones which discernable not is it points, these from Apart effort. of duplication large a represents purpose given a for tools 50 than feasible a for researcher single to ensure that all toolswork correctly to pick the most efficient the for one problem This case. use intended are analyses Data support. decision medical as such fields in critical even analysis data for them using and of shift a future, the in However, computers/workstations/clusters. personal on performed presently develo systemis ready for this transitiontheto cloud. community A effortcan theand fill gap produce positive many side effectsthe for field bioinformatics of are actively developed that potential errors have already been reported and addressed. Thus, the current state state current the Thus, addressed. and reported been already have errors potential that developed actively are unt largely combining is bioinformatics in management workflow of west.de/forschung/forschung [ 31145694 Corresponding author’s address Jens Dr. Allmer, Prof. Bioinformatics, 45479 Mülheim an der Ruhr, Germany Ruhr, der an Mülheim 45479 Bioinformatics, [2] PMID: POSTER PRESENTATIONS 126 1.Department ofChemicalEngineering,IstanbulTechnical University,Istanbul,Turkey Zehra Sarıca Network Model Pathways inPyruvate Kinase Using Residue Predicting Potential Allosteric Communication Corresponding Author’s Address: Corresponding Author’s 1802-77 TurkishJournal ofBiology, 42(5): 392–404 (2018). doi:10.3906/biy- multiple statesofthebacterialribosomeusingresiduenetwork analysis. References: Network Model Keywords: high selectivityforpyruvatekinaseofS. aureus. drugs with attractive fornovelspecies-specific regions makethesesites sites. Thelow sequence conservationand critical location of these and betweentheactive andallosteric sites; at the monomerinterface sites on pyruvate kinase of S. aureus areproposed as newdrug target two different Here, employed bytwospecies. mechanisms allosteric Conclusion: of theenzyme. pathways have differentpatternsintenseandrelaxedconformations of theprotein.Inaddition,webspotentialallosteric functional sites that cantransmitaperturbation among and longpathwaysofresidues interfaces. Residueswithhighbetweennessmeasurespoint to short at the activesites,at known allosteric sitesas well as at the monomer Results: which canbeevaluatedasnewdrug between two target species, sites. that mark differences residues critical – areusedtoreveal betweenness the proteintopology.and – degree,closeness Centralitymeasures composed ofinter-connectedof trace theskeleton nodes, [1]which and threestructuresofS. aureusaremodeledasresidue-networks Method: conserved allostericsites. for thelowly is possible,especially drugs with highselectivity specific where thedesignofspecies- diseases, for thetreatmentofinfectious its activity. Thelatter property makes thisproteinanattractivetarget allostery tomodulate and uses sites structure accommodatesfouractive is thereforea key enzymeincellularmetabolism.Thehomo-tetrameric Background: 1 , Ozge Kurkcuoglu The model captures functionally important residues, located important residues, Themodelcapturesfunctionally Inthisstudy, twenty crystal structuresbelongingto H. sapiens Allostery;PyruvateKinase;CentralityMeasures;Residue [1] Kürkçüoğlu, Ö. Exploring allosteric communication in communication allosteric Exploring Ö. Kürkçüoğlu, [1] Results revealed the similarities and differences between and differences the similarities revealed Results Pyruvate kinase catalyzes the last step in glycolysis and catalyzes thelaststepinglycolysis Pyruvatekinase 1 [email protected] POSTER PRESENTATIONS 127 USA 2 [1] Bray N, Dubchak I, and Pachter L. Avid: A global Dubchak I, and Pachter [1] Bray N, Maximal Exact Matches; Universal Hitting Sets; K-Mer Universal Sets; K-Mer Matches; Hitting Maximal Exact [5] Li H. Aligning sequence reads, clone sequences and assembly [5] Li H. arXiv:1303.3997, 2013. contigs with BWA-MEM. L. Mavid: Constrained ancestral alignment of [6] Bray N and Pachter multiple sequences. Genome Research, 14(4):693–699, 2004. Phillippy A, S, Delcher AL,[7] Kurtz Antonescu Shumway M, Smoot M, and open software for comparing large C, and Salzberg SL. Versatile [2] Choi JH, Cho HG, and Kim S. Game: a simple and efficient whole and Kim S. Cho HG, [2] Choi JH, exact match filtering. genome alignment method using maximal 29(3):244–253,2005. Computational Biology and Chemistry, and Ohlebusch E. S, Efficient multiple genome [3] Hohl M, Kurtz 2002. alignment. Bioinformatics,18: S312–S320, read alignment based on maximal exact Long [4] Liu Y and Schmidt B. match seeds. Bioinformatics, 28(18): i318–i324, 2012. reference and decreases genomes reduces memory footprint and query problem. running time, mitigating the MEM enumeration Keywords: Sampling References: 2003. alignment program. Genome Research,13(1):97–102, that every sequence of length L contains at least one k-mer from the that every sequence of length L contains at least one k-mer specific a small UHS for a compute we [9], Usingexistingtools set. the reference index of universal k-mer a build L and value of k and sampling of the we show that universal k-mer genome. Consequently, uses less MEMs detect extend matches to find and genome to query space and processes queries faster than existing sampling methods. in the conclude that filtering and indexing only the universal k-mer We alignment [4, 5], and as anchor points in comparison of closely as anchor points in comparison of closely alignment [4, 5], and related genomes [6, 7]. the original MEMs, Despite recent advancements in computing both in resources and runtime, and problem remains challenging, present a novel cannot be effectively handled for large genomes. We of the sampling on MEMs based for detecting indexing method k-mer such from a universal hitting set [8], which is a set of k-mers k-mers an urgent need for faster and memory-efficientfaster and need for methods, algorithms, an urgent structures arises. and data In order to efficiently analyze manage and structures are data approaches and newer computational these data, essential. One widely-studied concept in sequence analysis is maximal strings two which are exact matches between exact matches (MEMs), in that cannot be extended either direction without a mismatch. MEMs long-read are used in whole-genome alignment [1–3], short-and As next-generation sequencing (NGS) becomes much cheaper and faster, sequencing (NGS) becomes much cheaper and faster, As next-generation , Zeynep Harcanoglu , Zeynep 1 Baris Ekim (MIT), Cambridge, MA, Institute of Technology 1. Massachusetts Turkey College, Istanbul, 2. TED Atakent FAST DETECTION OF MAXIMAL EXACT MATCHES MATCHES EXACT MAXIMAL OF DETECTION FAST SAMPLING K-MER UNIVERSAL WITH POSTER PRESENTATIONS 128 USA [email protected]://people.csail.mit.edu/ekim Address: Author’s Corresponding 2019. algorithms forapproximating compact universal hitting sets. bioRxiv, [9] EkimB, BergerB, Y.and Orenstein parallel Memory-efficient e1005777, 2017. high-throughput sequencing. PLoS Computational Biology, 13(10): k-merDesigning smalluniversal hittingsetsforimprovedanalysisof Y,[8] Orenstein Pellow D, MarcaisG, ShamirR,and Kingsford C. genomes. GenomeBiology, 5(2):R12,2004. 3 Ames Street, Cambridge, MA, 3AmesStreet,Cambridge,MA, POSTER PRESENTATIONS 129 Middle East Technical University, University, Middle East Technical 2 [1] Glass K, Huttenhower C, Quackenbush J, Yuan GC. Yuan [1] Glass K, Huttenhower C, Quackenbush J, Boric Acid, HepG2, Biological Network Modelling, Biological Network Modelling, Boric Acid, HepG2, , Yeşim Aydın Son , Yeşim Aydın 1 Corresponding Author’s Address: Graduate School of Informatics, Health Informatics Department interactions. PLoS One. 2013 May 31;8(5):e64832. doi: 10.1371/ doi: 31;8(5):e64832. May One. 2013 interactions. PLoS 23741402 journal.pone.0064832. PubMed PMID: A, Kedaigle Soltis AR, Gitter A, Fraenkel Gosline SJ, N, [2] Tuncbag Datasets of Diverse High-Throughput Interpretation E. Network-Based Biol. Comput PLoS Software Package. the Omics through Integrator doi: 10.1371/journal.pcbi.1004879. Apr 20;12(4):e1004879. 2016 PubMed PMID: 27096930 regulatory network and perform similar network modeling approach at lower concentrations of boric acid. Keywords: Regulatory Network, Microarray References: refine predicted biological networks to messages between Passing acid. To discover regulatory mechanisms lying beneath the expression acid. To interactions differentiating of regulatory up network made changes, a in boric and unexposed HepG2 cells acid exposed was obtained by Omics-of algorithm forest Consequently, [1]. algorithm using PANDA Integrator software[2] was used to find optimal subnetworks which facilitates the essentialunderstanding boric acid induced regulatory changes their visualization in the network and in a brief and concise prominent hubs of validate the most we plan to the future, In manner. Treating cell lines may help reduce toxic effects which with boric acid Treating higher hand, other On the proliferation. reinforce cell survival and might inhibit proliferation to have been reported boric acid concentrations of for many cell lines. In a gene expression performed with profiling study decrease in cell line, we have observed substantial HepG2 hepatoma the expression of genes involved in cell-cycle progression and many metabolic processes inhibitory concentration of boric at half-maximal Boric acid is well known for its concentration dependent effects for its concentration dependent Boric acid is well known on level. organism and cellular level the at processes both biological Department Health Informatics University, Graduate School of Informatics, 2. Middle East Technical Department CONCENTRATION THROUGH COMBINED USE OF OF USE COMBINED THROUGH CONCENTRATION ALGORITHMS MODELING NETWORK Ayegül Tombuloğlu Health Informatics University, Graduate School of Informatics, 1. Middle East Technical EXPLORING ALTERED REGULATORY TRANSCRIPTION TRANSCRIPTION REGULATORY ALTERED EXPLORING BY INDUCED INTERACTIONS FACTOR-GENE INHIBITORY HALF MAXIMAL ACID AT BORIC POSTER PRESENTATIONS 130 2. GenomeandStemCellCenter(GenKök),Kayseri,Turkey 1. ComputerEngineeringDepartment,ErciyesUniversity,Kayseri,Turkey Meryem AltınKARAGÖZ METAGENOMICS DATA CONVOLUTIONAL NEURALNETWORKS ON RAPID PATHOGEN DETECTION OF USING lengths ranging from500to10000, L= {500,1000,10000} lengths also read data used. CNNmodels weretrainedonrepresented withseveral of countsandOHEhave been of theoligonucleotide=k) co-occurrence representation method that has beengenerated with k-mer (thelength of two In orderto compare theperformancesCNNapproaches, spectral WGS pathogen database have beenused in our for classification study. and MinION (WGS) sequencing genome whole Illumina respectively pathogen data that was generated by different NGS platforms, performances consequently.classification From these perspectives, would be ableto exploit furthercorrelationsand yielding better hot-encoding (OHE)and running the CNN on the encoded sequences using k-mervia one- profiles,directly embeddingtheDNAsequences to k-merrestricted saturates quickly.which size, Wethat instead of claim response filters),the correlation exploitation capacity of CNNs are counterparts offiniteimpulse (or asnonlinear correlated sequences However, as CNNsarenonlinearfeatureextractorsfor short term ontop of thesefeatureextractors. cascading thetaxonomicclassifiers to ConvolutionalNeuralNetworks(CNN)featureextractors and feeding theoligonucleotidecount vectors basically literature is current recently [1]. The mainapproach in the proposed methodologies in the have beenreported results and successful network classifiers, neural- detection problemhasbeenalsoaddressedbythenewarchitecturesof problems, thecorresponding taxonomic spectrum ofdata classification use. With the of clinical success deep learning algorithmsina broad reads totaxahavebeenproposed,andmanyofthemareadopted for learning-basedof DNAsequencing methodsfortheclassification of origin.Toof eachsequencingreadto species date,machine several pathogen detection taskcanbestated as thetaxonomicclassification new paradigm, In this technologies. next generationsequencing using accuracy and diagnosewithhigh pathogenic species all search the otherhand,itispossibletodesigndetectionsystemsthat can small biodiversity range and cannot correspond to generic use.On such asimmunologicaltests,PCRpanels,andbiosensorstarget a detection methods. Novel methods that can performfast detection is required forconventional in somecases, late timeofintervention antimicrobial treatmentcorrectly. Around 72 hours, whichleadsto a isthereforeinneed,orderto guide diagnosisand an infection Rapid identification of pathogens that are the causativeagents of progresses rapidlyinhourstowards terminal outcome. as thedisease yet usually ineffective, the actionhasto be taken in a short timewindow broad spectrum antibioticsare which in as sepsis such scenarios The targeted treatment of infectious diseasesiscrucialinseveral 1 , ÖzkanUfukNALBANTOĞLU 1,2 POSTER PRESENTATIONS 131

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deep learning algorithms in a broad spectrum of data classification problems, the the problems, classification data of spectrum broad in a algorithms learning deep Paglia, L., La Rosa, M., Renda, G., Rizzo, R., Gaglio, S., & Urso, A. (2018). Deep Deep (2018). A. Urso, & S., Gaglio, R., Rizzo, G., Renda, M., Rosa, La L., Paglia, Computer Engineering Department, Erciyes University, Kayseri, Turkey Kayseri, University, Erciyes Department, Engineering Computer [1] Fiannaca, A., La Paglia, L., Rosa, M., Renda, La A., Paglia, [1] Fiannaca, La based 1 - Read Length 500 bp 1000 bp 10000 bp 500 bp 1000 bp 10000 bp Pathogen Detection, Dna Sequence Dna Sequence Detection, Analysis, Metagenomics, Pathogen xtractors.as are nonlinear CNNsHowever, feature extractors short for term correlated reatment of infectious diseases is crucial in several several in crucial is diseases infectious of reatment t essesrapidly inhours towardsterminal outcome. Rapididentification pathogens of that are the Meryem Altın KARAGÖZ

Comparison of pathogen detection accuracy for OHE (CNN- detection Comparison of pathogen pathogen detection, DNA sequence analysis, metagenomics, machine learning machine metagenomics, analysis, sequence DNA detection, pathogen

ces consequently. From these perspectives, pathogen data that was generated by different NGS NGS different by generated was that data pathogen perspectives, these From consequently. ces Comparison of pathogen detection accuracy for OHE ( OHE for accuracy detection pathogen of Comparison The algorithms for each dataset dataset each for algorithms The learning

1 engths ranging from 500 to 10000, L= {500, 1000, 10000} also with distinct k distinct with also 10000} 1000, {500, L= 10000, to 500 from ranging engths e Rapid Pathogen Detection of Using Fiannaca, A., La La A., Fiannaca, Convolutional Neural Networkson

llumina llumina References: [1] 198. 19(7), bioinformatics, BMC data. metagenomic of classification taxonomic bacteria for models learning Corresponding author’saddress MinION MinION I genuslevel. The resultingdetection accuraciescan be foundin cases several in representation OHE with rates accuracy enabling steps extraction feature intermediate without sequences nucleotide the from systems. Keywords: Tabl methods. Dataset thesefeature e exploitation correlation the filters), response impulse finite of counterparts nonlinear as (or sequences k to restricted are CNNs of capacity one via sequences DNA the embedding directly profiles, further exploit to able be would sequences encoded performan database pathogen WGS MinION and (WGS) sequencing genome whole Illumina respectively platforms, compare to order In study. our in classification for used been have k with generated been has that method representation spectral occurrencecounts of and OHEhave been used.CNN modelswere trained on represented data with several read l PCR panels, and biosensorstarget a small biodiversity range and cannot correspond to generic u other hand, itispossible to designdetection task systemsthat detection can searchpathogen all pathogenic paradigm, new species and this diagnose In with technologies. sequencing generation next using accuracy high taxon the as stated be can machine and of them many are adopted for clinicaluse. With the success of neural of architectures new the by addressed also been has problem detection taxonomic corresponding network classifiers, and successfulresults have to been vectors reported count recently[ oligonucleotide the feeding basically is literature current the in methodologies proposed Convolutional NeuralNetworks (CNN) feature extractors and cascading thetaxonomic classifiers topon of The targeted the as window time short a in taken be to has action the yet ineffective, usually are antibiotics spectrum disease progr causative agentsinfection of an is therefore inneed, order in to guidediagnosis and antimicrobial treatment late a to leads which hours, 72 Around correctly. conventional detection methods. Novel

for bacteria taxonomic classification of metagenomic BMC data. bioinformatics, 19(7), 198. Corresponding Author’s Address: ([email protected]) Table 1: Table (CNN-cor) based methods. The algorithms for each k-mer ohe) and validation. dataset have been tested with 10k-fold References: & Urso, A. Rizzo, R., Gaglio, S., (2018). Deeplearning models G., CNN is capable of learning directly from the nucleotide sequences sequences directly from the nucleotide of learning CNN is capable without intermediate steps enabling-end-to-end feature extraction detection systems. Keywords: Machine Learning with distinct k-mer size (3≤k≤7) at genus level. The resulting detection detection resulting The level. genus at (3≤k≤7) size k-mer distinct with has achieved CNN model 1. better accuracies in Table be found can result, a in several cases. As OHE representation with accuracy rates POSTER PRESENTATIONS 132 3.AYA R&DBiotechnologyInc,İstanbul 2. DepartmentofBiomedicalEngineering, BiruniUniversity, İstanbul 1. Mete EmirÖzgürses Evren Atak DISCOVER THENEWPOTENTIALS AS BIOMARKER FUNCTIONAL GENEENRICHMENT STUDYTO PERFORMINGHEAD-AND-NECK CANCER: Department ofMolecularBiology&Genetics,BiruniUniversity, İstanbul 1 , AhmetMelihÖten development of biomarkers for HNC that basically follows cell cycle, follows cell for HNCthatbasically development of biomarkers demonstrates usthatZNF324B [4]. This gene couldbeacandidateof about the strong associationbetweentheselisted pathways and HNC are found intheliterature Experimental andcomputational evidences reticulum. in endoplasmic pathwaysignaling andproteinprocessing pathways regulated by ZNF324B p53 gene arerevealedascellcycle, that is runby significantly expressedgenes.Asa result, 3 different GSE83519 to perform thepath analysis withRpackage ofpathfindR Lastly, HNCRNAseqdata was takenfromGEOdataset with number gene names. Pathways playingaroleinHNCarehighlightedwiththeindicationof interactionnetwork has beencreatedviaSTRING.genes, gene-gene list of are displayed.Withthefinalized and regulatedgenes miRNAs by the setsofmiRNAsarelistedandour gene pool was expanded. The moved onthesecondpart such that the correspondinggenesregulated FRMD5, PCMT1, PDGFA, TMC8,YIPF4 Then, we and ZNF324B genes. the miRwalk,wefirstlylistednamesof miRNA that are regulated by are FRMD5,PCMT1, PDGFA, TMC8, YIPF4 and ZNF324B [3]. Through having playedaroleinHNCthat 6 genes literature, wehaverevealed As already and demonstrated metabolic processes. in the mechanism role forthedevelopmentandstagingofHNCintermsfunction, that playacrucial genes expressed HNC toidentifythesignificantly in In thisstudy,gene enrichment aim toperformfunctional webasically in thefieldofgenomicsandproteomics. asthemostpopular biomarker isseen approach with highestspecificity of HNC[2].Amongmany,and furtherdevelopmentof thediscovery and specificity,accuracy with highest the subtypes clarifying especially are requiredfortheearlydiagnosisofHNC diagnostic approaches thenovel Even CTandPETareusedtodetectstageHNC; treatment ofHNC. used fortheeffective agents arecommonly novel treatment, radiation therapy, chemotherapy, targeted therapeutics and of head and neck cancer.and the sub-type Followed by the surgical methods canbefolloweddependingthecombinationofriskfactors factor.and genetics chemoprevention Manydifferenttreatment factors are;smoking,risk alcohol,occupationaltoxics,HPV-infection, There aremanyriskfactorsaffectingheadand neck cancer. These hypopharynx, oropharynx, lip, oralcavity, nasopharynx,orlarynx[1]. disease, existinginthedifferentlocationsof head and neck including; defined asacomplex with highmortalityintheworld..It is cancers Head and neck cancer(HNC)isthe one of the most widespread 1 ,Öykü İrigülSönmez 2 , ŞeymaElbeyoğlu 3 , AslıYenenler 1 , 2 POSTER PRESENTATIONS 133 Biomedical Engineering, Faculty Faculty Biomedical Engineering, [1]: Lo Nigro C, Denaro N, Merlotti A,Merlotti M. Merlano Denaro N, C, Nigro Lo [1]: Gene-Gene Head And Molecular Diagnostic, Interaction, PubMed Central PMCID: PMC6213883. Corresponding Author’s Address: of Engineering and Natural [email protected] Sciences, Biruni University, İstanbul - PubMed Central PMCID: PMC2490629. PubMed Central PMCID: signature model for Q. A six-mRNA Chen X, Zhu L, Wang [3]: Guo W, cell carcinoma. Oncotarget. neck squamous and head the prognosis of 10.18632/oncotarget.21786. doi: Oct 10;8(55):94528-94538. 2017 PMCID: PMC5706893. PubMed PMID: 29212247; PubMed Central Control Quality Protein (2018). Y. Jeon, and H. Han, H., Moon, [4]: International Journal of in the Endoplasmic Reticulum and Cancer. Molecular Sciences, ; PubMed PMID: 30282948 19(10), p.3020. Head and neck cancer: improving outcomes with a multidisciplinary multidisciplinary a with neck cancer: improving outcomes and Head 10.2147/ doi: 18;9:363-371. Aug Res. 2017 Cancer Manag approach. Central PubMed PMCID: PMID: 28860859; PubMed CMAR.S115761. PMC5571817. molecular diagnostic on Update CH. Chung Slebos RJ, KT, Palka [2]: Semin Jun;35(3):198-210. Oncol. 2008 tests in head and neck cancer. PubMed PMID: 18544435; doi: 10.1053/j.seminoncol.2008.03.002. with high specificity. We believe will further demonstration that our be We with high specificity. diagnosis and early kit for of new biomarker introduction used for the of HNC. subtyping Keywords: Discovery of Biomarkers Neck Cancer, References: p53 signaling and protein processing pathways. As a further, we want want we further, a As pathways. processing protein and signaling p53 of course of HNC, subtyping gene for role of ZNF324B the to elucidate genes significantly of other with the association in HNC expressed POSTER PRESENTATIONS 134 3. AYAR&DBiotechnologyInc,İstanbul 2. DepartmentofBiomedicalEngineering,BiruniUniversity,İstanbul 1. DepartmentofMolecularBiology&Genetics,BiruniUniversity,İstanbul EmirÖZGÜRSES Şeyma ELBEYOĞLU VIA FUNCTIONAL GENE-ENRICHMENT APPROACH BIOMARKERS INTHEBLADDER CANCER AN ATTEMPT TO THEPOTENTIAL IDENTIFY platform. Then, identifiedmiRNAs again was put on miRWalk tofind from literature [1] and found associated miRNAsthrough miRWalk in bladdercancer,that proventobehavefunctionalactivities 22 genes approach to expand gene pool for bladder cancer. First, we identified to betargeted. For thispurpose,weused functionalgeneenrichments insight fordevelopmentof biomarkers and important pathways have interactions that has had a potential to provide an point of gene-gene from the behind BC,especially us toelucidatethemechanism helps data In general,mRNAexpression was usedto subtype the BC[2]and to showthese9gene’ssharedmiRNAsbeusedforfurther studies. we couldn’tones, Weany connections. identify constructedascheme genes out within 3differentclusters.Forof 33 in connections remaining are only9 There level. with others, inmediumconfidence relationship interaction networkscould be created in the STRING to reveal their Then, gene-gene that countedatleast4times. the ones pool, weselect coming from miRWalk.miRNAs Amongmanygenesinourextended gene pool, we have also mined the associated geneswith the list of through miRWalkstep. Prior inthe second of our tothefinalization [1] inthefirststep,andthenidentifyrelatedmiRNAs with textmining BC asidentificationof 22 genes that proven to be significantinBC we perform functionalgeneenrichmentto expand the gene pool for determination ofBC.Foragent fortheeffective specific thispurpose, identify thebiomarkersfor early diagnosisof BC, and also to reveal the in thefieldofcancer,to otherresearches As similar ouraimisto correlations andetc. and miRNA expressiondata, clinical DNA methylation,PTMs levels, as somaticmutations,mRNA such to becombinedwell aspects have quite unknown.Toremains basis ofBC,several elucidatethemolecular onhealthfield,molecularbasisofbladder and cancer improvements the best chance for treatment of MIBC. Despite the new treatments cystectomy, tri-modalitytherapywithneoadjuvantchemotherapyoffers radical preferred intreatmentofNMIBC; chemotherapy is intravesical and immunotherapy conservation with intravesical BCG vaccine or or not. For example,theresectionoftumour followed by induction are developed for BC depended on whether being muscle invasive (NMIBC). Uptonow,bladder cancer treatment methods several bladder muscle invasive cancer (MIBC) and invasive non-muscle for prognosis: implications different very that have two “tracks” along and itdevelops disease as complex world. BCconsidered Bladder cancer (BC)isone of the most common cancertypes in the 1 , ÖyküİRİGÜLSÖNMEZ 1 , AhmetMelihÖTEN2,EvrenATAK 3 , AslıYENENLER 1 , Mete 2 POSTER PRESENTATIONS 135 Biomedical Engineering, Faculty Faculty Biomedical Engineering, [1]: Pichler, R., Fritz, J., Tulchiner, G., Klinglmair, G., G., Klinglmair, G., Tulchiner, J., R., Fritz, [1]: Pichler, Bladder Cancer, Gene Enrichment, MiRNA-Gene Cancer, Bladder [email protected] the Molecular Subtypes of Bladder Cancer: Illustration in theCancer the Molecular Subtypes of Bladder Cancer: doi: Sep;72(3):354-365. Urol. 2017 Eur Dataset. Genome Atlas PMID: PubMed 30. Mar Epub2017 10.1016/j.eururo.2017.03.010. 28365159; PubMed Central PMCID: PMC5764190. Corresponding Author’s Address: of Engineering and Natural Sciences, Biruni University, İstanbul - Relationship, Pathfindr References: accuracy of (2017),“Increased al. et W. Soleiman, A.,Horninger, & a novel urine mRNA-based test for bladder cancer surveillance”, BJU 28941000 International,121(1), 29-37. PubMed PMID: M, Kim WY, Aine M, Höglund Ochoa A, McConkey DJ, [2]: Choi W, Genetic in Alterations Real FX, Kiltie AE, Milsom I,Dyrskjøt L, SP. Lerner Among more than 110 pathways, we selected pathways with Fold we selected 110 pathways, Among more than with Fold pathways and downregulated Enrichment more than 75 and then upregulated believe that our study is pioneer displayed. We genes of each pathway template for any for BC and a good find new potential biomarkers to gene-miRNA knowledge about will provide new other studies that role in BC related to their expression levels. interaction and their Keywords: in BC, we have put final gene list in Reactome. Among KEGG and signalling carcinogenesis, Viral PI3K-Akt in cancer, Pathways many, Hepatitis B and Human papillomavirus infection pathway, pathway, MAPK signalling and Reactome most in both KEGG pathways were hit CBL,TP53, MAPK1, CREB1, CDK2, with genes. MAP3K9 SP1and IGF2, expression Next, we took RNA code and BC with GSE13507 data of p-valuesprioritized them according running pathfindR. just before their related genes. With the unification of all these, our final gene final these, our all of the unification With genes. related their pool our gene 4 times in at least counted and genes was created, pool pathways the related identify To for further processes. were selected POSTER PRESENTATIONS 136 1SD Hinxton,Cambridge,UK 4.European MolecularBiologyLaboratory,EuropeanBioinformaticsInstitute(EMBLEBI),CB10 3.Institute ofInformatics/HacettepeUniversity,06800Ankara,Turkey Turkey 2. DepartmentofComputerEngineering,MiddleEastTechnical University,06800Ankara, Technical University,06800Ankara,Turkey 1. CancerSystemsBiologyLaboratory(CanSyL),GraduateSchoolofInformatics,MiddleEast Çetin-Atalay Heval Ataş BINDING PREDICTION AFFINITY MODELLING FOR COMPOUND-TARGET DEEP ANDSHALLOW CHEMOGENOMIC 1 , AhmetRifaioğlu Forests PairwiseChemogenomics; Input Deep NeuralNetworks;Random Keywords: computational predictionofcompound-protein interactions. approach forthe of chemogenomic demonstrates theusefulness The considerablyhighperformance ofour models inthis challenge final round,ourRMSEperformance was1.066 (4th bestteam,overall). model hasreached an RMSE valueof 1.119 (5th best team). In round2/ model performanceresultsonchallengeround1, our best performing between 25 compounds and 207 kinases (round2). Considering the (round1) and394pKd values between 70compoundsand199kinases models. Thechallengeisbased on the predictionof 430 pKd values www.synapse.org/#!Synapse:syn15667962/ wiki/583305) with our Drug-Kinase BindingPredictionIDG-DREAM Challenge(https:// (Tablewas carriedoutviacross-validation 1).We participated the PerformancepChEMBL values. calculationandparameteroptimization predicts bindingaffinityfortheinput compound-target pair intermsof 1.b). For both approaches, output is a single node (a regressor), which further processedontwo additional hidden feed-forwardlayers(Figure representation ofcompoundandtarget features areconcatenatedand nodes simultaneously,layers, latent followingtwohiddenprocessing a pair offeaturevectorsforcompoundsand targets from disjointinput (Figure 1.a). For approach2, we used PINN architecture, whichtakes takes aconcatenatedfeaturevector(compound+target)asinput based proteindescriptor. For approach1, we usedRFalgorithm,which proteins withk-separated-bigram-PSSM feature vectors asa homology- compounds. Weand compounds withECFP4fingerprints represented binding of affinities a large setof kinases against several drug candidate learningtechniques,topredictthe supervised (random forests-RFs-) andshallow input deepneuralnetworks-PINNs-) using deep(pairwise based computationalmethods, we developedtwochemogenomics study,ligands fortargetswithlimitedornotrainingdata. In this novel and targetspaceinonemodel,sothattheycanbeusedtopredict popular,approaches became modelling both compound utilize which candidate compoundsandtarget proteins. Recently, chemogenomic between drug and repurposingforthepredictionofinteractions discovery Machine learningtechniquesarefrequentlyusedinthefieldof drug 1 , VolkanAtalay Binding Affinity Prediction; BindingAffinity Learning; Machine 1,2* 2 , Tunca Doğan , 1,3,* 4, MariaMartin 4 , Rengül

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POSTER PRESENTATIONS 138 Bornova, 1,2. MolecularBiologySection,DepartmentofBiology,FacultyScience,EgeUniversity, Cansu Pirim ASTHMA IN INTRON 7OF PYHIN1GENEASSOCIATED WITH BIOINFORMATIC ANALYSIS OF POLYMORPHISMS M, Sienra-Monge JJ,M, Sienra-Monge Heinzmann B, del Rio-Navarro KA, Deichmann I, Ford JG,S, Huntsman ChungKF, Vora CalhounWJ, H, LiX, Castro Dunston GM,WatsonMantese VJ, HR, Ruczinski EzurumSC,LiangL, W, DietteGB, AdkinsonNF, RossKD, Abel RA, ShiM, Faruque MU, JR, Chapela R, Rodriguez-Cintron Rodriquez-Santana Avila PC, Avila L, DJ, Hu D, Hansel NN, Hernandez RD, Salam MT, Israel E, Galanter J, M,Togias RomieuI,VanSoto-Quiros MyersRA, Li X, Berg Den A, CeledónJC,RafaelsN,C, SternDA, Capurso D, Conti DV, Roth LA, Hancock DB, Levin Baurley JW, AM, Himes BE, Mathias RA, PE, Eng DG, AmplefordEJ, ChiuGY, GaudermanWJ, GignouxCR,Graves asthma/asthmadata.htm. Aug 16, Accessed 2019. [4] Torgerson data. surveillance Updated March 2019. https://www.cdc.gov/ Control and Prevention[3] CentersforDisease (CDC).Asthma Sep;126(3):453-65. PubMedPMID:20816181 epigenetics of asthma: an update. J AllergyClinImmunol.2010 ginasthma.org.Aug 16,2019. [2] HoSM.Environmental Accessed Updated 2018.http://www.for asthmamanagementandprevention. References: Prediction, ComputationalMethods,Bioinformatics Keywords: be associatedwithasthma,targetedbythesemiRNAswereestimated. algorithm andfinally,Naive BayesClassification thegenesthought to sequences viatheMatureBayes program that using the from pre-miRNA sequence. Potential have beenidentified mature miRNA sequences structure andthermodynamicstabilityhavegotten from humanmiRNA with appropriate hairpin secondary precursor miRNAsequences sites; to theconserved were identified.Withreference sequences Order Primatescontaining predictedmiRNA regions and conserved were examinedby bioinformatics toolsin11ofthe different species detected intheintron7regionofPYHIN1 polymorphisms gene, In thisstudy;containingtwo different singlenucleotide sequences only[4]. of Africandescent individuals at PYHIN1genein identified were studies showedthat asthma-related singlenucleotidepolymorphisms association groups[3]. Genome-wide than otherethnic prevalence asthma is evaluated, it is known that African individualshavea higher of factors aswellgenetic[1,2].Incasetheprevalence triggered be mediated by by environmental epigenetic mechanisms to that are believed interactions obstruction duetogene-environment Asthma isachronicinflammatorydiseasecharacterizedby airway 1 , CemalÜn Asthma,PYHIN1, MicroRNA Detection,Target Gene [1] GlobalInitiativeforAsthma(GINA).strategy 2 POSTER PRESENTATIONS 139 KC; Genetic Research on Asthma in African Diaspora (GRAAD) Study, Genetic Research Study, on Asthma in African Diaspora (GRAAD) KC; Nicolae DL.Ober C, genome-wideMeta-analysis of association ethnically diverse American populations. studies of asthma in North 31;43(9):887-92. PubMed PMID: 21804549 Nat Genet. 2011 Jul Corresponding Author’s Address: of Science Faculty ÜN Ege University, Cemal Dr. Prof. HARBORS study, Burchard EG; Burchard Americans Genetics in Latino of Asthma study, HARBORS Genes-Environment of Study Latino in Admixture and (GALA) Study, Americans Americans, of African (GALA2) and Study Genes Asthma, & Asthma ResearchChildhood and FD; Martinez Environments (SAGE), Asthma Management Childhood ST; Weiss (CARE) Network, Education (CAMP), Williams LK;Program of Asthma Phenotypes and Study Interactions by Race-EthnicityPharmacogenomic Barnes (SAPPHIRE), A, Wenzel SE, Busse WW, Gern JE, Lemanske RF Jr, Beaty TH, Bleecker Bleecker TH, Beaty JE, Gern RF Jr, Lemanske SE, Busse WW, A, Wenzel Asthma Childhood City Mexico BA, Raby ER, SJ; Meyers DA, London Gilliland Study (MCAAS), FD; Children's (CHS) Health Study and POSTER PRESENTATIONS 140 2.Ege UniversityFacultyofScienceBiochemistryDepartment 1.Ege UniversityInstituteforGraduateStudiesinScienceandEngineering Seda GEZER INTERACTIONS BETWEEN ACIDS AMINO MODELLINGMOLECULAR OF HYDROPHOBIC [email protected] Ege University, Faculty ofScience,BiochemistryDepartment Address: Corresponding Author’s Modeling; Valine. Keywords: density ofthebonds. a graph was created showing theinteractionsbetweenthemand wasselected.Aftertheenergycalculationswerecompleted, (d, p)level Forstructures, modeling ofthese molecular most stable structures of each amino acid we selected wereselected. water.In orderto see theinteractionofaminoacidswitheachother, the was selected.Energycalculatingdoneto be invacuumand level amino acidsweselectedforthisthesis, modeling ofthe molecular amino acids.Inordertomake of these stability us tounderstandthebasicpropertiesandrelative acids helps amino with theconformationofthese non-polar aminoacids.Dealing and are essential amino acids These and isoleucine. leucine valine, in ourbodies.Theaminoacidsselectedthisstudy are; of thecells biological phenomenaduetohydrophobicinteractionforthesurvival of proteinwithwater.In addition to protein folding, therearemany stable andbiologicallyactive.Becauseitreducesunwantedinteractions for thefoldingofproteins.Thisisimportant for theproteinto remain do not interactwithwater. Hydrophobicinteractionsareimportant and of longcarbonchains consist generally Hydrophobic molecules 1 , CenkSELÇUKİ Hydrophobic Interaction; Isoleucine; Leucine; HydrophobicInteraction;Isoleucine; Molecular 2 ω B97xD /6-311 ++ G(d,p) ω B97xD /6-311++G POSTER PRESENTATIONS 141 , 2 , Gizem SOMUNCUOGLU 2 2 [1] Laksshman Sundaram, Hong Gao, Samskruthi Reddy [1] Laksshman Missense Mutation, Deep Neural Network , Nur Sena ULUSKAN , Nur Sena 1,2 , Ahmet ULUDAG Nat Genet. 2018 Aug; 50(8): 1161–1170. PubMed PMID: 30038395 Nat Genet. 2018 Aug; 50(8): 1161–1170. Corresponding Author’s Address: [email protected] References: Li, Jack A. McRae, Yanjun Jeremy F. Padigepati, Anindita Dutta, John Jörg Hakenberg, Nondas Fritzilas, Kosmicki, Shon, Jinbo Xu, Serafim Batzoglou, the clinical human impact of Predicting Farh. Kai-How Xiaolin Li, Kyle mutation with deep neural networks. The program, which effectively we used in filtering during the variants patients in 30 5 diagnosis of the whole exome analysis (WES), provided determine to a different approach patients, remaining 25 For easily. should also is needed. It the variations have fewer allel frequency is it mutations, functional studies of no since there are that noted be together with their clinic recommended that patients be evaluated Keywords: Researchers at Illumina has developed an algorithm that provide Researchers that Illumina has developed an algorithm at predicting the clinical with deep neural mutation impact of human uses networks[1]. Algortihm lots of features such as six non-human primate species variations and protein structure to train common with pathogenic mutation neural networks. Algorithm enables identify in interpret detected mutation to have used program We 88% accuracy. diagnosis. 30 patient that rare genetic disease preliminary It is difficult to interpret the variations have fewer allel frequency. When the variations have fewer allel frequency. interpret is difficult to It detected, general mutation databases not defined in a mutation, of prevalence, type some features e.g. by is usually evaluated it silico algorithm classification, etc. mutation,result of in 2 2. Next Genetic Centre, Department of Molecular Genetic, Istanbul, Turkey Department of Molecular Genetic, Istanbul, 2. Next Genetic Centre, Oguzhan KALYON Oguzhan EmreTEPELI of Molecular Biology Faculty of Arts and Sciences, Department University, 1. Yildiz Technical Turkey and Genetics, Istanbul, MISSENSE MUTATION EVALUATION WITH DEEP DEEP WITH EVALUATION MUTATION MISSENSE NETWORK NEURAL POSTER PRESENTATIONS 142 2. MiddleEastTechnical University,3.HacettepeUniversity 1. KaradenizTechnical University, Serbulent Unsal TRAINABLE PROTEIN REPRESENTATIONS OPPORTUNITIES ANDPROBLEMS RELATED TO NOVEL driven representations is crucial for anycomputationalproteinanalysis. crucial is representations driven exploring trainable,data- complexity ofbiologicalentities.Hence, to predictingthepropertiesofproteins,due it comes when more important compared to the machine learningtechniqueused, significantly is itself task.. Thetestsshowedthattherepresentation for anycomputational crucial is such asvectors, entities, as numerical • Importanceofrepresentations: Key pointsinferred andexemplifiedinthisstudycanbesummarizedas: new directionsforproteinrepresentationconstruction. and propose our results and wediscussed objectives, aspects andtheir Wethese representationmodelsaccordingto their technical classified literature. in recent in differentpredictivetasksrelatedtoproteinscience improvements overthestate-of-the-art all ofwhichreportedsignificant inspired bytheNLPfieldandmethodsfromneuralnetworkencoding, representation approachessuchasword2vec/doc2vec-basedmethods we mainlyconsiderednoveltrainableproteinandmoleculardata Forontological termswithlownumberoftraininginstances. this, for theprediction of for protein-function-prediction,especially study,In this weaimtoinvestigatethepotentialoftrainableembeddings domain. getting popularityinthelife-sciences on thepatternsfound in thedata. Lately, are trainablerepresentations and generatedbased hand, aredataand/ortaskspecific, on theother Trainablethe naturalproperties ofthesemolecules. representations, rulesdesignedby human experts,mostlyinspiredfrom on pre-defined and trainable.Fixedfixed representations: are based representations by a efficiently machine learningclassifier. Therearetwo main types of these representations,inherentfeaturesofproteinscanbelearnt representation ofproteins.Using properties isgeneratingaholistic of thekeypointsforaccuratelypredictingproteinfeatures/ One perturbation is introduced tothesystem(e.g., the removaloraddition when a small in theresulting vectors the dramaticvalue changes should havesimilar vectors.Aprobleminprotein representationsis (e.g.entities (i.e.k-mers) king-man+woman~=queen) andsimilar representations, thevectorarithmetic should give consistent results • Representationproblem: stability for. should allow the reuse for tasks differentthan the one it was created latentfeatures.Moreover,to enlighten experts a representation such should begeneratedinsuchawaythat it canbeinterpretedbydomain A trainable proteinrepresentation interpretability ofmodels. is learning problem: • Interpretability 1,2 , AybarC.Acar 2 , Tunca Dogan One of the key problems in machine in problems of the key One Representingcomplexstructures 3 In stablesemantic POSTER PRESENTATIONS 143 Although the number Although In most studies, proposed proposed In most studies, Tunca Dogan, tuncadogan@ Dogan, Tunca Protein Representation; Deep Learning; Learning Learning Deep Learning; Representation; Protein gmail.com, Health Informatics Program, Institute of Informatics, gmail.com, Health Informatics Program, Turkey 06800 Ankara, Hacettepe University, of alternative protein representations of alternative protein rapidly in the is growing independent benchmark literature, there is no their evaluation. A for study. benchmarking pipeline is proposed in this Keywords: Representations Corresponding Author’s Address: trainable protein representations trainable protein representations evaluated via ablation are being with very studies and by comparing similar representations. However, showed that classical a few number of studies representations are that successful can be more TF-IDF) (e.g., be baseline models accepted to a this, representations. Because of novel trainable to compared baseline methods, is required. standardization, regarding • Benchmarking of representations: of a few sequences to/from the whole protein dataset) or with small with or dataset) protein whole the to/from sequences few a of there Currently, embedding process. value changes in the parameter benchmarks/tests of limited number for assessing are a the stability on are actively working We representations. available protein/gene of for this problem. potential solutions • Baseline comparison problem: POSTER PRESENTATIONS 144 3. ErciyesUniversity,FacultyofEngineering,ComputerEngineeringDepartment,Kayseri 2. ErciyesUniversity,GenomeandStemCellCenter(GenKök),Kayseri Deparment, Kayseri 1. ErciyesUniversity,GraduateSchoolofHealthSciences,BioinfomaticsSystemsBiology Samed SAKA RRNA TAXONOMIC CLASSIFICATION TAXONOMIC METRIC LEARNING FOR 16S 1,2 networks was adopted as the taxonomic embedding network.Fornetworks wasadopted asthetaxonomic a neural network architecture.Afterthetraining, oneofthetwin metricsofeachgiven pairwastrainedusingSiamese the similarity via satisfying space explicitly gene tothis of each vectors frequency space wasdefined. A neuralnetworkembeddingthepentanucleotide to a taxonomicallymeaningfulmetric 16S rRNAsequenceimplicitly sharing thesamegenus.Therefore, theagrammatrixlocatingeach the onesinsamedomain but different phyla, up to the ones based ontheirtaxonomicsimilarity,similarity from incrementing of attained ascore constructed asfollows.Eachpairofsamples problem. Taxonomicon thetrainingdata matrices were similarity relatives the unexploredclose order tosimulate from thetrainingin we havecreatedtrainingandtestdatasets excluding theclosetaxa hierachially. Forfrom genustoclass, clade level eachhierachial to 694 genera, 199 113 orders, 95and families, classes, 35 phyla dataset contained 13880 rRNA sequences.The samples belonging At first,wecreatedadataset from SILVAis database [2]which for 16s underrepresented phylogenyproblem. remote relativesbecomesstaightforward, proposing a solution to the or to close the classification rRNA genesintothisspaceisachieved, least attaxonomicalquanta)embedding16S meaningful. Once (or at are phylogenetically distances metric feature space,where are embeddedtoa 16S rRNAsequences problem where learning of taxa remote relativesofunexploredranks,wepropose an auxiliary order toaddresstheproblem,andbeingable accurately detect taxonomic databases. In is mostlyunexploredwithincomplete as prokaryoticbiodiversity can causedefectedmicrobiotaanalyses the finaltaxonomiccompositionsignificantly. Thisproblemfrequently relatives areoftenplacedto taxonomically unrelatedunits,distorting the database, remote are containedin of atestsample relatives close Although thisapproachcanresultinaccuratedetectionwhile are usuallyadoptednature oftheproblems. thanks totheclose ork-merson bag-of-words, classifiers specifically) forDNAsequences processing (e.g.[1] orRandomForest NaïveBayesianClassifiers microbiota sample. 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robi [1] Q. Wang, G. M. Garrity, J. M. Tiedje, and J. R. Cole, M. Tiedje, and J. J. M. Garrity, G. [1] Q. Wang, c Although this approach can result in accurate detection while close relatives of a test sample are are of a test sample close relatives while detection in accurate can result approach this Although

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Erciyes words, or k or words, Classification performances accuracy of the proposed and 3 : Classification accuracy performances of the proposed and the reference methods. reference the and the of proposed performances accuracy Classification - learning problem where on the training data data training the on University, Graduate School of Health Sciences, Bioinfomatic Sciences, of Health School Graduate University,

we rRNA Taxonomic Classification of - ces from the training

Chaudhary, Nikhil, et al. "16S classifier: a tool for fast and accurate taxonomic classification of 16S 16S of classification taxonomic accurate and fast for tool a classifier: "16S al. et Nikhil, Chaudhary,

] Random Proposed uxiliary Taxonomic Metric Learning For 16S 3 Erciyes 1 [ rRNA hypervariable regionsin Corresponding author’s address [email protected] Keywords References: of assignment rapid for classifier Bayesian “Naïve Cole, R. J. and Tiedje, M. J. M. Garrity, G. Wang, Q. [1] rRNA sequencesinto the new bacterial taxonomy,” Appl. Environ. Microbiol., 2007. ribosomal SILVA “The al., et Quast C. [2] 2013. Res., Acids Nucleic tools,” based 1. Table taxon same the of samples the removing by conducted is level clade each for experiments sample. test corresponding a for set training accuracies were compared to the to the state compared were accuracies algorithm based classifier a Forest Random experimental same the with profiles pentanucleotide current the outperforms is significantly methodology embedding taxonomic proposed that suggesting convention on microbial structures informed biologically learning methodologies based learning deep that claims results The tasks. prediction phylogenetic or taxonomic on inference better in result of microbiot a taxonomically meani pair given each of metrics similarity the satisfying via explicitly space this to gene each of vectors frequency ne Siamese using trained was asthe taxonomicembedding network. taxonom the to assigned was net) neural embedding the of should neighbor nearest the before, seen/trained been has not class taxonomic the if same even that expecting beataxonomic relativein a higher clade. levels clade different for experiments Independent solutionto At first, hierachially. phyla 35 and 95 classes, orders, 113 199 families, genera, to 694 belonging samples 13880 to class, genus from level clade hierachial each taxa matri similarity taxonomic their on based theones sharing the same genus. the problems. to placed often are relatives remote database, the in contained significantly. composition taxonomic final databases. taxonomic incomplete with unexplored is mostly biodiversity prokaryotic an propose we ranks, unexplored of taxa of relatives remote detect accurately to able being and problem, a are phylogenetical staightforward, becomes relatives remote or close to classification the achieved, is space this studies, microbiome In identifying regions. variable and classified taxonomically, consequently revealingthe biodiversity profileaof microbiota sample. classifiers Forest Random or [1] Classifiers Bayesian Naïve (e.g. processing language natural in used methods on bag

based tools,” Nucleic Acids Res., 2013. accurate for fast and Nikhil, et al. "16S classifier: tool a [3] Chaudhary, taxonomic classification of 16S rRNA hypervariable regions in metagenomic datasets." PloS one 10.2 (2015): e0116106. Corresponding Author’s Address: [email protected] Stem Cell Center (GENKÖK), Kayseri Networks References: of “Naïve Bayesian classifier for rapid assignment Environ. Appl. the new bacterial taxonomy,” rRNA sequencesinto Microbiol., 2007. project: ribosomal RNA gene database [2] C. Quast et al., “The SILVA Improved data processing and web- Table 1. Table the reference methods. Independent experiments for each clade level same taxonomic class the removing the samples of by is conducted test sample. from the training set for a corresponding Keywords: taxonomic or phylogenetic prediction tasks. Moreover, in practice the taxonomic or phylogenetic prediction tasks. Moreover, performance of microbiota analysis following tools can bee boosted the proposed framework. profiles with the same experimental train/test sets). Table 1 shows 1 Table sets). same experimental train/test the profiles with taxonomic proposed that suggesting results the of summary the outperforms the current is significantly embedding methodology taxonomic classification. convention on microbial The results claims that deep learning based methodologies learning inference structures might result in better biologically informed on same taxonomic class has not been seen/trained before, the nearest before, the nearest been seen/trained class has not same taxonomic clade. relative in a higher be a taxonomic neighbor should experiments Independent for different clade levels from genus classification the and were performed, class were accuracies to the state-of-the-art to compared conventional 16S rRNA classification classifier algorithm [3] based Forest Random is a which methodology, using the same on pentanucleotide features and conditions (i.e. run test sample, the corresponding represantation vector (i.e. vector (i.e. represantation corresponding the test sample, output the neural embedding of the net) was assigned of label to the taxonomic the even if that in this space, expecting training neighbor the nearest POSTER PRESENTATIONS 146 Natural Sciences,SabancıUniversity,Istanbul34956Turkey 1. MolecularBiology,GeneticsandBioengineeringProgram,FacultyofEngineering Aylin Bircan RECEPTORS PHYLOGENETIC ANALYSIS OF CALCIUM-SENSING 1 , OgünAdebali [email protected]. Tuzla,Sciences, Istanbul,34956 Turkey Tel.: 216-568-7043; E-mail: be addressed: Sabancı University, Faculty of Engineering and Natural Address: Author’s Corresponding 31189130. PubMed PMID: Epub 2019/06/13. doi:10.1530/JME-19-0104. receptor mutations. J MolEndocrinol. 2019. calcium-sensing from studiesof insights C. Molecular andclinical [3] Gorvin 30104700. 2018/08/15. doi: 10.1038/s41580-018-0049-3. PubMed PMID: coupled receptors. Nat Rev MolCellBiol.2018;19(10):638-53. Epub PM. ofsignallingandbiasedagonisminG protein- Mechanisms [2] Wootten D, M, Babu MM, Sexton Marti-Solano Christopoulos A, 27339034. 2016/06/25. doi: 10.1016/j.tem.2016.05.005. PubMed PMID: TrendsCalcilytics. Metab. 2016;27(9):643-52. Endocrinol Epub Sensing ReceptorMutations: Prospects forFuture Treatment with References: Casr Mutations Keywords: the receptorfunctionduringevolution. each aminoacid to understand how changes insequencemodulate of evolution historyofCaSRto reveal theevolutionarysignificance diseases isnot fully understood. Here weaimto establish theprecise acid substitution at a certain positionon the receptorfunctionand receptor activationandfunction[3],however,amino of each theeffect residues ondifferentdomainsofCaSRareimportantfor that specific mutationsshow hypocalcemia [3].Studiesonthesedisease-causing and autosomal familial hypocalciurichypercalcaemia dominant such as CaSRmutationscausedifferentdiseases gain-of-function and loss- germline anddifferent level the extracellularcalcium in changes smallest pathwaysdetects even signaling [2].CaSRmainly intracellular to activatedifferent G proteins with different and couples three extracellular loops [1]. CaSR functions by forming a homodimer domain, seven transmembranedomain with threeintracellularand domain calledVenusextracellular Flytraprich domain withacystine composed ofalargebilobed which is receptors protein-coupled C G receptor (CaSR)isamemberofclass The calcium-sensing Calcium-Sensing Receptor; G Protein-Coupled Receptor; Receptors; G Protein-Coupled Calcium-Sensing [1]MayrB,C. ActivatingCalcium- Glaudo M,Schofl 1 To whom correspondencemay POSTER PRESENTATIONS 147 1 , Kemal TURHAN 2 , Zerrin IŞIK 2 Drug Repositioning; Network Biology; Computational Computational Drug Repositioning; Network Biology; Approach Corresponding Author’s Address: University Department of Biostatistics Technical Ülkü ÜNSAL Karadeniz and Health Informatics [email protected] content representing hypotheses including protein-protein different -drug/ drug -disease/ drug interaction networks, drug-target / drug- disease-diseaserelationships or side effect relationships have been studies in repositioning drug network-based this study, established In in this field opportunities problems and cancer area are reviewed. Key further studies. are summarized to guide researchers for Keywords: completed in a shorter time (average 8 years), less completed in a shorter costly (mean for are three main approaches There less risky. billion) and $1.6 biological experiment and mixed repositioning: computational, drug methods is approaches. One of the computational the network-based proteins two whichuses physical relationship between approach and functional similarities between genes to discover a new usage network approach, network-based In known drug/compound. for a Drug developing is a complicated, time-consumingDrug developing is (approximately per drug) billion high-priced $12 (average years per drug), 13 are designed for drugs these reasons, risky investment.a For and be used in treatment eventually can and binding multiple targets of different diseases. In view of these findings, drug computational using bioinformatics by proposed were repositioning approaches development can be drug repositioning, drug analysis tools. Using , Ali CÜVİTOĞLU 1 Ülkü ÜNSAL and Health Informatics Department of Biostatistics University, Technical 1. Karadeniz of Computer Engineering University, Department 2. Dokuz Eylul REVIEW OF DRUG REPOSITIONING WITH NETWORK NETWORK WITH REPOSITIONING DRUG REVIEW OF IN CANCER ANALYSIS POSTER PRESENTATIONS 148 3Erciyes University,FacultyofEngineering,ComputerEngineeringDepartment,Kayseri 2.Erciyes University,GenomeandStemCellCenter(GenKök),Kayseri Deparment, Kayseri 1.Erciyes University,GraduateSchoolofHealthSciences,BioinfomaticsSystemsBiology Kübra YALÇIN VARIATIONS ASSOCATED WITHLIVERCIRRHOSIS COLONIZED INHUMAN GENETIC GUTREVEALS GWAS ON PARVULA VEILLONELLA STAINS the trained model. In our conclusion, findings suggest that genetic algorithm. Atotal of68SNPs on after featureselection wereselected value wasobtainedonthetestdata using Xgboostcurve of ROC features onthemodelcreated.Approximately 0.82 selecting AUC algorithm overSNPprofiles. Important SNPs weredeterminedby wasperformedbyusinggradientboosted trees learning Machine healthy controlcohort. V.phenotype onthecirrhosis- by disease parvulastrainsare clustered in 1,985,732 SNPs weredetected. The SNPdistancematrixcanbeseen quality high,lowSNPs werefilteredandasaresult,total of Topair resolution. base at asingle sequence reference the SNP keep utility provides asummaryof the coverageof mapped reads on a against V. parvula. Then vcffilescreated with samtools mpileup which Aligner) -MEM,these288metagenomeswerealigned Wheeler and a total of 288 samples wereanalyzed. Using BWA (Burrows- 136 healthy controls cirrhosis from theanalysisand152liver liver Firstexcluded were low qualityand abundancesamples ofall, RAST tool wasusedforanotation oftheVeillonellaparvula genome. number CP019721.1)The accession wasusedasthereference. patients and145 healthycontrols.V. parvulastrainUTDB1-3(NCBI There werea total of 314 samples, including169 liver cirrhosis Firstly,cirrhosis. liver metagenomedata1 were obtained.cirrhosis liver biomarker of gut thatishypothesizedtobeasignificant commensal Veillonellaparvula wasperformed.V. parvulaisacommonhuman microbiome, SingleNucleotidePolimorphismon a (SNP) analysis cirrhosis performed forliver previously analyses taxonomic existing and functional changes.Therefore,inthisstudy, inaddition to the should bestudiedalongwiththetaxonomic of microorganisms full spectrum of variations the to reveal are nolongersufficient that onlytaxonomicanalyses Yet,organisms. abundance ofcertain show accumulatingevidence these alterationsmayappear as taxonomicdiversityand relative Recent studies1,2haveshownthat complexdiseases. of several is altered, either aspathogenesisorcomplicationfactors,incase decade, we have cometo the knowledge that the gut microbiome During thelast interactions. metabolic andsignalling commensal host-in crucial involving actively of genes millions harboring several species, of thousandsmicrobial consists Human gutmicrobiome

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Chen low quality and low abundance samples abundance low and quality low Qin, Qin, Corresponding Author’s Address: Stem Cell Center (GENKÖK), Kayseri [email protected] Kübra Yalçın PMID: 25079328 metagenome-wideA (2012). et al. of association study [2] Qin, J., 10.1038/ 55-60. 490. Nature. 2 diabetes. in type microbiota gut nature11450. PMID: 23023125 patients diabetic 2 type metagenomes of Gut al. (2017). et [3] Chen Y., have characteristic singlenucleotide polymorphism distribution in 5. 10.1186/s40168-017- Bacteroides Coprocola. Microbiome. 0232-3. Figure 1 subjects and subjects with cirrhosis. References: 10.1038/nature13568. microbiome in liver liver cirrhosis. Nature. Keywords: variations of V. parvula in parvula is human gut significantly with associated variations V. of liver cirrhosis raising applications potential of non-invasive diagnosis for the liver disease. drug targets or novel theuropathic

] GWAS on 2 Erciyes University, Graduate School of Health Sciences, Bioinfomatics Systems Systems Bioinfomatics Sciences, of Health School Graduate University, Erciyes Erciyes 1 Kübra Yalçın [email protected] Yalçın Kübra Corresponding author’s address ErciyesUniversity, Genome and StemCell Center (GENKÖK), Kayseri [ 490. 55 [3] 10.1186/s40168 5. Microbiome. Coprocola. in Bacteroides distribution polymorphism References: [1] 10.1038/nature13568. 25079328 PMID: 1. Figure

theuropathic Keywords: were determined byselecting feature algorithm. Xgboost using data test the on obtained was model. trained the on non of applications potential raising cirrhosis liver with associated significantly SNPs were detected. the that implies using by performed was learning Machine these pair base single a at sequence reference a on reads mapped of coverage the of summary a provides utility resolution was used as was all, analyze were samples 288 of a total and controls healthy microbiome parvula liver Firstly, cirrhosis analy variations of microorganisms shouldbe studied along with the in this study,addition in to the existingtaxonomic analys come tothe knowledge that themicrobiome gut is altered, either as diseases. complex several of case diversity and relative abundance Human microbiome gut consists of thousandsof microbial species, involving in crucialhost 3

POSTER PRESENTATIONS 150 4. DepartmentofComputerEngineering,HacettepeUniversity,Ankara,Turkey, Turkey, 3.CanSyL, GraduateSchoolofInformatics,MiddleEastTechnical University,Ankara,06800, European BioinformaticsInstitute(EMBL-EBI),Hinxton,Cambridge,CB101SD,UK, 1. DepartmentofComputerEngineering,MiddleEastTechnical University,Ankara,Turkey, 2. Rengül Çetin-Atalay Gökhan Özsarı HUMAN PROTEINS SUBCELLULAR LOCALIZATION PREDICTION OF AMULTI-VIEWCANSLPRED: METHOD TO developed by considering thesubcellular localizationproblem as lysosome (LYS), and models are peroxisome (PEX). The classification (EXC),endoplasmic reticulum (ERE),secreted Golgiapparatus (GLG), membrane (MEM),mitochondrion(MIT), (NUC), cell (CYT), nucleus locations: cytoplasm subcellular provides predictionsforoneof nine model each where models independently constructedclassification applied attheend.CanSLPredto GOCCtermsis of nine consists CC termsinGOhierarchy. ThemappingofUniProtKB SLidentifiers is then extracted byconsidering‘is_a’hierarchy relationsamongGO UniProtKB SL identifiersarefirstmapped to GO CCterms. and GOCChierarchy.hierarchy To generatethenewSLhierarchy, of formed to both UniProtKBunite thecharacteristics hierarchies: SL is location hierarchy (SVM) approach. Thenewsubcellular Machine by employing a weighted mean voting multi-viewSupport Vector is describedto predict thesubcellularlocalizationofhumanproteins This dataset iscalledTrustmethod dataset.Third,anewclassification to propagate the proteinsaccordingto their subcellularlocalization. annotated in UniProtKB/SwissProt and applying the new SL hierarchy taking theproteinswhosesubcellularlocalizationisexperimentally hierarchy. Second, a dataset of protein sequences isgenerated by Component (CC) Ontology (GO)Cellular andGene (SL) hierarchy ProteinUniversal (UniProtKB) KnowledgeBase Location Subcellular First,is introducedbymerging location hierarchy a new subcellular protein sequence.Therearethreemajorcontributionsinthisstudy: probabilisticpredictionsforeach (protein descriptors)and seven prediction modelsthat provide sevendistinctrepresentations probabilistic view approach,feature-based we employseven for humanproteins. Intheproposedmulti- predictions localization method view classification (CanSLPred) that provides subcellular there isstillroomforbetter performance. Here,weintroducea multi- the lasttwodecades;however,localization areproposedin subcellular computational several methods forautomated prediction ofprotein Therefore, and time-consuming. are expensive methods which proteins canexperimentallybe identified by purification or imaging biology,of localization The subcellular andproteomicsresearch. understanding the functionsof proteins, drug targeting, systems the subcellularlocalizationofproteinsiscrucialfor Determining 1 , AhmetSüreyyaRifaioğlu 3 , VolkanAtalay 1 1 , Tunca Doğan 2,4 , POSTER PRESENTATIONS 151 [email protected] [1] Salvatore M, Warholm P, Shu N, Basile W, Elofsson A. Basile W, Shu N, P, [1] Salvatore M, Warholm Subcellular Localization, Prediction, Human Proteins, Human Proteins, Prediction, Subcellular Localization, Corresponding Author’s Address: overall MCC scores are 43%, 53%, and 56%. overall MCC scores are 43%, 53%, and Keywords: SVM, Multi-View References: for improved human subcellular new ensemble method a SubCons: 2017;33(16):2464–70. localization predictions. Bioinformatics. version of Golden dataset where the steps we follow to generate Trust version dataset where of Golden the steps we follow to generate Trust We for the protein sequences are applied dataset. in Golden dataset with five state-of-the-artcompare the results of CanSLPred methods: [1] and DeepLoc. [1], MultiLoc2 [1], CELLO2.5 SubCons [1], LocTree2 outperforms the others with 59%, 68%, 61% overall CanSLPred dataset, Matthews correlation coefficient (MCC) scores on Trust-Test Golden dataset, respectively dataset, where SubCons’ Golden-Trust dataset), and Golden-Trust dataset (a refined version of Golden dataset and Golden-Trust dataset), dataset is by applying the new SL created hierarchy dataset). Trust on the proteins whose subcellular localization is experimentally consists of protein Golden dataset in UniProtKB/SwissProt. annotated sequences subcellular whose localization is experimentally annotated of three protein resources: out mass spectrometry least two in at refinedis a dataset Golden-Trust (Mass-Spec),UniProtKB. and SLHPA, used to construct probabilistic prediction models, which produces models, which produces construct probabilistic prediction used to for a query indicating the localization probability probabilistic scores A weighted score protein sequence. is calculated based on the scores obtained probabilistic from seven feature-based probabilistic Binary prediction models (SVMs) mean voting. by employing weighted applying thresholding prediction is given by the weighted score. on dataset Trust-Test by using three datasets: evaluate CanSLPred We (our in-house (SubCons’ benchmarkdataset), Golden dataset normalization, normalization, prediction models, by probabilistic weighted-mean process, In the feature extraction seven thresholding. and voting, protein descriptors are selected cases out of 160 from (40 descriptors methods: and 4 normalization POSSUM, SPMap iFeature, three tools: Power Robust scaler, MinMax normalization, normalization, Standard transformation), where these seven protein descriptors contribute the of probabilistic best in the combination SVM is prediction models. a binary classificationa binary decides the models each of where problem or location subcellular corresponding the to localizes protein if a and extraction Feature given in four steps: The prediction is not. POSTER PRESENTATIONS 152 Yeditepe University,DepartmentofGeneticsandBioengineering Özgür Yüksel, ANALYSISTHEORETICAL OF BACTERIAL SIGNALING HONESTY INCOMMUNICATION –GAME [4] J. Evolutionary GameTheory(Stanford Encyclopedia McKenzie A. PMCID: PMC4890463. Epub 2015 Oct 21. PubMed PMID: 26503040; PubMed Central Nature. 2015Nov5;527(7576):59-63. doi:10.1038/nature15709. communication inbacterialcommunities. enable electrical channels LiuJ,[3] PrindleA, M, Ly Asally J, S, Garcia-Ojalvo GM. Ion Süel Cellular Microbiology. 2009;11(7):1034-1043. Stoodley P. L, [2] Hall-Stoodley Evolving conceptsinbiofilminfections. PMC4862617. 2015 Jul 22. PubMed PMID: 26200335; PubMed Central PMCID: 2015 Jul 30;523(7562):550-4. doi: 10.1038/nature14660. Epub Nature. within biofilms. oscillations to collective gives rise dependence M, LeeM, Asally DY, LyJ, S, Garcia-Ojalvo GM. Metabolicco- Süel References: Signaling Game,CellularAutomata Keywords: configurations ofeachcell-type. distant cells. The strategies prevaildepending on the initialspatial signal and maintenance ofmetaboliccoordination among electrical neighbors [6]. The model demonstrates spatial propagation of 2-D latticespacewherean individual cell only interacts with immediate cellular and molecular processesareexecutedviacellularautomata in [5]. The and areceiver where thegameisplayedbetweenasender in Lewiscommunication phenomenonissimplified signalingterms dependent fitnessofstrategieswiththeEGT approach. The complex biofilm based electrical communicationand to calculate frequency The aimofthisstudy is to analyze cooperation and competition in stability ofastrategyandspecifiesdynamicsforthepopulation[4]. manner, EvolutionaryGame Theory (EGT) analyzesevolutionary mustbetakenintoconsideration.Inanexplicit dynamic environments identify competitivebehaviors,individual’sstrategy and its stability in studiesreadilyrevealcooperation. whereas experimental In order to competitive behaviorsareoftendifficultto observe communities, signalswithinthebiofilm[3].Inmicroscopic range electrical through long-and coordinatemetabolism bacteria communicate factors [2].Particularly,nutrient utilizationand resistance, virulence behavior and regulate the expressionof surface molecules,antibiotic Trough communication biofilmcommunitycoordinate cell-cell embedded in extracellularpolymericsubstances(EPS)calledbiofilms. scale, bacteriaformcooperativecommunities [1]. Inmicroscopic resources individuals competeforlimited of thepopulation whereas fitness the overall Cooperative behavioroftenincreases interactions. of social scale every in role Cooperation andcompetitionplaycrucial EmrahNikerel Electrical Signals, Evolutionary GameTheory,Signals, Electrical Lewis [1] Liu J, Humphries J, Prindle A, Gabalda-Sagarra POSTER PRESENTATIONS 153 Corresponding Author’s Address: 26 of Genetics Department and Bioengineering, University, Yeditepe Ağustos Yerleşimi, Kayışdağı Cad, 34755 Ataşehir, İstanbul e-mail: [email protected] [5] Huttegger S, Skyrms B, Smead R, Zollman K. Smead Evolutionary Skyrms B, S, [5] Huttegger systems vs. partial games: signaling signaling of Lewis dynamics 2009;172(1):177-191. Synthese. pooling. Reviews mechanics Statistical of of cellular automata. S. [6] Wolfram 1983;55(3):601-644. Modern Physics. of Philosophy) [Internet].Plato.stanford.edu. 2019 [cited 2019 Aug Aug 2019 [cited 2019 [Internet].Plato.stanford.edu. Philosophy) of https://plato.stanford.edu/entries/game- from: 18]. Available evolutionary/ POSTER PRESENTATIONS 154 İstanbul. 2 DepartmentofMolecularBiology,GeneticsandBioengineering,SabancıUniversity, 1 DepartmentofBioengineering,GebzeTechnical University,Kocaeli. Dilara Uzuner CANCER INTERACTIONS OF METASTASIS MECHANISMS IN AND CELLULAR NETWORKS IDENTIFIES MOLECULAR INTEGRATIVE ANALYSIS OF TRANSCRIPTOME DATA Gene-Regulatory Networks, TranscriptomeGene-Regulatory Keywords: 216S489) supported through financially a grant by TUBITAK (Project Code: study was This in humanandmice. cells cancer of pre-metastatic reveal functionalsubnetworks and elucidate thesurvivalmechanisms regulatorynetworksto and gene- protein-protein interactionnetworks available intheliteraturewereintegratedwith data of cancersamples that have aroleinmetastasis.Inthisstudy,interactions transcriptome of metastasis by identifying crucialgenesand proteins and their the mechanisms study isusingthesecomputational toolstoreveal changed betweenthecompared conditions subnetworks that are enrichedwithgenesthat are significantly interactionnetworkstoidentify transcriptome data on genome-wide [2,4] reported to have better performance than other tools in theliterature network-based analysistools KeyPathwayMiner and BioNet have been networks. Twointeraction mapping transcriptomedataonmolecular by unknown molecularmechanisms ones to discover most promising process transcriptomicdata, network-based analysesareamong the Although there aremultiplecomputational cells. approaches to behind theformationof pre-metastatic elucidate themechanism Transcriptomics approach to isa commonly used genome-wide is stillunknown formation ofthispremetastaticniche.However, theexactmechanism hypoxia andcontentofextracellularmatrixareimportantforthe factors ofthetargetsuch as tissues show thatmicroenvironmental cancertreatment.Recent studies of metastasisiscrucialforefficient primary tumor. Therefore,understanding the underlyingmechanisms treatment, cancer canrelapseinpatientsyearsafter the removalof the chemotherapy. cancer cellshave resistanceto Because pre-metastatic therefore non-proliferativemetastatic cancer cellscanescapefrom adapt to that tissue. Traditional chemotherapies killproliferativecells, get cancerous at a new tissue,theystop proliferation for a to while incancertherapy.a major challenge Beforemetastaticcancercells oncology,advances havebeenmade in clinical metastasisremains Although significant tissues. to different cells the spreadingofcancer The causeofmostthecancer-related deathsismetastasis,which Cancer isoneofthecommoncausesdeath in the21st century. 1 , PınarPir . Using differentalgorithms, KeyPathwayMiner and BioNet map Cancer, Metastasis, Protein-Protein Interaction Networks, 1 , DevrimGözüaçık [1] . 2 , Tunahan Çakır [2,3] . The aimof this 1 POSTER PRESENTATIONS 155 Tunahan CAKIR, Assoc. Prof. CAKIR, Assoc. Prof. Tunahan 1. Sleeman JP. The metastatic niche niche The metastatic stromal and Sleeman 1. JP. Corresponding Author’s Address: University Kocaeli, Department of Bioengineering Gebze Technical TURKEY [email protected] Bioinformatics. 2010;26(8):1129-30. Epub 2010/03/02. doi: 2010/03/02. Epub Bioinformatics. 2010;26(8):1129-30. PubMed PMID: 20189939. 10.1093/bioinformatics/btq089. Hellmuth M, Ditzel HJ, J, Gitzhofer K, Pauling N, 4. Batra R, Alcaraz of de novo pathway List M. On the performance Baumbach J, doi: 2017/06/27. Epub enrichment. 2017;3:6. NPJ Syst Biol Appl. PMCID: PubMed PMID: 28649433; 10.1038/s41540-017-0007-2. PMC5445589. 22699312; PMCID: PMC3470821. 22699312; Pauling Krohmer A,J, Muller T, Kotzing T, Friedrich N, 2. Alcaraz Efficient key mining: pathway combining networks J. Baumbach J, Epub 2012;4(7):756-64. Biol (Camb). Integr data. OMICS and 22353882. PMID: PubMed 10.1039/c2ib00133k. doi: 2012/02/23. BioNet: Dittrich MT. Muller T, Dandekar T, Klau GW, 3. Beisser D, networks. biological analysis of the functional for R-Package an References: Epub 2012;31(3-4):429-40. Rev. Metastasis Cancer progression. PMID: PubMed 10.1007/s10555-012-9373-9. doi: 2012/06/16. POSTER PRESENTATIONS 156 4. ErciyesUniversity,FacultyofMedicine,DepartmentMedicalMicrobiology,Kayseri 3. ErciyesUniversity,FacultyofEngineering,ComputerEngineeringDepartment,Kayseri 2. ErciyesUniversity,GenomeandStemCellCenter(GenKök),Kayseri Deparment, Kayseri 1. ErciyesUniversity,GraduateSchoolofHealthSciences,BioinfomaticsSystemsBiology Özkan UfukNALBANTOĞLU Ayşenur SOYTÜRK IN HUMAN GUTMICROBIOME IN SILICO DETECTION OF PUTATIVE MIRNA human- generated groups showed similar structuresto human miRNA human miRNA in differentnumbersandlocations. Similarly, 5healthy werefound microbial species to to similar contain miRNAsequences As aresult,inthegroup patients, 1141 of 5cirrhosis different method. were determinedusingKraken2 microbialtaxonomicassignment harboring that valuewerelocatedand the speciesloci the threshold contigs exceeding the secondarystructureofmiRNAs. Themicrobial sizeof70-110 nucleotides, whichgoverns considering thepre-miRNA Screening was performed on 70 nucleotide runningwindows scans, 5healthypeople and 5 were considered. people with cirrhosis In these value. threshold to thedetermined according screened were liver disease.Asa subset of these data, 10 human metagenomes associated withcirrhosis,whichiscommonworldwide as a terminal obtained from EBI Metagenomics -EMBL-EBI from human feces human genomecontamination)data were after filtering assembled detector.yet highlyspecific Wholegenomesequencing(shotgun, of detection(0.98) was determinedfora moderately sensitive, method is showninFigure 1. Based on this characteristics,a threshold best accuracyvalue.Theprecisionrecallgraph for theSVMclassifier (SVM) hasgiventhe and support vector machine process classification k-Nearest Neighbors)havebeenexperimentedforthemiRNA (Random forests, Support Vector Machines, Gradient boosted trees, methods classification features wasdeveloped.Several on sequence were identifiedandusedasatrainingset.AmiRNAdetectorbased in Mirbase,allof which haveexperimentallyprovento be miRNAs, miRNAs, whichconstitute the first structure of 1917 human miRNAs in silicoapproach mining thegut metagenomes. Inthisstudy, pre- to humanmiRNAinintestinalmicrobiotabydevelopingan of structuressimilar The aimofthisstudy is todeterminethepresence difficult andeconomicallyunfavorable. estimation ofpotentialtargetsmakeexperimental and themultiplicity structure [2]. ThemiRNA interest also anewandimportantresearch is affect humancells human microbiome harboredin bacterial miRNAs [1]. 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In these scans, 5 healthy people and 5 people with cirrhosis with people 5 and people healthy 5 scans, these In - k develop in miRNAs, were identified and used astraining a set. , them in the context of health and disease [ disease and health of context the in them affect human cells is also a also is cells human affect Curr Genomics. 2009 Nov; 10(7): 478 10(7): Nov; 2009 Genomics. Curr

MicroRNA groups showed similar structures to human miRNA in 1793 different species. species. different 1793 in miRNA human to structures similar showed groups ”, Precision recall recall Precision the secondary structure of miRNAs. The The miRNAs. of structure the secondary University, Faculty University, University, Bacillussp [1] Dong Yue, Hui Liu and Yufei Huang , “Survey of Hui Liu and Yufei [1] Dong Yue,

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[2] Shmaryahu A, Carrasco M and Valenzuela PD, “Prediction of “Prediction PD, [2] Shmaryahu A, Carrasco M and Valenzuela bacterial microRNAs and possible targets in human cell transcriptome.”, J Microbiol. 2014 Jun;52(6):482-489 Corresponding Author’s Address: Stem Cell Center (GENKÖK), Kayseri Figure 1. References: Computational Algorithms for MicroRNA Target Curr Genomics. 2009 Nov; 10(7): 478–492. Prediction”, the detection/treatment of disease with novel potential biomarkers the detection/treatment Keywords: Microbiome Interaction show up in a broad biodiversity spectrum. Moreover, in the context in the biodiversity spectrum. Moreover, broad show up in a species of disease, certain in the putative miRNA exhibit variation experimental validations should be Further sequences they harbor. extent these sequences what are reveal to in order to carried out human gene expression. regulating if they function in expressed and microbiome new dimension in host- such validations, a case of In in practical opportunity as well as a in question, interactions will be species p=0.043, pluranimalium identified as Streptococcus were Cellulophaga 0.048, = p pilosa Limnochorda 0.036, = p Bacillus sp identified as genus were the most significant and 0.024 lytica p = Nocardioidaceae = 0.045, Actinomyces p = 0.047, p Bacteroidia p=0.043. The study suggests that human gut microbiome contains abundant number of sequences resembling human miRNA. These sequences in 1793 different species. Student’sspecies. different in 1793 determine to used was t-test a significant there was whether difference putative number of for the significantThe most each species miRNA for the groups. between ] 0.043, 2 Erciyes University, Graduate School of Health Sciences, Bioinfomatics Systems Biology Deparment, Kayseri Deparment, Biology Systems Bioinfomatics Sciences, of Health School Graduate University, Erciyes Erciyes Erciyes

Ayşenur SOYTÜRK in silico 1 features was developed. which constitute the first structure of 1917 human miRNAs in Mirbase, all of which have experimentally experimentally have of which all Mirbase, in miRNAs human 1917 of structure first the constitute which proven microbiota by boosted trees determined for a EMBL t to according screened were metagenomes human 10 data, these of subset a As disease. value. threshold the multiplicity of potential targets make experimental estimation difficult and economically unfavorable. economically and difficult estimation experimental make targets potential of multiplicity the this aimof study The SVM the for graph recall precision The value. accuracy best the given has (SVM) machine vector support 1. Figure in shown is method classifier located = Prediction assembled after filtering human genome contamination genome human filtering after assembled tide tide 70 nucleo on performed governs [1] References: generated significant a was there whether determine to used species significant genus wereidentified microbiome method different 1141 patients, 5 cirrhosis of group the in a result, As miRNA [ transcriptome. in cell human The study suggeststhat humanmicrobiome gut 0.043. miRNA Figure 1. 1. Figure regulated by by regulated MicroRNA (miRNA) are small noncoding fragmentsRNA of about 19 3 4

Corresponding author’saddress

certain species exhibit variation in the the in variation exhibit species certain have important regulatory functions regulatory important have validations shouldbe carried in out order to reveal microbiome interactions will be in question, as well as a practical opportunity in the detection/treatment of of detection/treatment in the opportunity practical a as well as in question, be will interactions microbiome biomarkers. potential novel with disease Keywords: function in regulating human gene expression. gene human regulating in function POSTER PRESENTATIONS 158 ISTANBUL 1. MarmaraUniversity,DepartmentofBioengineering,SystemsBiomedicineLaboratory, Busra Aydin DIFFERENTIAL CO-EXPRESSION NETWORK ANALYSIS NON-FUNCTIONING PITUITARY ADENOMAS VIA BIOMARKERS FOR PROGNOSIS OF INVASIVENESS-RELATED NOVEL GENOMIC 1 , KazimYalcinArga [email protected] Address: Author’s Corresponding Adenoma. Front Endocrinol.(2019)10,361. With PrognosisHuman Pituitary of Non-functioning Non-invasive Analysis ElucidatedaCoreModule inAssociation References: Pituitary Adenoma,Invasiveness, Prognosis, Biomarker Keywords: targets inthefuture. aseffectivebiomarkersand therapeutic prognosis and sub-typing featured gene modulewhichmight play a prominentroleinNFPA on may providetheevidence results These cord tissues. and spinal these modulegeneswerealsoexpressedinblood, gland, salivary of PA,and prognosis types. Furthermore, plussomerelatedcancer validated in associationwiththeindicatorofinvasiveness was insilico allmodulegenes.Moreover,co-regulates identified genemodule and PRDM14 were top five regulatorswithhighestdegreevaluesthat NFPAinvasive [1] .GATA1, with distinctprognosis GATA2, ETS1,ESR1, able to categorize thepatients into two groups and as invasivenon- A_24_P255786, A_24_P683553, and A_24_P916979), was which PDCD2, SGIP1, SWSAP1, and four unknown genes(A_24_P127621, including CEACAM6, CYP4B1, EIF2S2, HID1, IFFO1, MYO18A, As a result, wehave identified a invasiveness. novel 13-genemodule, for NFPAbiomarkers as potentialsystems considered prognosisand might be mRNAmodules,which and co-regulated co-expressed (n=18) 22) phenotypes. Weand non-invasive identifieddifferentially associated transcriptome dataset (n = 40) (n considering invasive = expressionnetworkanalysisof NFPA-carried out a differential co- patterns amongphenotypes.Inthisstudy, distinct co-expression we outstanding approach forelucidationof groups of geneswhich show an is network analysis co-expression Differential into meta-analyses. PA,data and their integration thereisagreat need foromics-level the multi-layered molecular mechanismlay behind the of invasion methods and leading to early and Revealing frequent recurrences. rarely.treatment to conventional resistance eventuates Invasion into surroundingtissues invasion gross somehow tumorscanexhibit features. Theyareknownasbenigningeneralbut challenging with clinically system tumors inthecentralnervous intracranial Non-functioning pituitary adenomas (NFPAs) arethemost common Differential Co-Expression Network, Non-Functional Co-Expression Differential [1]Aydin,B., Y. &Arga, K. Network Co-expression 1 Kazim Yalcin Arga, E-mail: POSTER PRESENTATIONS 159 , 1 , Tutku Yaras , Tutku 1 , Burcu Ekinci , Burcu Ekinci 2 3,4 , Suha Miral , Suha Miral 2 [1] Parikshak NN, Swarup V, Belgard TG, Irimia M, TG, Belgard V, Swarup NN, Parikshak [1] Autism Spectrum Disorders; Alternative RNA Splicing; Splicing;Autism Spectrum Disorders; Alternative RNA , Yavuz Oktay 1,4 References: Ubieta L, de la Torre V, Hartl C, Leppa Gandal MJ, Ramaswami G, Genome-Geschwind DH. Horvath S, JK, Lowe Blencowe BJ, J, Huang lncRNA,changes in wide regional gene expression and splicing, PMID: PubMed Dec 15;540:423-7. 2016 patterns in autism. Nature. 29995847 traits, compared to PCA analysis based on differential traits, compared to PCA analysis based expression. Overall, transcriptome-wide that our results suggest alternative with ASD leukocytes of patients splicing analysis in peripheral blood provides further insight disease into etiopathogenesis and should be purposes. considered for biomarker development Keywords: Co-Expression aberrant AS events. Specifically, we found that NR4A2 gene, which NR4A2 that we found AS events. Specifically, aberrant involvedin neurotransmitter synthesis had factor transcription is a aberrant splicing pattern in children compared to their parents and was a member of a co-expression module enriched for p53 downstream, also AP1 and alpha linolenic We acid (ALA) metabolism pathways. on differential alternative splicingPCA analysis based observed that correlation with autistic better to between children parents led and co-expressionassociated with autism. be genes may of networks exon level expression analysis gene and we performed end, this To in between non-syndromic their parents autistic dizygotic twins and by using the RNA-seq method to compare co-expression networks autism. Such combinatorial and their association with AS events in analyses revealed that some of the genes in co-expression modules release and neurotransmitter related to synapse maturation displayed Autism spectrum disorder (ASD) is a neurodevelopmental disorder deficits in social skills, repetitive behaviors, characterized by abnormality of speech nonverbal communication. As much as 1 and disruption that hypothesized We in 59 children is diagnosed with ASD. of some biological phenomenon like alternative splicing (AS) and , Pelin Unal Varis , Pelin Unal 1 4.Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Balcova, Genome Center, Dokuz Eylul University Health 4.Izmir Biomedicine and Izmir, Turkey. 1.Department of Genome Sciences and Molecular Biotechnology, Izmir International Sciences and Molecular Biotechnology, Izmir 1.Department of Genome Balcova, Izmir, Institute, Dokuz Eylul University Health Campus, Biomedicine and Genome Turkey. Dokuz Eylul University and Adolescent Psychiatry, Faculty of Medicine, 2. Deparment of Child Izmir, Turkey. Health Campus, Balcova, Biology, Faculty of Medicine, Dokuz 3. Department of Medical Eylul University Health Campus, Balcova, Izmir, Turkey. SPLICING OF NEURONAL GENES AND ALTERED AND ALTERED GENES NEURONAL OF SPLICING IN BLOOD NETWORKS TRANSCRIPTIONAL Kaan Okay Gokhan Karakulah TRANSCRIPTOME ANALYSIS OF AUTISTIC DIZYGOTIC DIZYGOTIC AUTISTIC OF ANALYSIS TRANSCRIPTOME REVEALED ABERRANT PARENTS THEIR AND TWINS POSTER PRESENTATIONS 160 Corresponding Author’s Address: Corresponding Author’s 22294735. population. Bull. 2013Schizophr May;39(3):555-63. PubMed PMID: an "orphan"receptor on sustainedattention in aschizophrenic Alaerts M, Del-FaveroMorla J,E, NR4A2: effects of Barabash A. B, Vázquez-Álvarez Sánchez- SantosJL, I, Cabranes JA, [5] Ancín 26;3:1. PubMedPMID:21423409. in autismspectrumdisorders.Front Synaptic Neurosci.2011 Jan [4] SmithRM,SadeeW. and aberrantRNAsplicing Synapticsignaling 2015 Jan2;11:53-70.PubMedPMID:25581244. in DSM-5criteriaforautismspectrumdisorder. AnnuRevClinPsychol. as reflected autism research in advances Recent [3] Lord C,BishopSL. May 25;474(7351):380-4.PubMedPMID:21614001. pathology.molecular convergent autistic brainreveals Nature.2011 Cantor RM, Blencowe BJ, DH. Geschwind Transcriptomic of analysis [2] Voineagu I, Wang JohnstonP, X, TianY, Lowe JK, HorvathS, MillJ, [email protected] POSTER PRESENTATIONS 161 1 , Aysegul Uysal , Aysegul 1 . The Sonic Hedgehog signaling . Tumor stem cells, like stem cells, Tumor . [3] [1,2] , Huseyin Aktug , Huseyin , 2 1 . Here, we prospectively investigated the effectof Here, we prospectively investigated . [4] Berrin Ozdil , 1 not cause sufficient cell death alone. Results of GBM and GBM cancer stem cells control groups and cyclopamine effect on them are different RNA and protein levels. at Different when results were often obtained co-cultureAs a result of this study, with these cells was obtained. especially when the differences in single cell culture and co-culture results are considered, micro-environmentthe importance of the effect in diseases and treatments has been revealed. signaling pathway active in GBM and GBM cancer stem cells, signaling pathway will of cyclopamine undergo a change in the inhibition effect in the cellsthe to of the same time, the ability At astrocytes. presence of cell-celland signalling, secrete Shh protein as paracrine interactions were expected to answer the question whether Shh signaling pathway cyclopamine inhibition. Although negative effect on positive or has a it does cyclopamine is an effective Shh pathway, agent to suppress the target for Glioblastoma multiforme treatment. Cyclopamine for Glioblastoma multiforme treatment. Cyclopamine target is an important agent used in the inhibition of the Shh signaling pathway to has shown the Smoothened (Smo) receptor and binding to by inhibit tumor growth in gliomas, pancreatic and colon carcinomas in animal studies GBM cancer and its inhibition on GBM and Shh signaling pathway stem cells co-cultured Cell-cell with astrocytes. interactions in the co-culture conditions were evaluated to determine the Shh whether are characterized by self-renewal and differentiation and resistant to resistantself-renewal and differentiation are characterized by and treatment regimens as radiotherapy and chemotherapy besides, such thought to be affected by they can spread distant organs and are cancer recurrence also found in tumor mass. Cancer stem cells have also been identified in tumor masses of patients with Glioblastoma multiforme, and these cells may cause worsening prognosis. In has become an important signaling pathway recent years, Hedgehog pathway is one of the important signaling pathways that active in that pathways is one of the important signaling pathway such types, carcinogenesis.cancer Many embryogenesis and both rhabdomyosarcoma, colorectal carcinoma,as medulloblastoma, multiforme Glioblastoma this pathway. of have increased activity in adult tumor primary brain (GBM) is the most common malignant not does even if it infiltrating tumor, growing, rapidly is a humans and organs the distant metastase to Among the members of the Hedgehog gene family, Sonic Hedgehog gene family, the Hedgehog the members of Among the embryonic development effects on (Shh) has the most prominent at the brain and extremities. 2.Suleyman Demirel University, Faculty of Medicine, Department of Histology and Embryology, Faculty of Medicine, Department of 2.Suleyman Demirel University, 32260, Isparta Turkey STEM CELLS IN MONO AND CO-CULTURE SYSTEMS AND CO-CULTURE IN MONO STEM CELLS Kocaturk Duygu Calık Embryology, 35100, Izmir of Medicine, Department of Histology and 1. Ege University, Faculty Turkey CYCLOPAMINE AFFECTS THE GENE EXPRESSION OF OF EXPRESSION GENE THE AFFECTS CYCLOPAMINE AND GLIOBLASTOMA ASTROCYTES, GLIOBLASTOMA POSTER PRESENTATIONS 162 Izmir, Turkey. Email:[email protected] Histology and Embryology Faculty of Medicine, Ege University 35100, Address: Author’s Corresponding Biol Ther. 2012;13(7):487–495.PMID:22406999 Gaunitz F. Hedgehogsignalinginglioblastoma multiforme. Cancer R,Gebhardt R, Hipkiss AR,ThieryJ,Baran-Schmidt J, Meixensberger [4] BraunS, A, C, Hovhannisyan Renner OppermannH,A, Mueller States; 2016Nov;34(4):981–998.PMID:27720005 Tumors. Epidemiology of Brain KA. [3] McNeill United NeurolClin. PMID: 19736325 progenitor proliferation.Development.2009;136(19):3301–3309. to control neural activation isrequiredupstreamofWntsignalling R,Le[2] Alvarez-Medina DreauG, Hedgehog RosM, Marti E. PMID: 23547970 and cancer therapy.carcinogenesis 2013. BreastCancerResearch. TheHedgehogsignallingpathway in breastdevelopment, Swarbrick A. References: in theabsenceandpresenceofcyclopamine. astrocytes (SVG),GBM and (KKH) of GBM stemcells selected genes 1. Figure SonicHedgehog;Cyclopamine Co-Culture; Keywords: 4. 4. 3. mohnd So rcpo ad a son o nii tmr rwh n loa, acetc n colon and pancreatic gliomas, in growth tumor inhibit to shown carcinomas in animal studies treatment. has and multiforme receptor path Glioblastoma signaling Shh (Smo) for the of Smoothened inhibition target the in important used agent important an an is become Cyclopamine has pathway signaling Hedgehog Glioblastoma with patients of masses tumor in identified been also have cells stem Cancer mass. tumor in found also recurrence cancer by affected be to thought are and organs distant spread can they besides, chemotherapy and radiotherapy as self- by characterized are cells, stem like Aysegul Uysal Corresponding author’s address 2. 2. (KKH) of(KKH) selected genes Figure 1. a rapidly growing, infiltrating tumor, even if it does not not does it if even tumor, infiltrating growing, rapidly a i and humans adult in tumor brain primary malignant common most the is (GBM) multiforme Glioblastoma pathway. this of activity increased have carcinoma, colorectal rhabdomyosarcoma, medulloblastoma, as such emb both in active that pathways signaling important the the embryonic development at the brain and extremitiesand brain the at development embryonic the Hedgehog Sonic family, gene Hedgehog the of members the Among Turkey. Email: [email protected] Email: Turkey. 1. 1. References inhibition on GBM and GBM cancer stem cells co cells stem cancer GBM and GBM on inhibition Keywords treatments has been revealed. At the sam the At astrocytes. of presence the in cyclopamine of effectinhibition the in change a undergo will cells, stem cancer GBM and GBM in active pathway signaling Shh the whether determine to evaluated were conditions culture were expected to answer the question whether Shh signaling pathway has a positive or negative effect on effect negative or positive a cyclopamine Although inhibition. cyclopaminehas pathway signaling Shh whether question the answer to expected were cause cause sufficient cell death alone. Results of GBM and GBM n co and culture cell single in differences the when especially study, this of result a As obtained. was cells these with co when obtained often were results Different levels. protein and RNA at different are them on effect c

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POSTER PRESENTATIONS

163 . [3] [1] . Owing to the recently . Owing to [2] , assessed differentially gene expressed [4,5] 1 [1] Srinivas PR, Kramer BS, Srivastava S. Trends Trends Srivastava S. [1] Srinivas PR, Kramer BS, Breast Ductal Carcinoma, Biomarker Discovery, Discovery, Breast Ductal Carcinoma, Biomarker , Emrah Nikerel 1 References: Oncol. in biomarker research for cancer Lancet detection. 2001;2(11):698-704. PMID:11902541 Stojadinovic A,I, Avital YG, Man Yang Liu M, Y, Zuo J, [2]Wang and network Pathway Ressom HW. MG, Tadesse RS, X, Varghese omics markers from cancer signature identification of for approaches 2015;6(1):54-65. PMID:25553089 data. J Cancer. [3]Cancer Genome Atlas Network. Comprehensive molecular from public databases mentioned methods above these sets using the clusteredsets and using a handful of distance measures. The identified networks are networks. benchmarked against previously known/identified Keywords: P4 Medicine Transcriptomics, Clustering, the results with external data. The aim of this work is to evaluate alternative clustering methods Density- Hierarchical, Fuzzy, clustering, methods k-means (Partitioning subnetwork identificationbased and Model-based clustering) for do using transcriptome data within biomarker discovery context. To so, we compiled a breast ductal carcinoma transcriptome dataset assess the candidates and identify networks, on carefully collectedidentify networks, on assess the candidates and from case-control e.g. datasets, studies only the measurements not but approach, systems biology introduced metabolic reaction etc.) are also networks (protein-protein interaction, also taken into consideration to link different -omics datasets. Focusing challenges are main data, scale transcriptome large use of the on large number and complex nature of gene-gene interactions, small sample sizes despite recently of available large cohorts and validation The discovery of biomarkers underwent a transition from knowledge-The discovery of biomarkers driven practice i.e. based on known mechanisms disease, of the target to data-driven discovery especially with the advent of high-throughput available large scale -omictechnologies and As such, datasets. collectionusing a consists of biomarker discovery workflow typical pruning and classification techniques to e.g. of statistical, clustering, Biomarkers are, typically used in medical context, any measurable used in medical context, any measurable Biomarkers are, typically molecule or set of molecules, or an (reconstructed) image, pointing sometimes even its state. feature, often the presence of diseases, a to prognosis It is utilized in diagnosis, of numerous and monitoring heterogeneous diseases, effective evaluating potentially therapeutic constitutes main gateway to P4 medicine regime and intrinsically BREAST CANCER DATA TRANSCRIPTOME BREAST Nehir Kızılilsoley and Bioengineering, Yeditepe University 1. Department of Genetics EVALUATING CLUSTERING METHODS TO IDENTIFY TO METHODS CLUSTERING EVALUATING USING DISCOVERY BIOMARKER FOR SUB-NETWORKS POSTER PRESENTATIONS 164 Emrah [email protected] [email protected] Address: Author’s Corresponding Nucleic AcidsRes.2019;47(D1):D711-D715.PMID:30357387 data. expression ArrayExpressupdate - frombulkto single-cell A. C, Fonseca Petryszak NA, R,Papatheodorou I, Sarkans U, Brazma GeorgeN, Snow FüllgrabeA, AliA, Iqbal H, HuertaL, [5]Athar A, Oncol (Pozn). 2015;19(1A):A68-77.PMID:25691825 Atlas (TCGA):of knowledge.Contemp source animmeasurable [4]Tomczak Czerwi K, PMID:23000897 portraits ofhumanbreasttumours.Nature.2012;490(7418):61-70. ń ska P, WiznerowiczM.TheCancerGenome Nehir Kızılilsoley Kızılilsoley Nehir POSTER PRESENTATIONS 165 . Understanding biological . Understanding biological [1] 1 . The aim of this study is to identify molecular signatures is to this study . The aim of [2] Kazim Yalcin ARGA Kazim Yalcin , 1,2 significantly forms dissociate from each other although all of them The whole mechanism of how these diseases are formed in kidney. needs to be elucidated urgently one by one, so that specific treatment for further valuable data we reported can be produced. In addition, biomolecules clinical experimental and efforts, because the proposed have significant potential as systems biomarkers for screening or therapeutic purposes in RCC types. study. This approach revealed already-known biomarkers, tumor biomarkers, revealed already-known This approach study. various receptors, well as as oncogenes in RCC types suppressors and metabolites as and miRNAs, transcription factors, other proteins, novel and potential therapeutic targets. In biomarker candidates addition, we predicted a significance difference among KIRP and location tumor the from originating probably KICH, with KIRC these three diseases that are and tissue origin. Our results support and GATA1, were determined as mutual transcriptional regulatory mutual were determined as GATA1, and and hsa-miR- components for all three diseases. hsa-miR-335-5p were determined 887-5p miRNAs as mutual for all three diseases. three diseases. receptor for BUB1 was determined as only mutual Retinoid derivatives, Acetyl-CoA oxidase were identified Proline and receptors, miRNAs Reporter TFs, as affected metabolites in RCC types. and metabolites specific to each disease were determined in this gene expression were datasets statistically analyzed and differentially were identified. Hub-proteinsexpressed genes were identified by were protein-protein interactions. Then, results account taking and metabolic networks to integrated with transcriptional regulatory levels. The metabolite and protein biomolecules at identify reporter prognostic performance of the biomolecules were also analyzed AR E2F4, ETS1, GATA2, using survival Six TFs, YBX1, time statistics. location of a cancer, cell type which they originate and genetic and originate which they cell type cancer, a of location alterations etc.), transcription factor, (mRNA,RNA at (receptor, miRNA), protein of gene expression levels profiles the integration metabolite by and with genome-scaleanalyze three biomolecular networks, and to comparative perspective. A multi- KIRC) in a KIRP, (KICH, RCC types here. Initially, applied and developed was analysis method stage Human Renal Cell Carcinoma (RCC) has the highest mortality rate highest mortality Carcinoma (RCC) has the Human Renal Cell cancers of the genitourinary in adults mechanism the current diagnosis, treatment and of RCC may improve prognosis of RCC. Renal clear carcinoma cell renal (KIRC), papillary cell cell carcinoma (KIRP) and chromophobe renal carcinoma (KICH) significantly many respects including dissociates from each other in SIGNATURES AT DIFFERENT OMICS LEVELS OMICS DIFFERENT AT SIGNATURES Aysegul CALISKAN Marmara University, Istanbul, Turkey, 1. Department of Bioengineering, Istinye University, Istanbul, Turkey, 2. Department of Pharmacy, COMPARATIVE ANALYSIS OF HUMAN RENAL CELL RENAL HUMAN OF ANALYSIS COMPARATIVE CARCINOMA MOLECULAR TO TYPES IN RESPECT POSTER PRESENTATIONS 166 URL: https://eczacilik.istinye.edu.tr/tr/kadro/106 URL: [email protected] e-mail: İstanbul,Turkey. Zeytinburnu, 24, A Rezidans, Mira YoluSokak, Çırpıcı Edirne Mah., Address: Author’s Corresponding 461–473. PubMedPMID:22112490. [2] CairnsP.Biomark. 2011;9(1-6): Carcinoma. Cancer RenalCell pp: 110358-110366.PubMedPMID:29299153. related genesbygenenetwork.Oncotarget. 2017, Vol. 8, (No. 66), C, Wang Y, Lu T, Wang F. Identifyclearcellrenalcarcinoma References: Keywords: Kidney;RenalCellCarcinomas;Biomarkers [1] Yan F, Wang Y, Chen Lu LiuC,ZhaoH, X, ZhangL, Postal address:Maltepe POSTER PRESENTATIONS 167 1 which is published by our which is published by [2] , 2 , Kazim Yalcin Arga , Kazim Yalcin 3 , Beste Turanli 1 [1] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre Soerjomataram I, Siegel RL, Torre J, Ferlay [1] Bray F, Cancer, Differential Interactome, Molecular Dynamics Cancer, , Betul Karademir 1 . The high heterogeneity . The high heterogeneity of colorectal cancer leads to difficulties , Gizem Gulfidan 1 This study was supported under FEN-C-1206-0199 project. Keywords: References: LA, estimates of Jemal A. GLOBOCAN cancer statistics 2018: Global countries. CA cancers in 185 36 for worldwide mortality incidence and Cancer J Clin. 2018 Nov 68(6):394-424. PubMed PMID: 30207593 using molecular dynamics simulations root mean square deviation deviation mean square root using molecular dynamics simulations were taken as performance root mean square fluctuation (RMSD) and metrics investigationto perform further in vitro cell culture. This will shed light on the identification of specific study biomarkers and accurate and targets for early detection, disease progression, drug colorectal cancer some systems of understand to helping treatment, by preventing high mortality. protein modules were identified. Principal Component Analysis for protein modules were identified. Principal analysis for prognostic purpose and Kaplan-Meier diagnostic purpose modules were determined were performed for each module. 16 modules were found while 6 potential diagnostic significant having having prognostic potential. In addition common significant in dPPIs sets both data were in observed point of drug repositioning and 6 targets for these interactions and 13 drug were identified. By dPPIs using “Differential algorithm Interactome” from “The sets were obtained data independent Two research group. samples and 51 tumor containing 644 Cancer Genome Atlas (TCGA)” normal samples “Gene and Expression Omnibus (GEO)” containing 32 tumor samples 32 normal samples. and As a result of differential dPPIs (2434 were determined interactome analysis, significant dPPIs highly interacting set) and data in TCGA dPPIs 1619 set, in GEO data the third most common type of cancer found in Turkey and worldwide and in Turkey cancer found of type common most third the [1] The aim of this explaining behavior of this cancer. the biology and therapeutics identify prognostic biomarkers and potential is to study interactions differentiated protein colorectal cancer using the for first stage, at this purpose Among groups. tumor healthy and among identified by were (dPPIs) the differential protein-protein interactions Colorectal cancer is one of the most lethal types of cancers common of lethal types the most Colorectal canceris one of base published by According to the data in both men and women. cancer colorectal is for Researchthe International Agency on Cancer, 1. Department of Bioengineering, Marmara University, Istanbul, Turkey 1. Department of Bioengineering, Turkey Istanbul Medeniyet University, Istanbul, 2. Department of Bioengineering, Marmara University, Istanbul, Turkey 3. Department of Biochemistry, COLORECTAL ADENOCARCINOMA COLORECTAL Hande Beklen Sarica Pemra Ozbek DETERMINATION OF CANDIDATE BIOMARKERS CANDIDATE OF DETERMINATION IN INTERACTOME DIFFERENTIAL THROUGH POSTER PRESENTATIONS 168 Marmara University, Istanbul,Turkey, [email protected] Address: Author’s Corresponding 2017 May21(5):285-294.PubMedPMID:28375712 AJournalofIntegrativeBiology.Candidate Biomarkers. OMICS: New Reveal Interactome andNetworkEntropyAnalysis Differential [2] Ayyildiz,D, Gov, Y. Sinha R, and Arga K. E, Ovarian Cancer Department of Bioengineering, POSTER PRESENTATIONS

169 [4] of previously reconstructed [3] . [2] 1 . [1] , Tunahan Çakır , Tunahan 1 values on reactions to obtain reaction scores. If genes control a their expression calculate was summed up to reaction independently, the corresponding reaction score, and if the genes code for different their expression levels subunits of an enzyme complex, minimum of reaction score. These scores indicate the potential as was assigned capacity of reactions based on transcriptional activities for of genes condition. By using many PD related transcriptome datasets for that To this aim, an updated version this aim, an updated To brain-specific genome-scale model of human metabolic network and reactions with improved compartmentalization of was used, transcriptome mapping mitochondrial. For cytosolic and as metabolites a template the improved network model of human was used as data, reconstruct the first brain-specificto metabolic network model of mouse by a homology based approach. Since many reactions are controlled by multiple genes, it is important to map gene expression of mouse models of PD created with both approaches are available approaches of mouse models of PD created with both OmnibusGene Expression in the public transcriptome database metabolism- oriented clustering-based pursues a (GEO). This study each mouse models with PD of transcriptome data the comparison of the mapping by patients PD of the transcriptome data with and other data on genome-scale brain-specific metabolic networks. metabolic pathways, they have been used for systematic investigation they have been used for systematic metabolic pathways, of complex disease mechanisms tests are mostly Experimental investigation of diseases including drug is one of the on model organisms. Mus musculus (Mouse) carried out for human diseases. Mouse most commonly used model organisms genetic- different approaches: two with are created PD models of based, and chemical (toxin) based. Numerous transcriptome data Metabolism has a principle Metabolism has a principle role in molecular mechanisms of diseases since it provides nutrients are necessary that to fuel pathways are of the metabolic changes necessary for cellular processes. Most genes affects of transcriptome level. Transcription the at initiated reactions, leading enzymes catalyzing metabolic the translation of Since changes in the activities to pathways. genome-of metabolic scale on enables metabolic networks mapping of transcriptome data Parkinson’s disease second most common (PD) is the Parkinson’s The number of people disease in the world. neurodegenerative suffering from neurodegenerative diseases has dramatically increased in recent Therefore, it is essential decades. to understand the disease mechanisms and diagnostic improve treatment in order to techniques DISEASE Ecehan Abdik University Gebze Technical 1. Department of Bioengineering, CLUSTERING-BASED METABOLISM-ORIENTED METABOLISM-ORIENTED CLUSTERING-BASED PARKINSON’S OF MODELS MOUSE OF ANALYSIS POSTER PRESENTATIONS 170 Gebze Technical University, Kocaeli, Turkey [email protected] Address: Author’s Corresponding network. FEBSOpenBio.Elsevier;2014;4:542–53. metabolic brain-specific reconstructed by anewly metabolism level effectsof neurodegenerative diseasesonhumanbrain transcription- T.of Çakır analysis Systematic K, Ülgen M, Sertbaş [4] University, 2019) Candidates forParkinson’s thesis,GebzeTechnical(Master’s Disease, to IdentifyDrugTargetMetabolic Networks Genome-Scale andDrug Kaynar,[3] Ali of Transcriptome IntegrativeAnalysis Data and Central; 2011;5(1):180. physiology. systems of whole-body analysis BMCSystBiol.BioMed metabolic networkfor Familitype genome-scale I. Amulti-tissue R,Woodcock Feist AM, Usaite-Black [2] Bordbar A, J, Palsson BO, Association fortheAdvancementofScience;2003;302(5646):819–22. neurodegeneration in Parkinson’s disease. Science (80- ). American References: Animal Models,Clustering, Transcriptome Keywords: realistically. PD models canreflectPD experimental of metabolism more which datasets togetheralsoenabledidentification of humanandmouse between differentmousemodelsofPD.and differences Clustering fold changes from the datasets to identify metabolicsimilarities of datasetsfromGEO.was appliedtothe clustering Hierarchical for controlgroupsandPDwerecalculatedanumber were investigated.Foldchanges between reactionscores changes both mouse PDmodelsand PD patients, disease relatedmetabolic Parkinson’s Metabolic Network, Disease,Genome-Scale [1] Dawson TM, Dawson VL. Molecular pathways of Molecular pathwaysof [1] DawsonTM,VL. Department of Bioengineering, POSTER PRESENTATIONS 171 Hence, for most [4] . In our attempt to understand to . In our attempt [1,2] . Integrating proteome data from two cellular from two proteome data . Integrating [3] [1] Will T, Helms V. Differential analysis of combinatorial Helms V. [1] Will T, Regulatory Protein Complexes; Protein Complex Complexes; Protein Regulatory Protein 1 framework can point out the most plausible drug targets. Keywords: Gene Enhancer-Target Interaction Network; Protein-Protein Prediction; Prediction References: BMC Bioinformatics. protein complexes with CompleXChange. occurring between enhancer accessibility, the occurrence of protein the occurrence of enhancer accessibility, occurring between The distinction between the complex and target gene expression. protein complex profiles in healthy and disease conditions will provide more efficient drug targets. Although transcriptional factors nowadays, by different previously, were thought to be “untargettable” modes of action they are inhibited or activated. drivers, our major are disease conditions where transcriptional factors DNA-binding transcriptional factors, left out otherwise. Experimentally genes factors will be collectedvalidated target of these transcriptional run will be Second prediction algorithm from relevant databases. the enhancers the integration genes. By for finding out target of the of accessibility and promoter regions, the of these enhancer data will be inferred. The mechanism regions regulatory uniting the association of differential protein complexes to these regulatory sequences target genes and will correlation be made based on the conditions with PPI and DDI, differential protein complexes will be complexes will differential protein DDI, and PPI conditions with categories: absence/ This differentia can be analyzed in 3 obtained. presence of protein complex, missing protein members in the same the protein complex. Taking complex, or differential abundance of complexes the first one into account, differential protein list will be for their role members of complexes will be browsed formed. Protein in transcription: The complex will be kept if it includes least two at networks and omic data integration. Based on dense subnetworks in integration. Based data networks and omic protein-protein, domain-domain networks and integrated interaction on predictions quantitative and qualitative both data, proteome protein complexes can be made involving two methodology a we formulate transcriptional regulation, Enhancer- complex prediction 2. Protein major prediction steps: 1. gene prediction target Transcriptional regulation involves with protein- many proteins, along Transcriptional interactions. When protein and protein-DNA these interactions occur simultaneously to initiate the basic transcriptional machinery, Protein can be defined as a protein complex. the resulting structure complexes providing communication among enhancer– promoter– with protein interaction gene regions can be studied further target , Pınar Pir 1 PROTEIN COMPLEXES PROTEIN Emel Kökrek of Bioengineering, Çayırova, KOCAELİ University, Department 1. Gebze Technical BRIDGING ENHANCERS AND TARGET GENES GENES TARGET AND ENHANCERS BRIDGING REGULATORY CONDITION-SPECIFIC THROUGH POSTER PRESENTATIONS 172 nvriy Dprmn o Boniern, 10, Çayırova, 41400, Bioengineering, of KOCAELİ. [email protected] Department University, Address: Author’s Corresponding mdpi.com/1420- 3049/23/6/1479 [Internet]. 2018 Jun 19;23(6):1479. Available from: http://www. Targeting Transcription Factors for Cancer Treatment. Molecules [4] Lambert M,JambonS, M-H. Depauw S, David-Cordonnier bbv097 https://academic.oup.com/bib/article-lookup/doi/10.1093/bib/ [Internet]. 2015Nov19;17(July2015):bbv097. from: Available promoter–enhancer interactionsand bioinformatics. BriefBioinform OS, Gabrielsen R. In theloop: Eskeland SandveGK, [3] MoraA, journal.pone.0183460 22;12(9):e0183460. Available from:https://dx.plos.org/10.1371/ RuanJ,positive analysis. editor. PLoS One[Internet].2017 Sep J. Protein complexpredictionviadensesubgraphs andfalse Araya [2] HernandezC,MellaNavarroG, Olivera-NappaA, 2019;20(1):1–14. ıa Pr ez Technical Gebze Pir Pınar POSTER PRESENTATIONS 173 Biophysics Department, School Biophysics Department, Shannon’s Information Communication Theory; Gene Information Communication Shannon’s 1 of Medicine, Altinbas University, Kartaltepe Mah. Incirli Cad. No11, Incirli Cad. Mah. Kartaltepe University, Medicine, Altinbas of Istanbul, Turkey. 34147 Bakirkoy, Email: [email protected] characteristically possessing information, and involving information in communication events of the cells, in the form of information translation of proteins from transformation, such as we see during genes. Keywords: Relation Allometric Scaling Length; Size; Protein Corresponding Author’s Address: translation from its encoding gene. The extension in the information translation from its encoding gene. The due to the presence amount introns in the eukaryotic DNA was of was basically using an exponent of the protein length. This solved by establishing an allometric relation between the information contents information interest. Here it is claimed that the parameters of of the explain means to a concepts, can be the related and theory, are molecular entities that source of allometric relations between such transmission of messages through noisy channels. It has a wide scope is concept that the allometric scaling Independent of applications. of modified use a we tried in our earlier studies to formerly mentioned, form of the information communication theory to relate the encoding equalize their numbers, to DNA's and the encoded protein's residue information contents. This was based on the assumption that there is a loss transformation rather than of information, during protein Allometric scaling relations between distinct formulates biological are observed or the logarithmic relations that parameters. There, as the parameters of interest can be formulated present between the parameters, the of plot the trendline to power fitted the of equation of the equation of the linear or as the antilogarithm trendline that is parameters. A distinct concept, of the same plot fitted to the log-log reliable deals originally with theory information communication GENES AND PROTEINS? GENES Yekbun Adiguzel Turkey School of Medicine, Altinbas University, Istanbul, 1. Biophysics Department, CAN INFORMATION COMMUNICATION THEORY BE BE THEORY COMMUNICATION CANINFORMATION BETWEEN EXPLAIN TO USED RELATION ALLOMETRIC POSTER PRESENTATIONS 174 2. DepartmentofBioengineering,IstanbulMedeniyetUniversity,Istabul,Turkey 1. DepartmentofBioengineering,MarmaraUniversity,Istanbul,Turkey Kazim YalcinArga Gizem Gulfidan ADENOCARCINOMA AND CANDIDATE DRUGS INPROSTATE IDENTIFICATION OF SYSTEMSBIOMARKERS an studiesto insight for clinic design diagnostic kit for early diagnosis and provide of cancers analyses studies integrating withsystems-level drug repositioning approach. This study will pave the way for further agents weresubmitted for prostate adenocarcinoma by the aidof Finally,hazard ratio>7,p-value<0.05. somecandidatetherapeutic values >0.9andas having prognosticpropertieswith specificity and ashavingdiagnosticpropertieswith sensitivity were determined modules Several diagnostic andprognosticfeatures wereexamined. of DIPsmodules consisting we foundvarioussignificant andtheir were carriedout for DIPs. analysis gene setenrichment Moreover, tumor suppressor proteins and 50 DIPs wereoncogenes.Also,the and itwasfoundthat77ofDIPs weredruggable, 49 DIPs were tumor phenotype. In addition, the featuresof DIPs wereinvestigated which 19 of these dPPIs were repressed,while164 were activated in in prostateadenocarcinoma, in dPPIsweresignificant taking roles that 183 dPPIs among 194 differentially interactingproteins(DIPs) differentiated attumorstate(dPPI).show interactions Ourresults protein-protein protein interactomedata in ordertofindsignificant 550 samples (52 normal -498 tumor samples) by using thehuman applied to gene expressionprofile of prostate adenocarcinoma having that end, differential interactome algorithm for prostate adenocarcinoma.Toof newdrugcandidates presenting using our differential interactomeapproach and provides the of systemsbiomarkerswithspecialfocusonprostate adenocarcinoma properties onnewdiseases approved drugs with highconfidenceand good pharmacokinetic approach tofindoutalready a useful is for drugrepostioningwhich provide opportunitiesand computational analysis technologies omic on waste ofmoneyandtime.Increasingstudies improvements inpoint of money,need investments timeand labor and thus itcauses treatment and productionoftherapeuticagentsforcancer discovery biology behindthesepathologies.Ontheotherhand, molecular the complexityof accurate androbust biomarkers considering However,of tumorigenesis. task todevelophighly a challenging itis as welltheprevention accurate diagnosisandprognosisofcancers cancer and the identification of efficacious biomarkersiscrucial for [1] and mediate variousphysiologicalprocessesinalllivingorganisms pathways,cellular physical interactionsamongproteinsinfluence since understanding oftumorigenesis, to reachasystems-level the alterationsinproteininteractomeismandatory Deciphering . Also, the elucidating of the molecular mechanisms underlying mechanisms . Also,theelucidatingofmolecular 1 , BesteTuranli 1 2 , HandeBeklen [2] . The present study highlights the concept study highlights . Thepresent 1 , [3] was developed and POSTER PRESENTATIONS 175 Department of Bioengineering, Bioengineering, of Department [1] Sevimoglu T, Arga KY. The role of protein interaction of protein interaction The role Arga KY. T, [1] Sevimoglu Protein-Protein Interactions; Prostate Adenocarcinoma; Prostate Interactions; Protein-Protein PMID: 28375712. Corresponding Author’s Address: [email protected] Istanbul, Turkey, Marmara University, repositioning for gynecologic tumors: a new therapeutic strategy for strategy therapeutic new a gynecologic tumors: repositioning for ScientificWorldJournal. cancer. PubMed PMID: 25734181. 2015;2015:341362. Differential Ovarian Cancer E, Gov KY. Sinha R, Arga [3] Ayyildiz D, Entropy Interactome and Network Biol. Biomarkers. Omi A J Integr Candidate Analysis Reveal New PubMed 2017 May;21(5):285-294.. Keywords: Repositioning Interactome; Drug Differential References: Comput Struct systems biomedicine. networks in PubMed PMID: 25379140. Sep 3;11(18):22-7. 2014 J. Biotechnol Y, K,Masuda Kobayashi Irie H, M, K,Banno [2] Yanokura M, Iida Drug E, Aoki D. Tominaga of cancer and to investigate repositioned cancer drugs. drugs. cancer repositioned investigate and to of cancer project. under TUBITAK/SBAG/117S489 supported was This study POSTER PRESENTATIONS 176 2.Department ofBioengineering,IstanbulMedeniyetUniversity,34700,Turkey 1.Polymer ResearchCenterandChemicalEngineeringDepartment,BogaziciUniversity R. BusraOzguney SITES INRAC1ONCOGENIC MUTATIONS TOWARDS INSILICO IDENTIFICATION OF RESCUE are nottotally recovered. Lastly,the importance ofthe weemphasize active, however theeffectsof P29S mutation on residues29 and 30 both statesofP29S in observed downstream effectorbindingresidues Mg2+ coordination andthebehaviorofGTP and regulatory/ significantly. Some of the P29S mutants indeed restorestheimpaired of these statesissimilarto the wt inactive, theotherstate deviates related toMg2+coordination, one particularly fortheresidues while and theircooperativity.mobilities residue occupancies, Intriguingly, in H-bonding adopt different conformationswithdistinctdifferences Further,the wtactive,P29Sactivetakesat least two unlike states that or attheinteractioninterfaceofeffectorproteins. mechanism generally perturbresidueswhichareeitherimportant in theRhoswitch (MD) Simulations.Mutations are observedto Molecular Dynamics Rac1 structuresareanalyzedby states) andtheP29Smutantactive their dynamicresponseeffectsonthewildtype (inactive and active residues introduced tothefirstandsecondslowestglobalmode hinge related toprotein’sthe dynamics mutations are function.Insilico informative regionsare hypothesized to have the capacity to alter to themechanistically vicinity at/in close positions The rescue towards thewildtype. of conformations that wouldcontrolandmodifytheensemble sites identify key conformational shiftduetomutations andmechanistically [4] the effectofoncogenicmutations might bedesignedtorescue suppressor mutations mutation(s)” sothat rescue drugsmimicking [2] melanoma in activation resulting the effector ultimately increases property and a “spontaneouslyactivated”statewith“fast-cycling” GDP/GTPthe intrinsic increases rate leadingto exchange nucleotide which P29S; mutations inRac1isthegain-of-function oncogenic nucleotide exchangeinhibitors(GDIs)[4].Oneofthemostfrequent (GAPs),factors (GEFs)andguanine guaninenucleotideexchange tightly controlledandmaintainedbyGTPase-activatingis proteins interacts withitseffectors[4].Regulationofthisswitchmechanism GDP bound state andactiveGTPinactive bound state inwhichit superfamily, Rac1maintainsitsfunctionviacyclingthrough two states: as a central playerincancertherapy resistance metastasis spot mutations yields cancerdevelopment,progressionand adhesion andmigration[1].TheabnormalactivityofRac1duetohot GTPase and a member of Ras superfamilyinvolvedincellproliferation, Rho a small C3botulinumtoxinsubstrate1(Rac1)is Ras-related . 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. (a) 17847101 Corresponding Author’s Address: Bogazici University, Research Center, Engineering and Polymer E-mail: [email protected] Istanbul, Turkey, [5] Bahar I, Atilgan AR, Erman B. Direct evaluation of thermal [5] Bahar I, Atilgan AR, Erman B. fluctuations in proteins using a single-parameter harmonic potential. Des. 1997;2(3):173-81. PubMed PMID: 9218955 Fold Proteins. Folded Gaussian Dynamics of Erman B. B, Ivet [6] Haliloglu T, 1197; 79(16):3090-93. Phys Rev Lett. Nussinov R, HJ, Wolfson Schneidman-Duhovny D, Emekli U, [7] prediction of hinges automated in protein HingeProt: Haliloglu T. PMID: PubMEd Mar;70(4):1219-27. 2008 structures. Proteins. and radio-resistanceand mechanisms: in cancer Opportunities PubMed Apr;124:29-36. therapeutics. Crit Rev Oncol Hematol. 2018 PMID: 29548483 Lathrop Salmon K, Hatfield GW, [4] Baronio R, Danziger SA, Hall LV, All-codon scanning identifies p53 cancer rescue P. Kaiser RH, PubMed mutations. Nucleic Nov;38(20):7079-88. Res. 2010 Acids PMID: 20581117 structure, localization, and expression of the human gene. Biochem PMID: PubMed Nov 2;277(3):741-51. Biophys Res Commun. 2000 11062023 Cells. in Solid Tumors. the Lead Takes RAC1 Aske JC, Dey N. [2] De P, 2019 Apr 26;8(5). PubMed PMID: 31027363 Gomez DE, J, Maggio Gonzalez N, GA,[3] Cardama Alonso DF, in drug Rac1 pathway Rolfo C, Menna PL.Relevance small GTPase of Figure 1. are colored Model Gaussian Network residues by hinge slowest mode as green. References: Rac1: GTPase Small P. Jordan Scherer SW, Gespach C, Boavida MG, with an experimental study to confirm the restored Mg2+ and GTP GTP and Mg2+ restored the confirm to study experimental an with mutant cases. affinities in Keywords: Dynamics, Dynamics, Internal G proteins, Protein Drug Design, Protein Mutagenesis, Biology, Computational Selectivity, Functional Control, Cancer Functional Rho GTPases, Function, regulatory/effector binding residues in gaining fast cycling property. residues binding regulatory/effector in cycling fast gaining property. Overall, rescue to identify framework a study proposes this positions be complemented the results would mutations and for the oncogenic Duhovny D, Wolfson HJ, Nussinov R, Haliloglu T. - in silico identification of sites in rescue Department of of Department [4] . The Polymer Research Center and Chemical Engineering Department, Bogazici University University Bogazici Department, Engineering Chemical and Center Research Polymer mechanistically mechanistically

1 2 hanisms: Opportunities in cancer therapeutics. cancer in Opportunities hanisms: ” state ” R. Ozguney Busra ntriguingly Rac1 isthe gain I PubMed

experimental study experimental Mutagenesis,Computational Biology, Protein Function, . and

J, Marques B, Beck S, Veríssimo F, Gespach C, Boavida MG, Scherer SW, Jordan P. Jordan SW, Scherer MG, Boavida C, Gespach F, Veríssimo S, Beck B, Marques J, 81.

- maintains its function via Overall, this study proposes a framework to identify rescue positions for the oncogenic mutations and the results would be would results the and mutations oncogenic the for positions rescue identify to framework a proposes study this Overall,

. In addition to that

. cleic AcidsRes. 2010 Nov;38(20):7079 electivity, mutations 29S active structure (a) S (a) structure active 29S

S [2] Nu Proteins. 2008 Mar;70(4):1219 2008 Proteins.

resistance mec either important in the Rho switch m switch Rho the in important either P

: Biomolecules, Allostery, Molecular - cooperativity glu T, Ivet B, Erman B. B. Erman B, Ivet T, glu to rescue the effect of the the of effect the rescue to guanine nucleotide exchange factors exchange nucleotide guanine .

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related C3 botulinum toxin substrate 1 (Rac1) substrate toxin botulinum C3 related .

Emekli U, Schneidman U, Emekli Baronio R, Danziger SA, Hall LV, Salmon K, Hatfield GW, Lathrop RH, Kaiser P. Kaiser RH, Lathrop GW, K, Hatfield Salmon LV, Hall SA, Danziger R, Baronio Bahar I, Atilgan AR, Erman B. Erman AR, Atilgan I, Bahar Matos Skaug P, - Cardama GA, Alonso DF, Gonzalez N, Maggio J, Gomez DE, Rolfo C, Menna PL. Menna C, Rolfo DE, Gomez J, Maggio N, Gonzalez DF, Alonso GA, Cardama Halilo De P, Aske JC, Dey N. RAC1 Takes the Lead in Solid Tumors. Solid in Lead the Takes RAC1 N. Dey JC, Aske P, De

is study, proposewe a novel approach unctional spontaneously activated Towards

Corresponding author’s address Bogazici Center, Research and Polymer Engineering Chemical of Department structures. [6] [7] [5] Des. 1997;2(3):173 [4] mutations. and radio and 29548483 [2] [3] [1] localization, and expressionof the human gene. green. Figure 1 Figure Keywords F complemented with an an with complemented residues29 and 30 are not totally recovered. property cycling inactive, the other state deviates significantly. deviates other state the inactive, P29S of effects the however active, P29S of states both in observed residues binding effector regulatory/downstream and GTP P29S active takes at least two states states two least at takes active P29S and their mutant active Rac1 structures are analyzed by Molecular Dynamics (MD) Simulations. Mutations are observed to generally pertur generally to observed are Mutations Simulations. (MD) Dynamics Molecular by analyzed are structures Rac1 active mutant are which dynamics related to protein’s function. protein’s to related dynamics dynamic their and perturbations as structure Rac1 mutant P29S of The rescue positions at/in close vicinity to the mechanistically informative regions are are regions informative mechanistically the to vicinity close at/in positions rescue The shift type th designed oncogenic mutations in “ interacts with its effectors its with interacts (GAPs), metastasis superfamily, Rac1 Ras adhesion and migration [1]

POSTER PRESENTATIONS 178 Bioengineering, SabancıUniversityOrhanlı,Tuzla, 34956,Istanbul 1.Faculty ofEngineeringandNaturalSciences,MolecularBiology,Genetics Cem Azgari,DefneÇirci, EXONS PREFERENTIAL NUCLEOTIDE EXCISION REPAIR IN Turkey Tel.: [email protected]. 216-568-7043;E-mail: Sciences, Tuzla, Istanbul, 34956 be addressed: Sabancı University, Faculty of Engineering and Natural Address: Author’s Corresponding 27688757. 2016Oct 11;113(41):11507-11512. Pubmed PMID: USA. Acad Sci resolution.Proc Natl maps ofthehumangenomeatsingle-nucleotide [3] HuJ, LiebJD, Adar S. CisplatinDNAdamage and repair SancarA, 1;29(9):948-60. PubmedPMID:25934506. Dev.resolution. Genes damage atsingle-nucleotide 2015 May excisionrepairofUV of humanglobalandtranscription-coupled [2] HuJ, AdarS, SelbyCP, LiebJD,analysis Genome-wide A. Sancar 1674. PMID:29186127 repair.differential mismatch Nat Genet. 2017 Nov 29;49(12):1673- References: Damage-Seq; Cisplatin; Oxaliplatin;Bezno[A]Pyrene;(6-4)Pps; CPDs; XR-Seq; Keywords: cancer types. some mutation burdenin light intointronic shed Our findings exons. likely to be due to thedifferentialhistonemarkersbetweenintronsand is suggest thatthepreference The results the poorly repairedgenes. repair. On the contrary, the exonic preferenceismoreprominent in repair is independent of transcription and transcription-coupled compared tointrons.We foundthatthebiasinnucleotideexcision exons arenot damaged but differentially are repaired moreefficiently seq based techniques XR- damage datasets, generatedwithrecentNGS- repairand intronic andexonicregionsby analyzing thegenome-wide bezno[a]pyrene. Here,we aim to between reveal differencesthe for bulky-adduct inducingdamaging agents suchasultravioletand within samegene repair levels nucleotide excision differential implies are less prone to a selection.[1]Such fact in skinand lung cancer transcription. However, there isa similar trend in tumor samples which exons compared to introns which are splicedout in the process of at amino acidlevel,alowerrateofnaturalvariantsareobservedin ofhazardousmutationsencoded effect of theeliminating Because contribution to the proteinsequenceandthereforefunction. direct informational due totheir tolerable comparedtointrons less regions wheremutagenesisis Exons areevolutionaryconserved [2] andDamage-seq Nucleotide ExcisionRepair; Exonic And Intronic Region; [1] Frigola, J, etal.Reducedmutation rate inexonsdueto Zeynep Kılınç,BerkTurhan [3] , respectively.suggests that Ourresearch Tomay whom correspondence andOgünAdebali 1 POSTER PRESENTATIONS 179 1,5 , Nurcan Tuncbag , Nurcan 3,4 , Ozlem Keskin 2,4 3D Mutation Patch; Protein-Protein Interactions; Patient- Protein-Protein Patch; 3D Mutation , Atilla Gursoy 1 We believe that from mutations to networks and eventually to clinical believe networks and eventually to from mutations to that We perspective in the novel provides a this study data, therapeutic and precision medicine. network-guided Keywords: Medicine Specific Network Modelling; Precision patient heterogeneity while network guided analysis analysis patient heterogeneity while network guided classified patients into five similaritygroups based on These in their affected pathways. mutation patches and each patient groups have a set of signature survival. By integrating has a significant association with patient group patches, we inferred potential signature through sensitivity data drug therapeutics for each group. According to our results, patient can be effective for Group 3 yet Group 2 can be resistant Pazopanib phosphorylation. PTEN of mediator is a which ATM of inhibition to interaction responses networks, and drug of the GBM cell lines. In conclusion, 10% of the mutations are located we found approximately in patches. Oncogenes statistically tend to have multiple patches have suppressors prone to are relativelysmall, whereas tumor that different patches Moreover, a small number of very large patches. can different domains that at in the same protein are often located the inter- patches decreased mediate different functions. Mutation therapy and the survival has been remained very low. In this study, this study, In very low. remained been survival has the and therapy among GBM patients from decrease the heterogeneity we aimed to propose classify the patients and Atlas (TCGA), The Cancer Genome mutation patient by using therapeutic hypothesis groups for patient systems level therefore implemented a using approach profiles. We three dimensional organization of the mutations (mutation (3D) spatial in patches), organization of mutated proteins patient specific protein Molecular alterations on genome accumulate through time and on genome accumulate through Molecular alterations One of diseases like cancer. lead to cellular functions and disrupt Glioblastoma Multiforme is well the deadliest of brain tumor, type which makes the disease as heterogeneity, known for its molecular still have not been efficiently for precision grouped incurable. Patients 5. Cancer Systems Biology Laboratory (CanSyL-METU), Ankara, Turkey 5. Cancer Systems Biology Cansu Dincer Turkey Informatics, METU, Ankara, Graduate School of of Health Informatics, 1.Department Turkey University, Istanbul, Engineering, Koc of Chemical and Biological 2. Department Engineering, Koc University, Istanbul, Turkey 3. Department of Computer Koc University, Istanbul, Turkey Medicine (KUTTAM), Translational 4. Research Center for 3D Spatial Organization and Network Guided and Network Organization Spatial 3D Mutations GBM of Comparison POSTER PRESENTATIONS 180 http://mistral.ii.metu.edu.tr/ http://mistral.ii.metu.edu.tr/ URL: [email protected] E Cankaya/Ankara B Institute Informatics University Technical East Middle Office Address: 1. Figure Assoc. Dr. Nurcan Tuncbag Corresponding author’s address respons ne interaction protein specific patient in proteins mutated of organization patches), (mutation mutations the of organization patient using by groups patient the heterogeneity among GBM patients from The Cancer Genome Atlas (TCGA), classify the patients and propose therapeutic hypot precision for grouped efficiently been not still have incura as disease the makes which heterogeneity, molecular its for known well is Multiforme Glioblastoma tumor, brain of type Molecular alterations on genome accumulate through time and disrupt cellular f Keywords: guided precision medicine. believe that from mutations to networks a Pazopanib can be effective for Group 3 yet Group 2 can be resistant to inhibition of which ATM is a mediator of PTEN phosphor integrating drug sensitivity data through signature patches, we infe pathways. These patient groups have a set of signature mutation patches and each group has a significant association with pat lar very decreased the of inter- number small a have to prone suppressors differentfunctions.tumor mediate can that differentdomains atlocated often areprotein same the indifferent patchesMoreover, whereas small, relatively are that patches multiple have to tend URL:http://mistral.ii.metu.edu.tr/ [email protected] Cankaya/AnkaraE-mail: B-204 Office Address:MiddleEastTechnical UniversityInformaticsInstitute Address: Corresponding Author’s Figure 1. - - mail: mail: 204 3D Spatial Organization and Network Guided Guided Network and Organization Spatial 3D

es of the cellGBM lines. In conclusion, we found approximately 10% of the mutations are located in patches. Oncogenes statis

Overview of the Method. 3D Mutation Patch; Protein Patch; Mutation 3D OverviewoftheMethod.

patient patient heterogeneity while network guided analysis classified patients into five

Cansu Dincer Cansu

1 ComparisonGBMof Mutations 4 Department of Health Informatics, Graduate School of Informatics, METU, Ankara, Turkey Ankara, METU, Informatics, of School Graduate Informatics, Health of Department 2 Research Center for Translational Medicine (KUTTAM), Koc University, Koc Istanbul, Turkey (KUTTAM), for Medicine Center Translational Research

Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey Istanbul, University, Koc Engineering, Biological and Chemical of Department

mutation profiles. We therefore implemented a systems level approach using three dimensional (3D) spatial (3D) dimensional three using approach level systems a implemented therefore We profiles. mutation 3

Department of Computer Engineering, Koc Univers Koc Engineering, Computer of Department

Cancer Cancer Systems Biology Laboratory (CanSyL - Protein Interactions; Patient Interactions; Protein 1 nd eventually to clinical and therapeutic data, this study provides a novel perspective in the network , Atilla Gursoy

therapy and the survival has been remained very low. In this study, we aimed to decrease

2,4 Assoc. Dr. NurcanTuncbag , Ozlem rred potential therapeutics for each patient group. According to our results, - Specific Network Modelling; Precision Medicine Medicine Precision Modelling; Network Specific

Keskin unctions unctions and lead to diseases like cancer. One of the deadliest - METU), Ankara,METU), Turkey 3,4 ity, Istanbul, Turkey Istanbul, ity, , NurcanTuncbag , groups groups based on similarity in their affected

1,5

Mutation patchesMutation tworks, and drug and tworks, ient survival. By ble. Patients Patients ble. ylation. We We ylation. ge patches. ge hesis hesis for tically tically -

POSTER PRESENTATIONS 181 Department of Chemistry, Izmir Chemistry, of Department [1] Zheng, Weiming, and Lin He. 2014. “Quantitative “Quantitative Lin He. 2014. and Weiming, Zheng, [1] Erman KIBRIS, and Nuran ELMACI IRMAK and Nuran ELMACI Erman KIBRIS, Polymer, DNA, Polyelectrolytes, Cationic Polythiophene, Cationic Polythiophene, DNA, Polyelectrolytes, Polymer, Corresponding Author’sAddress: [email protected] 35430 Urla İzmir. Institute of Technology, Sébastien Richeter, et al. 2015. Soft Matter 11 Mechanisms of DNA with Cationic Polythiophene.” “Self-Assembly and Hybridization (32): 6460–71 Carlos Aleman. and Eric A. Perpete, Julien, David Zanuy, [3] Preat, DNA: to Atomistic Polymers Cationic Conjugated “Binding of 2011. Dodecamer.” Simulations of Adducts Involving the Dickerson’s Biomacromolecules 12 (4): 1298–1304. References: Measurements of Thermodynamics and Kinetics of Polythiophene– Detection.” Biomaterials Sciencein DNA 2 Complex Formation DNA (10): 1471. [2] Rubio-Magnieto, Jenifer, Knoops, Elias Stefan Knippenberg, Gebremedhn Cécile Delcourt, Azene, Amandine Thomas, Jérémie by comparison of quantum chemical calculations and molecular chemical calculations and quantum comparison of by mechanical calculations. The interactions between the oligothiophene S-Hand the ssDNA (electrostatic N-O, / O-H bonds, etc.) were change on determine the nature of the conformational analyzed to the oligothiophene when ssDNA is added. Keywords: Chemistry Molecular Dynamic Simulation, Computational In this study, molecular dynamics simulations of complexes formed molecular dynamics simulations In this study, oligothiophene and differentnucleotides ssDNA chains with by enlighten the to performed were side groups containing cationic experimental studies. The essential thing for a molecular is simulation this purpose, the the force field definition of the system of interest. For force field which parameters of oligothiophene are not present in the generated (CHARMM, AMBER, GROMOS), were current databases the red when single-strandedor the color DNA (ssDNA) is added, changes in the solution are visible they can be to the naked eye, so that DNA cleavage and theranostic reaction sensor, used as a tool for DNA by The red shift or color change was explained polyplex applications. conformational change a the ssDNA molecule leads to the fact that known of structural change is not the form but on the polythiophene, [1-3]. etc.) stacking, twisting, clearly (i.e. flattening, The absorption spectra of the cationic polythiophenes are shifted to the cationic polythiophenes of spectra The absorption POLYELECTROLYTE (CATIONIC OLIGOTHIOPHENE) (CATIONIC POLYELECTROLYTE BARBAK, Nehir NALINCI MOLECULAR DYNAMICS SIMULATION STUDY ON THE ON STUDY MOLECULAR DYNAMICS SIMULATION CONJUGATED A DNA& BETWEEN INTERACTIONS POSTER PRESENTATIONS 182 4. BilkentUniversity,DepartmentofPsychology 3. BilkentUniversity,DepartmentofMolecularBiologyandGenetics 2. ZonguldakBülentEcevitUniversity,DepartmentofMolecularBiologyandGenetics 1. BilkentUniversity,DepartmentofNeuroscience Toulopoulou Kübra Çelikbaş FOR SCHIZOPHRENIA EARLY ADULTHOOD PERIOD ANDIMPLICATIONS FRONTAL CORTEX DURING LATE ADOLESCENCE- ALTERNATIVEDYNAMIC SPLICING INTHE EVENTS 1,4 alternative splicing complexityinthehuman transcriptome byhigh- [2] PanO, Q,Shai LeeLJ, Frey BJ,BJ. Blencowe of Deepsurveying 456(7221),470–476. PubMedPMID:18978772 regulation in humantissuetranscriptomes.Nature. 2008 Nov 27; MayrC,KingsmoreSF,L, GP, Schroth &BurgeCB.isoform Alternative References: Schizophrenia; Frontal Cortex Keywords: pathways. involved inthedisease-related but they are can beimportant candidates since schizophrenia-related implicated tobe are notpreviously which also foundnovelgenes in the diseasecontext.Wegenes maybeanimportant mechanism association studies(GWAS), and our results suggestthat DEU ofthese which werepreviously associated with schizophrenia by genome-wide genes (PCDH9,ANKR39) genes therearesomeschizophrenia-related during LAEAperiod comparedtootherperiods.Amongthese changes many neuropsychiatric disorder-related genesshowdynamicAS in severalneuropsychologicaldisorders.Ourresultsrevealed that focused onthefrontal cortex regionsinceitsimportanceisimplicated Wethe neuropsychologicaldisordersfirstappear during LAEA. only childhood, and young, middle and late adulthood since manyof and compared the DEUdata of this period to early infancy, early adulthood (LAEA) period brain samplesduringlateadolescence-early the changesinDEUofnormal by AltAnalyzeandR,investigated available AffymetrixHumanExon1.0 ST arraydata (GSE25219) different lifeperiods. In this study, we have analyzed the publically no study investigating ASchangesinthebrainofanindividualduring neurontypesand function of specific during neurogenesis, thereis of ASinthe roles are manystudiesinvestigatingthepossible there dysregulated or autismif as schizophrenia such disorders to the complexityof the brain, AS may lead to neuropsychiatric number of AS eventscompared to other tissues undergoes AS genes recent studies,themajority (92-94%) of all humanmulti-exon the genomeby generating multipleprotein isoforms. According to of and itcontributesto the diversity process aftergeneexpression Alternative splicing(AS)or differential exonusage (DEU) isa regular 1 , KeremMertŞenses Alternative Splicing; Differential Exon Usage; AlternativeSplicing;Differential [1]Wang ET, SandbergR,Luo S, Khrebtukova I, Zhang [1] andbrain,especiallytheneocortex,hashighest 2 , AliOsmayGüre 1,3 [2] , Timothea . While contributing [3] . Although [4]

POSTER PRESENTATIONS 183 disease. Neuron. 2006;52(1):93–101. PubMed PMID: 17015229 PubMed PMID: 2006;52(1):93–101. disease. Neuron. Spatio-al. et Y Zhu Cheng F, YI, Kawasawa HJ, Kang [4] Oct 2011 Nature human brain. transcriptome of the temporal PMID: 22031440 PubMed 26;478(7370):483-9. throughput sequencing. Nat Genet. 2008 Dec; 40(12):1413-5. 40(12):1413-5. Dec; 2008 Genet. Nat sequencing. throughput 18978789 PMID: PubMed in neurologic regulation Splicing RB. Darnell DD, [3] Licatalosi POSTER PRESENTATIONS 184 1. İzmirInstituteofTechnology, BioengineeringDepartment Ekin KestevurDoğru BIOCATALYSIS A THERMOPHILIC P450FOR INDUSTRIAL BIOINFORMATICS BASED APPROACH TO DESIGN two differentpositions ofprogesteroneinCYP119. 1. Figure Keywords: selective hydroxylationofprogesterone substrate bindingand mutants willbedesignedtoobtain efficient was performedwithusingdoublemutant Triple/quadrupleenzymes. double mutants process and secondroundofdocking elimination Best mutantswereusedforcreating according totheirenergyscores. to two differentpositionsinP450s. Dockingresultswereeliminated progesterone canbind coordinates ofprogesteronefordockingsince using DockMCM Protocol of PyRosetta. We used two different starting was performed by Met, Ala, His, Arg and Ile. Progesterone-docking 257, 354) and mutated with PyRosetta program (69,151, were selected 153, 155, 205, 208, 209, 213, 214, 254, P450s. Finally,residues withprogesterone-hydroxylating 12 residues [2] that create with clashes substrate. We also used COACH-D server performed with CYP119 to identify residues Progesterone-docking other CYPs that can catalyzeprogesteronehydroxylation naturally. with that willbemutatedaccordingtostructuralalignment residues Crystal structureofCYP119 (PDB ID:1F4T)wasusedforselecting and aldosterone. cortisone like of importanthormones molecules is not the originalsubstrate of CYP119, for production of precursor to utilize CYP119 hydroxylation for selective of progesterone, which temperature andlowpHconditions at high be usedasbiocatalyst for industrialproduction it showsactivity since P450 can from Sulfolobusacidocaldarius,which acidothermophilic andefficiency.carbon bondswithhighselectivity an CYP119 is structures becausetheycancatalyzetheoxidation of inactive are important for hydroxylationofsteroid (P450) monooxygenases process forpharmaceuticalmanufacturing. CytochromeP450 of steroidaldrugsisanimportant Enzyme catalyzedbiosynthesis for ligand-binding site predictionand selected consensus binding like cortisone like progesterone of hydroxylation production industrial high selectivity andefficiency. monooxygenases are important for hydroxylation of steroid structures because they can catalyze the oxidation of inactive c b catalyzed Enzyme e Gülbahçe/ İzmir IZTECH, Bioengineering Department, K003 Adress: 1;26(5):689 Mar 2010 Bioinformatics [ docking. molecular J. Yang Y, Zhang Z, Peng Q, Wu [2] solfataricus: high resolution structure and functional properties. Park[1] SY, Yamane K,Adachi S, Shiro Y, Weiss KE, Maves SA, Sligar SG.Thermophilic cytochrome P450 (CYP119) from Sulfolobu References: 1 Figure Keywords: mutants double bind to two different positions in by 354) 257, 254, 214, consensus bindingresidues substrate. with clashes create that residues identify to naturally. hydroxylation progesterone catalyze can CYPs that other with alignment structural 3 - mail: [email protected] ] Chaudhury ]

using DockMCM Protocol of PyRosetta. PyRosetta. of Protocol DockMCM using Thermophilic P450 for Industrialfor Biocatalysis P450 Thermophilic 1 Bioinformatics Based Approach to Design a a Design to BasedApproach Bioinformatics mutants

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POSTER PRESENTATIONS 185 IZTECH, Bioengineering IZTECH, [1] Park SY, Yamane K, Adachi S, Shiro Y, Weiss KE, Weiss K, Shiro Y, S, Adachi Yamane SY, [1] Park for implementing molecular modeling algorithms using Rosetta. for implementing molecular PMID: PubMed Bioinformatics 2010 Mar 1;26(5):689-691. 20061306. Corresponding Author’s Address: İzmir Department, K003 Gülbahçe/ e-mail: [email protected] properties. J Inorg Biochem. 2002 Sep 20;91(4):491-501. Sep 20;91(4):491-501. J Inorg Biochem.properties. 2002 PMID:12237217. protein– improved COACH-D: J. Yang Z, Y, Zhang Q, Peng [2] Wu poses refined ligand-binding prediction with binding sites ligand Nucleic Acids Res. 2; 46: 2018 Jul docking. through molecular 29846643. W438–W442. PMID: interface script-based a PyRosetta: JJ. Gray S, Lyskov S, Chaudhury [3] References: SA,Maves Thermophilic (CYP119) P450 cytochrome Sligar SG. functional and resolution structure solfataricus: high from Sulfolobus POSTER PRESENTATIONS 186 1. MiddleEastTechnical University Muazzez ÇelebiÇınar LEARNING TECHNIQUES PROTEIN-COUPLED RECEPTORS USING MACHINE PREDICTION OF TRANSMEMBRANE REGIONS OF G mfHydrophobicity and17-residueslidingwindows generated using is performed with thetrainingset,which Table1: Keywords: further potentialtherapeuticopportunitiesformanyseverediseases. by openingup drugdiscovery and facilitatestructure-based research higher truepositiverate. This study can shedlightonother scientific server. Withthisalgorithm,weobtainmorethan85\%accuracy with separately [6].Thesecondarystructuresarederivedfrom the JPred Fleming andKyte-Doolittle are implemented hydrophobicityscales both Moon- hydrophobicity scale, structure information. As secondary on hydrophobicity of each aminoacidintheproteinsequenceand the learning techniques.Thealgorithmisbased algorithm usingmachine study,thesis this wetrytodevelopaGPCR-specificTM prediction determined GPCRs.With of TMregionsfor6the29experimentally Markov models [5]. However, TMHMM fails to predict the total number provided usingtheTMHMMpredictiontool,whichisbasedonhidden data[4]. The topology information of other membrane proteinsis thousands have experimentallysolvedtransmembrane(TM) region and structuralfunctionalinformation,only29GPCRsamongthe UniProt, which isa freely availabledatabase of protein sequences of themembrane.In resulting fromunstableenvironment limitations protein topologyand experimental arelackingowingto the technical are located[3].Dataonmembrane binding sites and cholesterol actuator protein(e.g.as ligandbindingsites, Gprotein)bindingsites regions such where essential transmembrane helices they haveseven times. Structurally, membraneseven industry[2]. AGPCRspansthecell proclaimed asoneofthemostimportanttargetsforpharmaceutical such as cancer and central nervoussystemdisorders,and can be humans in of diseases receptors areassociatedwithavariety These including hormones,neurotransmitters,odorants, photons, and ions. ligands of extracellular a diversity GPCRs canrecognize changes. extracellular stimuliinsidethecellby undergoing conformational significant membranereceptorfamilyineukaryotes[1].Theytransmit receptors (GPCRs)areoneof the largestand the most G protein-coupled Corresponding author’s address author’s Corresponding [6] [5] PMID: [4] 1265 [3] cells, [ forest. random [1] References: generated usingmfHydrophobicity and 17 1. Table Keywords sev many for opportunities therapeutic potential further up structure opening by facilitate discovery drug and research scientific other on light shed can study This rate. positive true higher Wi server. JPred the from derived are structures Moon amino acidthe in protein sequencesecondary and the structure information.Ashydrophobicity scale, both prediction algorithm usingmachine learningtechniques. The algorithmbased is hydrophobicityon ofeac of the 29experimentally determinedGPCRs. Withthis thesis study, is we try todevelopwhich a GPCR tool, models Markov prediction hidden on TMHMM based the using provided is proteins membrane other of information topology information, functional and on structural and sequences protein of database available freely a is which UniProt, owing to the technical and experimental limitations resulting from unstableprotein) environment G (e.g. of the membrane. protein In actuator bindingsites, sites binding and cholesterol ligand binding as sitessuch are located regions essential where helices transmembrane industry pharmaceutical and cancer as such humans in central nervousdiseases system of disorders, variety a with and can be proclaimed associated are as one of the receptors most important These ions. targets and for the photons, GPCRs can recognize a diversity of extracellular ligands including hormones, neurotra in eukaryotes protein G T R R P [email protected] 2 ] ly 29 GPCRs among the thousands have experimentally solved transmembrane (TM) region data region (TM) transmembrane solved experimentally have thousands the among GPCRs 29 ly

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based . The . The h POSTER PRESENTATIONS 187 [email protected] [1] Liao, Z., Ju, Y., & Zou, Q. (2016). Prediction of G of Prediction (2016). Q. Zou, & Z., Liao, [1] Y., Ju, research, 2016, 45.D1: D158-D169. PubMed research, 2016, 45.D1: PMID: 27899622. [5] https://www.uniprot.org/help/transmem [6]https://www.cgl.ucsf.edu/chimera/docs/UsersGuide/midas/ hydrophob.html Corresponding Author’s Address: [2] Zhang, D., Zhao, Q., & Wu, B. (2015). Structural studies of G Structural (2015). B. Q., & Wu, Zhao, D., [2] Zhang, protein-coupled PubMed 836. cells, 38(10), Molecules and receptors. PMID: 26467290. G-protein-coupled of Oligomerisation (2001). Milligan, G. [3] PubMed 1265-1271. Cell Science, 114(7), of Journal receptors. PMID: 11256993. the universal acids protein knowledgebase. Nucleic [4] UniProt: References: protein-coupled and features with SVM-prot receptors PubMed PMID: 27529053. Scientifica, 2016. random forest. POSTER PRESENTATIONS 188 Genome Campus,Hinxton,Cambridge,CB101SD,UK. 2. EuropeanMolecularBiologyLaboratory,BioinformaticsInstitute,WellcomeTrust Turkey. 1. DepartmentofBiologicalSciences,MiddleEastTechnical University,06800,Ankara, Handan MelikeDönertaş Ulaş Işıldak HUMAN BRAIN GENE EXPRESSION HETEROGENEITY INAGING 1 g) bt o i dvlpet 0 o 0 er o age). of years 20 to with associated are aging during increase heterogeneity (0 development in not but age), w lifespan, specializati longevity to individuals time, over expression gene of deregulation accumulatio gradual a by accompanied mechanisms, repair and maintenance in decline gradual by characterized is Aging heterogeneity mechanism. increase, heterogeneity with associated Figure 1. Figure Keywords process not is heterogeneity increased Tel: human human brain. Nature. 2004;429(6994):883 [1] References: E Campus, Hinxton, Cambridge, 1SD, CB10 UK. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Corresponding author’s address age. Current Biology. [2] , MehmetSomel - Figure 1. Heterogeneity; Human;Brain Keywords: specific effectoftheagingprocess. by thegeneraleffectoftime,but it isa heterogeneity isnotonlydriven pathways. Welifespan and disease-related alsoshowedthat increased heterogeneity,gene expression associated withmultiple is which results showed that human brainaging is associatedwithincreased our Overall, regulation mightberelatedtounderlyingmechanism. that gene implying increase, associated withheterogeneity positively also and transcriptionfactors)is number ofregulators(miRNAs (e.g. axonguidance,postsynapticFurthermore, specialization). the to longevity (e.g. autophagy, mTOR signaling)and neuronal function that arerelated associated withthepathwaysandbiologicalprocesses genes showingconsistentheterogeneityincreaseduring aging are but not indevelopment(0to20yearsofage).We alsofoundthat the gene expressionheterogeneityduringaging (20 to 98 years of age), regions covering whole lifespan,we observed a in consistent increase human brain By analyzing19 transcriptome datasetsacrossdiverse heterogeneity betweenindividuals[2]. increased geneexpression over time,leadingto deregulation ofgeneexpression stochastic genetic and epigenetic alterations [1]. This accumulationmay cause accompanied byagradual accumulation ofstochastic mechanisms, Aging is characterizedby gradual declineinmaintenanceand repair mail: mail: 2 Ulaş Işıldak Lu T, Pan Y, Kao S Kao Y, Pan T, Lu Somel M, Khaitovich P, Bahn S, Pääbo S, Lachmann M. Gene expression becomes heterogeneous with with heterogeneous becomes expression Gene M. Lachmann S, Pääbo S, Bahn P, Khaitovich M, Somel

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POSTER PRESENTATIONS 189 European Molecular Biology Molecular Biology European [1] Lu T, Pan Y, Kao S-Y, Li C, Kohane I, Chan J, et Chan J, I, Li C, Kohane S-Y, Kao Y, Pan T, [1] Lu Tel: + 44 (0) 1223 49 4363 + 44 (0) Tel: [2] Somel M, Khaitovich P, Bahn S, Pääbo S, Lachmann M. Gene M. Lachmann S, Pääbo S, Bahn P, Khaitovich Somel M, [2] expression Biology. with age. Current heterogeneous becomes 16713941. PubMed PMID: 2006;16(10). Corresponding Author’s Address: Trust European Bioinformatics Institute, Wellcome Laboratory, UK. Cambridge, CB10 1SD, Genome Campus, Hinxton, E-mail: [email protected] References: human brain. ageing in the DNA damage and regulation al. Gene 15190254. PubMed PMID: Nature. 2004;429(6994):883–91. POSTER PRESENTATIONS 190 2. BioinformaticsGraduateProgram,MuglaSitkiKocmanUniversity,Mugla,Turkey, 48000 1.Department ofComputerEngineering,MuglaSitkiKocmanUniversity,Mugla,Turkey, 48000 Basak Abak TCGA DRUG SUMMARY INTERFACE 1 , Tugba Onal-Suzek [email protected] [email protected] Address: Corresponding Author’s [2] https://www.ncbi.nlm.nih.gov/pcassay/ vignettes/TCGAbiolinks/inst/doc/clinical.html References: Keywords: Interface wasdevelopedusingRshinypackages. name and downloading the summarytable in csv format. Web User interface, ouralgorithmprovidestheuserfilteringoptionsbydrug user was incorporatedtothedatatable.Atbackendofthis drug. Most active protein tarrget from PubChem BioAssay database fromthePubChem BioAssay javascript pop-ups to scraptheresultsof scripts and javascript NCBI EutilsRpackages drug, themostactiveproteintarget nameisdeterminedby using the therapy drugs.Asthenextstep,foreach immunotherapy/molecular the chemotherapy/ bcr_patient_barcode andtumortypefiltered database. We joinedthesetwo dimensions of data based on the patients aredownloaded from TCGA (TheCancerGenomeAtlas) cancer’s and drugdataofeach clinical first, interface, generate this drug, informationandsmokingstatuswerelisted.To thesurvival when aon user clicksthe chosen drug, the list of patients with their Fordrug howmanypatientssurvived. 2) andforeach purpose, this enabling thedownload of a summary table of 1) thelistof drugs type inTCGA,for eachcancer interface a userfriendly toimplement TCGAtexts in are embeddedasfree study was, data. Our aimforthis results. However,significant records thedrug names intheclinical and drugs,theinformationprovidedisenoughto get statistically therapeutics. AlthoughTCGAtimes sparse data for survival contains and in- silicohypothesisgeneration towards development of new TCGArepositories like pipeline development arevitalforin-house data and matchednormaltissue.Publicpatientbasedgenomic tissue TCGA[1]available resourceof11,000 isapublicly patientswithtumor Abstract: TCGA; DrugData;PubChemBioAssay;R; Javascript [1]https://bioconductor.org/packages/release/bioc/ 1,2 2 database for each POSTER PRESENTATIONS 191 1,2 which is a customized [1] force-directed algorithm to visualize Rengül Çetin Atalay Rengül Çetin

[2] , 2 Mehmet Volkan Atalay Mehmet Volkan

, 1 promises, now we can reach better results to draw biological graphs edge crossings to the size of the drawing area. Overall relative fitness values are used to choose parent individuals. have applied our study to 3 pathways and the results better are We certain some EClerize with respect to than those of the base study measurable fitness criteria with a reasonable longer execution genetic algorithm as optimum, global perspective of the time. From a limited area or selected edges/vertices limited area or according to are exchanged a the the routines of the selected mutation technique. In the crossover, operation of exchanging vertices is performed between two selected the aesthetic criteria of undirected graphs are study, In our graphs. served as fitness measurements GA.of In each iteration, to measure fitness values of graphs are calculated how well graphs are drawn, by 6 different fitness measurements ranging from the number of graph drawing. By use of a well-known heuristic technique, Genetic drawing. graph with drawing layout undirected graph Algorithm(GA), we integrate have the previous used study EClerize a fine-tuneras on GA.GA. We the diversity for providing The genetic algorithm draws strength from and the crossover global optima, and the mutation are the most 5 techniques in the In our study, important resources diversity. of the phase and 2 techniques in the crossover mutation phase are employed. vertices In mutation, of a selected graph are moved randomly within In our previous study, we described EClerize In our previous study, Kawai and improved Kamada as EC numbers. EClerize contain nodes with attributes that pathways creates clusters of nodes with enzymes that belong to the same EC GA, our purpose is to avoid the class. Here, in our study EClerize Type optimum solutions for EClerize global during and obtain local optima vertices that represent the enzyme structure. In these type of graphs, graphs, of these type In enzyme structure. represent the vertices that information some vertices which have clustering be there could which is the numerical Enzyme Commission to (EC) number, dedicated classification enzyme. With the help of these EC numbers, of an the same EC clustering can be conducted. The vertices belong to that distance the with proportional same cluster, the members of class are in the distance tree. Graphs can be used to represent various domain-specificrepresent used to be can Graphs information. By using vertices and edges to represent items to symbolize the data the relation between these items, various type of information can the graphs. Biological be represented by which graphs consist of genes, proteins, and enzymes; components such as have great contain Some of the biological graphs importance in bioinformatics. TECHNIQUES FOR BIOLOGICAL GRAPHS BIOLOGICAL FOR TECHNIQUES Fırat Aksoydan Ankara, TURKEY University, Middle East Technical of Computer Engineering, 1,2.Department 06800, Ankara, Turkey University, Informatics, Middle East Technical 3. Graduate School of A CUSTOMIZED FORCE-DIRECTED LAYOUT FORCE-DIRECTED A CUSTOMIZED ALGORITHM GENETIC WITH ALGORITHM POSTER PRESENTATIONS 192 Corresponding Author’s Address: Corresponding Author’s undirected graphs.Inform.Process. Lett., 31:7,15,1989 [2] T. Kamada andS. Kawai. Analgorithmfordrawinggeneral Biology, Vol. 16,No.04,1850007(2018). with EC attributes”, Int. Journal of Bioinformatics and Computational graph drawing algorithm for biological customized force-directed References: Layout, EnzymeCommissionNumbers,GeneticAlgorithm Keywords: algorithms, however, noneofthemconsidersclustering. combine GAwithsomeforce-directed studieswhich There areseveral altogether.techniques clustering algorithmandsome a force-directed of aGA, the firstonewithimplementation knowledge, ourworkis Fromof thegraph drawing theperspective studies to the bestofour attributes. are associatedwithEnzymeCommission whose vertices 1 address author’s Corresponding 1989 31:7,15, [2] (2018). 1850007 No. 04, 16, Vol. Biology, Computational and Bioinformatics of Journal Int. attributes”, EC with biological for [1] References: Genetic Algorithm Keywords the graph drawing studies to the best of our knowledge, our work is the first one with one first the is work our knowledge, our best of the to studies drawing graph the of perspective From the attributes. Commission Enzyme with associated are vertices whose graphs biological genet as optimum, global of perspective the From time. execution longer areasonable with criteria fitness measurable some certain to respect We have applied our study to 3 pathways and the results are better than those of the base study EClerizewith fitness values are used to choose parent individua relative Overall area. drawing the of size the to crossings edge of number the from ranging measurements iteration, our study, the aesthetic criteria of undirected graphs are served as fitness measurements of GA.In each technique.In the crossover, the operation of exchanging vertices is performed between two selected graphs. In limited area or selected edges/vertices are exchanged according exchanged are edges/vertices selected or area limited a within randomly moved are graph selected a of vertices mutation, In employed. are phase crossover the in the most important resources of the dive global providing for diversity the from strength draws algorithm combine GA with some force GA, aforce layout drawing with GA. We have used the previous study EClerize as a fine drawing. By use of awell graph during EClerize for solutions optimum global obtain and optima local the avoid to is purpose our creates clusters ofnodes withenzymes th force In our prev same cluster, proportional with the distance in the distance tree. the of members are class EC same the to belong that vertices The conducted. be can clustering numbers, EC Enzyme Commission(EC) number, whic structure.In these typeof graphs,there could be some vertices which have clustering information dedicated to enzyme the represent that vertices contain graphs biological the of Some in bioinformatics. importance by the graphs. Biological graphs which consist of components such as genes, represented be can proteins, and information of enzymes; type various have items, these between relation the represent to edges and items

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aksoydan T. Kamada and S. Kawai. An algorithm for drawing general undirec H.F - directed algorithm to visualize pathways that contain nodes with attributes as EC numbers.EClerize . Danaci, 1, A CUSTOMIZED FORCE ACUSTOMIZED 2 3

to measure how well graphs are drawn, fitness values of graphs are calculated by 6different fitness : Department of Computer Engineering, Middle East Technical University, Ankara, Graduate School of Informatics, Middle East Technical University, 06800, Ankara, Turkey TECHNIQUES FOR BIOLOGICAL GRAPHS FOR BIOLOGICAL TECHNIQUES ious study, we described EClerize [1] which is a customized and improved Kamada Kawai [2] [2] Kawai Kamada improved and a is customized which [1] EClerize we described study, ious ALGORITHM WITH GENETIC ALGORITHM ALGORITHM WITH GENETIC ALGORITHM Graph Visualization, - [email protected]

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Figure 1. on the gastrointestinal submucosal tumors with the R program. on the gastrointestinal submucosal tumors the number of patients in the is that of the study The disadvantage used is gastrointestinal submucosal tumors dataset not very large. relevant patient data. This can be overcome by increasing the Keywords: Ui. Shiny Web Decision Tree, In addition, it is explained that it will enable the users to make the will enable the users to it is explained that it addition, In with R. analysis to be more dynamic and visual which is set, is performed on a real-life data The presented study be performed to from texture analysis, allows this analysis gathered performing the instead of accessing users over the web remotely by and has not been previously performed analysis on the local computer, tumor). In the application, classificationtumor). In the application, with decision algorithm tree, which is one of the data mining methods, is used and it has been 89%. accurate classification performance in found that it provides The web-based and dynamic operation of this application is provided with the Shiny R sofware environment. package of It is shown that the results by taking the input values can be calculated from the users environmentin the web created with this package. via the interface In this study, a web based application was realized a web based application a real data by using In this study, a decisionsystem set in order to establish for gastrointestinal support Leiomyoma & tumor stromal (Gastrointestinal submucosal tumors References: [1] for R, [2] 2016 Corresponding author’s address a tumor tumor Keywords decision support system for for system support decision tumor in performance classification accurate provides it that found been has it and used is sofware env enable will it that explained is it addition, In package. this with created interface the via environment web the mor be to analysis the make to users the the performing of instead web the over users accessing by remotely performed be to analysis this allows the on analysis

Nuclear Medicine, Genome Center 4.Izmir Biomedicine and Asım Leblebici Müjde Soytürk Oncology, of Translational of Health Sciences, Department University, Institute 1. Dokuz Eylul School of Medicine, Department of Gastroenterology, 2.Dokuz Eylul University, of and Research Hospital, Department Ataturk Training 3.Izmir Katip Celebi University, A WEB BASED DECISION SUPPORT TOOL FOR FOR TOOL SUPPORT DECISION BASED A WEB TUMORS SUBMUCOSAL GASTROINTESTINAL POSTER PRESENTATIONS 194 Corresponding Author’s Address: Corresponding Author’s Çağlayan Kitabevi,1stedition,2016 [2] Kartal E, Balaban, E., “M. E. “R ile Veri Madenciliği Uygulamaları”, CRAN.R-project.org/package=shiny. J.,McPherson, Web 2015,Shiny: ApplicationFramework forR,http:// References: [1]Chang, W., Cheng, J., Allaire, J. J., Xie, Y. ve [email protected]. POSTER PRESENTATIONS

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T Prediction table of . Bacteroidetes Bacteroidetes Deniz KayaEce , Eray Şahin 1 Deniz KayaEce Grice E.A., J.A. Segre The human microbiome: Our second genome. Annu.Rev. GenomicsHuman Technology Acibadem Mehmet Al Mehmet Acibadem 1 Grice E.A., J.A. Segre The human microbiome: Our second genome. Annu.Rev. GenomicsHuman Technology Acibadem Mehmet Al Mehmet Acibadem 1 Genet. 2012;13:151 [CrossRef] [Google Scholar] Corresponding author’s address [email protected] 1. Table Figure1 in showed pipeline to respect with scored is partition test The 20%. References: [1] 1. Figure or (Illumina) I column: clustering a of addition where table clustering into converted tables OTU microbiome O (ONT)

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with respect to pipeline showed in Figure1 or O (ONT). 1: Table is scored The test partition versus 20%. 80% by reads partitioned ONT Figure 1. tables converted effect on microbiome analysis. The microbiome OTU of a clustering where addition column: I (Illumina) into clustering table a sequencing technique implementing any bias on the microbiome sequencingbias on there is a have presented that We tables. OTU Proteobacteria and techniques especially for the Bacteroidetes tables. distribution in the OTU Keywords: Analysis are developing methods for the various DNA extraction protocols, protocols, extraction DNA the various methods for are developing different kinds different of library preparations, kinds of microbiome sequencing techniques. kinds of different now analysis pipelines and In this research presented how sequencing we have method effects Illumina samples (50 66 have totally whole microbiome analysis. We (ONT)) for observing the and 16 Oxford Nanopore Technology analysis. Naïve Bayes sequencing technique bias on the microbiome joined for scoring to observe whether learning and the SVM Learner The second genome that effect the human health is the microbiome human health is the effect the genome that second The but the microbial diversity is well understood The importance of we have still could not handle several biases microbiome in whole microbiome analysis, researchersget trustworthy analysis. In order to [1] Genet. 2012;13:151 [CrossRef] [Google Scholar] Corresponding author’s address [email protected] 1. Table Figure1 in showed pipeline to respect with scored is partition test The 20%. References: 1. Figure or (Illumina) I column: clustering a of addition where table clustering into converted tables OTU microbiome O (ONT)

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Deniz Ece Kaya Deniz Ece Sezerman Osman Uğur and Dept. of Biostatistics University School of Medicine Mehmet Ali Aydinlar 1. Acibadem Bioinformatics,Istanbul, Biyoinformatik Yazılım A.Ş., İstanbul 2. Epigenetiks Genetik THE EFFECTS OF SEQUENCING TECHNOLOGY ON ON TECHNOLOGY SEQUENCING OF EFFECTS THE ANALYSIS MICROBIOME BASED AMPLICON THE POSTER PRESENTATIONS 196 edu.tr http://epigenetiks.com.tr/ Address: Author’s Corresponding article] [PubMed][CrossRef][GoogleScholar] 170. doi: 10.1146/annurev-genom-090711-163814. [PMC free second genome.Annu.Rev.Human Genet.2012;13:151– Genomics References: [1] Grice E.A., SegreJ.A.Our Thehumanmicrobiome: E.A., [1]Grice

ugur.sezerman@acibadem. POSTER PRESENTATIONS 197 . With this method, we have [2] 2 to train a classifier predict oral health using to to [3] [1] Gao L, Xu T, Huang G, Jiang S, Gu Y, Chen F. Oral Chen F. Gu Y, Jiang S, Huang G, L, [1] Gao T, Xu Oral Microbiome; Metagenomics; Machine-Learning , Andres Aravena , Andres Aravena 1 abundances and dysbiosis with beta-binomial regression. arXiv:1902.02776. 2019 JA. Machine learning techniques accurately classify Foster [3] Beck D, microbial communities by bacterial vaginosis characteristics. PLoS One. 2014 9(2):e87830. Pubmed PMID: 24498380 80% success rate. Keywords: References: microbiomes: more and more importance in oral cavity and whole and Cell. 2018; 9:488-500 PubMed PMID: 29736705 Protein body. Modeling microbial Willis, A.D.. Witten, D., [2] Martin, B.D., the absolute abundances the absolute abundances microbe; that is, of each the number of have modeled DNA reads that correspond to a specific genome. We a fitting disease states by and the abundance shifts between healthy beta- binomial probability distribution identified a set of bacterial species that are associated with caries we used machine periodontitis disease configurations. Next, and learning methods with state healthy oral have predicted the We bacterial abundances. and periodontitis cases to (i) find the bacterial composition in healthy (i) find the bacterial composition in healthy periodontitis cases to and and dysbiotic salivary (ii) microbiome, find a set of bacterial species that are associated with a particular disease state, (iii) use machine learning approaches predict the oral health of the individual, given to do this, we aligned DNA sequences the bacterial abundances. To from the salivary microbiome to a database that contains the full- length genomes of reference bacterial species. Then, we calculated could indicate numerous oral and non-oral diseases[1]. Traditional oral and non-oralcould indicate numerous diseases[1]. Traditional find the bacterial species help to microbiological methods associated of small portion a capture these methods dysbiosis. However, with the microbial composition. Metagenomics proposes high-throughput microbial the comparing by dysbiosis of patterns the study to methods healthy and disease abundances of thousands of taxa between caries libraries conditions. In this study we used shotgun of 22 healthy, Our bodies host a wide variety of commensal microbes from the oral the from commensal microbes variety of wide a host bodies Our Firmicutes, cavity, healthy oral a In the gastrointestinal tract. cavity to Spirochaetes Bacteroidetes, and Fusobacteria, Proteobacteria, species microbial constitute 93% of the total oral healthy profile. In a stays in specific of those microbes state, the abundance proportions. significant affect the bacterial deviations from this balance However, This process called dysbiosis is and diversity in the oral cavity. Emrah Kırdök Biotechnology Department of 1. Mersin University, Genetics of Molecular Biology and Department 2.Istanbul University, USING METAGENOMICS AND MACHINE-LEARNING MACHINE-LEARNING AND METAGENOMICS USING HEALTH ORAL THE UNDERSTAND TO POSTER PRESENTATIONS 198 with 80% success rate. success 80% with state oral healthy the predicted have We abundances. bacterial using health oral predict to classifier a train to [3] methods learning machine used we Next, configurations. disease periodontitis and caries with associated are that species bacterial of set a identified have we method, this With [2]. distribution probability binomial specific genome. We have modeled the abundance shifts between healthy and d and healthy between shifts abundance the modeled have We genome. specific we calculated the absolute abundances of each microbe; that is, the number of DNA reads that correspond to a that database a to microbiome salivary the from sequences DNA aligned we this, do To bacterialabundances. the individual,given the of health oral bacterial of set a find (ii) microbiome, speci salivary dysbiotic and healthy in composition bacterial the find (i) to periodontitiscases and carieshealthy, 22 of librariesshotgun used we study this In conditions.disease and thousa of abundances microbial the comparing by dysbiosis of patterns the study cavity. This process is called dysbiosis and could indicate numerous oral and non specifi in stays microbes those of abundance the state, healthy a In profile. microbial oral total the of 93% constitute capture a small portion of the microbial composition. Metagenomics proposes high proposes Metagenomics composition. microbial the of portion small a capture fintomicrobiological help methods healthy oral cavity, cavity, oral healthy to cavity oral the from microbes commensal of variety wide a host bodies Our emrahk Corresponding author’s address characteristics. PLoS One. [3] arXiv:1902.02776. 2019 m Modeling A.D.. Willis, D., Witten, B.D., Martin, [2] body. Protein and Cell. 2018; 9:488 [1] Gao L, Xu T, Huang G, Jiang S, Gu Y, Chen F. Oral microbiomes: more and more importance in oral cavity and whole see the bacterialchange, relative abundance to representLines healthy sample. 95% we figure this In zero. to normalized are state healthy of abundances Bacterial states. dysbiotic different the vertica the and group, control to compared change Figure Keywords References: Beck Beck D, Foster JA. learningMachine techniques accurately classify microbial communities by bacterial vaginosis Using m Using es that are associated with a particular disease state, (iii) use machine learning approaches to predict the predict to approaches learning machine use (iii) state, disease particular a with associated are that es c c proportions. However, significant deviations from this balance affect the bacterial diversity in the oral [email protected] Significant taxa that changed in dysbiotic states. The horizontal axis shows the differential abundance abundance differential the shows axis horizontal The states. dysbiotic in changed that taxa Significant learning to understand the o the understand to learning : oral microbiome; metagenomics; machine metagenomics; microbiome; oral

Taxa Corresponding Author’s Address: Corresponding Author’s represent 95%confidenceintervals. to healthy sample.Lines see thebacterialabundancechange,relative abundances ofhealthystate are normalizedto zero. Inthisfigurewe dysbiotic states.Bacterial different Condition labeldefinesthe to control group, andtheverticalaxisshowsbacterialspecies. horizontal axisshowsthe differential abundance change compared 1. Figure Pseudoramibacter_alactolyticus Propionibacterium_acidifaciens 2 Istanbul University, Department of Molecular Biology and Genetics

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Actinomyces_cardiffensis 2014 9(2):e87830. Pubmed Actinomyces_timonensis Streptococcus_sobrinus Mogibacterium_timidum Eggerthia_catenaformis Lactobacillus_salivarius Streptococcus_mutans Actinomyces_johnsonii Neisseria_meningitidis Actinomyces_dentalis Prevotella_aurantiaca Treponema_denticola Actinomyces_israelii Tannerella_forsythia Prevotella_baroniae Prevotella_oulorum Prevotella_dentalis s enomic etag Parvimonas_micra Prevotella_shahii 1 Neisseria_sicca Filifactor_alocis Significanttaxathatchangedindysbioticstates.The Slackia_exigua Mersin University, Department of Biotechnology - EmrahKırdök 500 PubMed PMID: 29736705

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Deniz Ece Kaya1, Deniz Ece and Dept. of Biostatistics School of Medicine Mehmet Ali Aydinlar University 1Acibadem Istanbul, Bioinformatics, Biyoinformatik Yazılım A.Ş., İstanbul 2. Epigenetiks Genetik NUCLEICACID CONTAMINANTS FROM LIBRARY LIBRARY FROM NUCLEICACID CONTAMINANTS PREPARATION POSTER PRESENTATIONS 200 2. GebzeTechnical University,DepartmentofComputerScience,Çayırova/KOCAELI 1. GebzeTechnical University,DepartmentofBioengineering,Çayırova/KOCAELI Emre Taylan DUMAN METHODS LSTM ANDCNN GENOMIC DATA COMPRESSION BY DEEP LEARNING Data. Keywords: the files. and to use more general compression techniquesto of reduce size data files in sequence to beableforomitstorage of qualityscores new wordsorletters between languages. LSTM uses previousword relationships to predict used for one dimensionaldata as text/voice recognitionor translation data. approach,Second LSTM(Long ShortTerm Memory),isgenerally requires two dimensional conversionof the onedimensionalgenomic that ismainlyusedforimagerecognitionandprediction bench marking. CNN(ConvolutionalNeuralNetworks) isatechnique fragment. Two differentdeeplearning approacheswaschosenfor information onthenucleotidetypeandlocationofread ina by usingthe data for predictionofthequality scores the sequence data reduction ofgenomic different techniquesandapproachesforsize use algorithms which compressing are varietyofdifferent there issue, and the widecharacterscaleofphredscores.Becausethis this techniqueshowslowperformancebecauseoftherandomness or letter occurance probabilitiesfor changing theirbit encoding. And files suchasgzip of thetextbased compression allow losseless techniques compressing tolerance for losing any single nucleotidereads. Commonly used text reduction. Becauseofthenaturegenomicdata, size there isno of data techniquescapabale of losseless requires specialcompressing one experiment 100 Tb in over That makes can produce 1 to sequencing device 5 Tb commercial of rawdata. run witha A single random andcancontain40differentcharacters. character canbe converted into error rates. These scoresarerelatively calling errorprobabilities.Phred scores representedby single ASCII the sequencingdevice,whicharelogarithmicallycalculatedbase- assignedby is sequenceofASCIIcharacters of thefastqfile scores and ‘N’letterforthenon-readablenucleotides contain five charactersthat represent four nucleotides (‘A’,’C’,’G’,’T’) reads Sequence reads andqualityscores. that containssequence file Sequence data is usuallyproduced infastq format, which isatext growing technologies, thechallangeofdata storage is sequencing in data data. produced increase by new Because oftheaccelerating the genomicsisstorage and the handlingof the huge amount of of One ofthemainchallenge and healtcareresearch. medicine of personalized Sequencing ofthegenomeisone the milestones [5] . Inthiswork,deeplearningtechniqueswereapplied to 1 , YusufSinanAKGÜL Compression;DeepLearning; CNN; LSTM; Genomic [4] . But these text compression techniquesusesword [7] . Aim of this work is to predictqualityscores work is of this . Aim 2 , PınarPIR [3] 1 . Storage of this amount . Storageofthis [2] . Phredquality [6] . CNN 1 . POSTER PRESENTATIONS

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Compression 0 0 0 0 1 0 0 1 0.. 0 1 0 0 0 0 0 0 0.. 1780. [email protected] - Kahn, Scott D. On the future of genomic data. genomic of future the On D. Scott Kahn, the and scores, quality with sequences for format file FASTQ Sanger The al. et JA, Peter Cock, genomical?. or astronomical data: Big et al. D., Zachary Stephens, algorithms compression and structures Data Pierre. BALDI, C.; Douglas WALLACE, C.; Marty Brandon, Krizhevsky, Alex; SUTSKEVER, Learning Methods LSTM and CNN AT on CC AC CA AA CG AG CT… Positi Genomic Data Compression by Deep memory. Neural computation, 1997, 9.8: 1735-1780. memory. Corresponding Author’s Address: Turkey Bioengineering 41400 Gebze/Kocaeli pı[email protected] structures and compression algorithms structures and compression 1731- 25.14: 2009, Bioinformatics, data. for genomic sequence 1738. Geoffrey E. Imagenet Alex; SUTSKEVER, Ilya; HINTON, [6] Krizhevsky, classification convolutional neural networks. In: Advances with deep 2012. p. 1097-1105. in neural information processing systems. short-term Sepp; SCHMIDHUBER, Jürgen. Long [7] Hochreiter, 1767-1771. astronomical or genomical?. et al. Big data: D., [3] Stephens, Zachary 2015, 13.7: e1002195. biology, PLoS A Robert. LOURDUSAMY, Senthil; Shanmugasundaram, [4] compression algorithms. International text of comparative study 2011, 1.3: 68-76. Computing, Journal of Wisdom Based Pierre. Data BALDI, C.; Douglas WALLACE, C.; Marty Brandon, [5] References: 331.6018: 728-729. science, 2011, for sequences file format JA, al. The Sanger FASTQ et [2] Cock, Peter the with quality scores, and Nucleic variants. 38.6: acids research, 2009, Solexa/Illumina FASTQ Gebze Technical University Bioengineering 41400 Gebze/Kocaeli Turkey Gebze/Kocaeli 41400 Bioengineering University Technical Gebze pınar [3] compression text of study comparative A Robert. LOURDUSAMY, Senthil; Shanmugasundaram, [4] algorithms. International [5] for genomic sequencedata.Bioinformatics, 2009,25.14: 1731 [6] 1097 p. 2012. systems. processing information neural in Advances In: networks. neural convolutional short Long Jürgen. SCHMIDHUBER, Sepp; Hochreiter, [7] 1735 Corresponding author’saddress Keywords: References: [1] [2] variants. FASTQ Solexa/Illumina mainly used for image recognition and prediction [6]. CNN requires two dimensional conversion of the one one the of conversion dimensional two requires CNN [6]. prediction and recognition image for used mainly gen dimensional word previous uses LSTM languages. between translation or recognition text/voice as data dimensional is work this of Aim [7]. letters or words new predict to relationships to techniques compression general more use to and files data sequence in scores quality of storage omit the of files. size reduce commercial sequencing device can produce 1 to 5 Tb of raw data. That makes over 100 Tb in one experiment experiment one in Tb 100 over makes That data. 5 1 to of raw Tb produce can device sequencing commercial size losseless of capabale techniques compressing special requires data of amount this of Storage [3]. geno the of nature the of Because reduction. such files based text the of compression losseless allow techniques compressing text used Commonly reads. prob occurance or letter word uses techniques compression text these But [4]. gzip as wide the and randomness the of because performance low shows technique this And encoding. bit their compressing different of variety are there issue, of this Because scores. phred the of scale character different use which algorithms the using by scores quality the of prediction for data sequence the to applied were techniques learning deep in a f read the of location and type nucleotide on the information is that technique a is Networks) Neural (Convolutional CNN marking. bench chosenfor was approaches of One research. healtcare and medicine personalized of milestones the of one is genome the of Sequencing hu the of handling the and storage is genomics the of challenge main the is storage data of challange the technologies, sequencing new by produced data in increase accelerating sequence contains that file text a is which format, fastq in produced usually is data Sequence [1]. growing and reads (‘A’,’C’,’G’,’T’) and letter ‘N’ for the non d sequencing the assignedby characters ASCII of sequence error into converted be can character ASCII single by represented scores Phred probabilities. error calling wit run single A characters. different 40 contain can and random relatively are scores These rates.

POSTER PRESENTATIONS 202 3.Department ofMedicalGenetics,SchoolMedicine,IstanbulScienceUniversity 1. IdeaTechnology Solutions 1,2. BogaziciUniversity,ComputerEngineering Özge Dinçsoy1 INTERACTIONS DISEASES USING LITERATURE MINEDGENE IDENTIFYING COMMON PATHOGENESIS OF Keywords: related inliteratureandtobe investigated inDNAdataofpatients are notfoundautism which genes, wehavecomeupwith new genes In addition tothese associated withdiseases. that arepreviously methods, wehaveobtained genes In conclusion,byusingtextmining Forresearch. are showninTable the secondapproach, the results 1. further in to beexamined therest2genes we arerecommending out of 7 genes havebeenfound previously linkedwith autism and genes of the twoapproach. From theresult of the firstapproach, 5 algorithm. We haveusedSFARI Geneforevaluation oftheresulting hold both genes inan edge. We sort genes accordingto PageRank four search queriesaccording to the number of documents that generated weighted gene graphsfromtheresultsofbeforementioned intersection setcontained 7 genes. In the latter approach, we have separately and obtained set ofthosegenes.The intersection illnesses Then, weinspectedthetop two 100 particular illnesses. genes forboth per documentforthese according tothenumberofoccurrences genes weighting and PageRank scoring. In the former approach, we rank Weanalysis. havefollowedtwodifferentapproaches,disease SciMiner by using the unionqueryresultandmarked thegenes represent which epilepsy orautism.We haveworkedon244159 PubMed articles on either on autism,articlesbothepilepsyand four different search queriesconsistingof articles on epilepsy, articles shown whichgenesare connected with each other. We havemade research automatically with the help of PubMed database. We have for thetwoToillnesses. do this, wehaveaimedto make literature ingenebaseand between diseases to propose candidate genes Here, by using textminingonarticles,our aim wasto find connections source oftheprecipitatinggeneticdefectcanbehelpfulintreatments. knowledge about patients, havingthe In these as well. disorders that in childhoodandmaytriggerotherneurological reveals illnesses with epilepsy alsohaveASD Spectrum Disorder(ASD)andEpilepsybecauseup to 20% ofpeople project, specifically, we focused on the relationship between Autism is acommonpathogenesishasbeenthesubjectofinterest.Inthis person andwhetherthere in thesame seen are brainrelateddiseases is acrucialtaskformedical research.Amongthese,there illnesses era,theelucidation of In thegenomic the relationshipamong , ArzucanÖzgür2,AhmetOkayÇağlayan3 [2] , a tool for target identification and functional enrichment , atoolfortarget identification andfunctionalenrichment Autism;Autism SpectrumDisorder;Epilepsy [1] . Both of them are lifelong pervasive POSTER PRESENTATIONS 203

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2 464 : 10.2147/NDT.S120509. PubMed PMID: 29296085; PubMed Central PubMedPubMed Central PMID: 29296085; 10.2147/NDT.S120509. : 3381 related related diseases seen in the e sam person and whether there 1, and to propose to and lifelong lifelong pervasive illness Query #1 ments Gene Gene Interactions We have made four different search queries consisting of articles on epilepsy,

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- previously previously disease weighting and PageRank scoring , , [1] Besag FM. Epilepsy in patients with autism: links, in patients with Epilepsy FM. [1] Besag AutismSpectrum Disorder , , the elucidation of the relationship ; m and we are recommending the rest 2 genes to be examined in further research Özge Dinçsoy Özge here are era Department of Medical Genetics, School of Medicine, Istanbul Istanbul Medicine, of School Genetics, Medical of Department helpful in treat has been the subject of interes Performance result of the second approach. result Performance 3 autis

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, [2], a tool for target identification and functional enrichment analysi : Basu S, Packer A. SFARI Gene 2.0: a community a SFARIA.Gene 2.0: Packer S, Basu

genes that are Performancesecondapproach. the of result . - literature research automatically literature Diseases Using Literature Mined Mined Literature Using Diseases can be t in SFARI Gene in SFARI nd nd approach, the results are shown in Table 1. Besag FM. Epilepsy in patients with autism: links, risks and treatment challenges. Neuropsychiatr Dis Dis Neuropsychiatr challenges. treatment and risks links, autism: with patients in Epilepsy FM.Besag HurJ, Schuyler AD, States DJ, Feldman EL. SciMiner: web - Abrahams BS,Arking DE, CampbellDB, Mefford HC, Morrow EM, Weiss LA, Menashe Wadkins I, T, thogenesis thogenesis Total number of genes Total number Number of genes found Number of Identifying Pathogenesis Common of [email protected] [email protected] [email protected] 1 2 3 References: [1] 18;14:1 Dec 2017 Treat. PMCID:PMC5739118. [2] In the genomic Fe 2009 Bioinformatics. analysis. enrichment functional and identification

Table 1. [3] Banerjee Keywords Among these, t disorders(ASDs). Molecular autism. 2013 address author’s Corresponding 2. [email protected] 3. [email protected] 2. [email protected] enrichment analysis. Bioinformatics. 2009 Feb 2;25(6):838-40. Bioinformatics. 2009 Feb enrichment analysis. Morrow Mefford HC, Arking DE,[3] Abrahams BS, DB, Campbell A. Banerjee-Basu Packer S, T, LA, Menashe I, Wadkins EM, Weiss community-driven a Gene 2.0: for the autism knowledgebase SFARI Molecular autism. 2013 Dec;4(1):36. spectrum disorders (ASDs). Corresponding Author’s Address: Table 1. Table References: 2017 risks treatment challenges. and Dis Neuropsychiatr Treat. PMID: PubMed 10.2147/NDT.S120509. doi: Dec 18;14:1-10. PMC5739118. PubMed Central PMCID: 29296085; EL. Feldman SciMiner: web- DJ, States Schuyler AD, [2] Hur J, and functional identification target for tool based literature mining newgenes neurological disorders as well. In these patients, having knowledge about knowledge having patients, these In well. as disorders neurological pa ASD [1] defec worked worked on 244159 PubMed articles SciMiner betweenAutismSpectrumDisorder base gene in diseases between connected with each other. articles on autism, articles on both epilepsy and a to to make obtained obtained seco contained contained 7 genes. In the latter beforementioned approach, four search queries according to the we number of documents that ho have generated weighted gene graphs from the results of different approaches according to the number of occurrences per document for these two particular illnesses. Then, we sepa inspected illnesses both for genes 100 top the We sort genes according to PageRank algorithm. We have used SFARI Gene for evaluation of the resulting genes of the two approach. linked with POSTER PRESENTATIONS 204 Has University Bioinformatics andGeneticsDepartment,FacultyofEngineeringNaturalSciences,Kadir 4. University, 3. ComputerEngineeringDepartment,FacultyofandNaturalSciences,KadirHas 2.Center forAppliedScientificComputing,LawrenceLivermoreNationalLaboratory, 1.Biosciences andBiotechnologyDivision,LawrenceLivermoreNationalLaboratory, Sevilay Güleşen Brian J.Bennion ACETYLCHOLINESTERASE OFSARIN EFFECTED DYNAMICS HUMAN GUMMING UPTHEWORKS: AND SOMAN research/sebnem-essiz/ [email protected] http://sites.khas.edu.tr/bioinformatics/ Eşsiz: KadirNatural Sciences, HasUniversity, 34083 Fatih, Şebnem Istanbul, Turkey Department, Facultyand Genetics Bioinformatics and ofEngineering https://bbs.llnl.gov/BrianBennion.html llnl.gov bennion1@ United StatesofAmerica. CA, 7000 East Ave,Livermore Lawrenceand BiotechnologyDivision, National Laboratory,Livermore Address: Author’s Corresponding 2015;10(4):1-31. dynamics simulations.PLoS ONE. soman inducedconformationalchangesrevealedby molecular Lightstone FC.Awrenchintheworksofhumanacetylcholinesterase: References: Molecular Dynamics;Principal ComponentAnalysis(PCA) Keywords: substrate totheactivesite. apo and different adducts effects on the entrancesitedynamicsof the adducted of simulations areanalyzedto understand thedifferences motions. Principaland soman (PCA) ofapo, sarin componentanalysis changesinbackboneandsidechain to investigatesarin-dependent MD simulations ofthesarin-adducted forms of hAChE wereanalyzed 50 ns longMDsimulationsoftheapo 50 and 40 ns long classical adduction to hAChE hasbeenadded to the analysis.40classical use two different pathways as entryandexitsites.Recently, thesarin that substrate andproductcan motions support the hypothesis These entrance andbackdoor motions forsubstrateentranceisdisrupted. When thehAChE isadducted by soman,thecorrelationof gorge and protein structure when thecatalyticSerine203 is phosphonylated. forms of hAChE wereanalyzed to examine theeffectson the dynamics (QM/MM) simulationsoftheapoandsoman-adducted mechanics and quantum mechanics/molecular dynamics (MD)simulations and eventually death agents (CWA) leads toseizures,paralysis, cognitive deficiencies, organophosphorous (OPs)pesticides weapon andchemical (hAChE)Intoxication ofhumanacetylcholinesterase by 3 1 , ŞebnemEşsiz , LauEY Human acetylcholinesterase (hAChE); Human acetylcholinesterase Soman; Sarin; [1]BennionBJ, SG, Essiz Lau EY, EmighA, Fattebert J-L, 1 , FattebertJ-L 4 [1] . 80 classical short 50 ns length molecular 2 , EmighA Brian J.Brian Biosciences Bennion: 1 , LightstoneFC 1 , POSTER PRESENTATIONS 205

4 ,Aziz Sancar 3 To whom correspondence may To , Yi-Ying Chiou 2 , Yanchao Huang , Yanchao 2 [1]Tubbs A, Nussenzweig A. Endogenous DNA [1]Tubbs Nucleotide Excision Repair; UV Damage; (6-4)PPs; CPDs; Nucleotide Excision (6-4)PPs; UV Damage; Repair; 1 replication-timing domains in the human genome by deep learning. learning. deep human genome by replication-timing domains in the Bioinformatics. Pubmed PMID: 26545821. 2016 Mar 1;32(5):641-9. Corresponding Author’s Address: Natural and Engineering of Faculty University, Sabancı addressed: be E-mail: 216-568-7043; Tel.: Turkey Istanbul, 34956 Sciences, Tuzla, [email protected]. References: Feb Cell. 2017 as a source of genomic instability in cancer. damage 9;168(4):644-656. Pubmed PMID: 28187286. A.Sancar S, Adar UV damage of Dynamic maps O, Adebali J, [2]Hu Natl Acad Sci. . Proc formation and repair for the human genome. 2017 Jun 27;114(26):6758-6763. Pubmed PMID: 28607063. of identification De novo X,Bo W. Shu P, Zhou Li H, Ren C, F, [3]Liu the repair preferences. We found out that in both early and late S early and in both that out found the repair preferences. We cells, early replicating domains are more efficiently repaired phased potential a investigate aim to relative to late replicating domains. We and lagging strand. differential repair efficiency between leading Keywords: Damage-Seq;XR-Seq; Replication excision repair events respectively, in various conditions[2]. In this excision events repair respectively, we analyze the Damage-seq results of cyclobutane and XR-seq study, pyrimidine-pyrimidonepyrimidine dimers (CPDs) and (6-4) cells synchronized HeLa from UV-irradiated [(6-4)PPs] photoproducts) at two stages of the cell cycle: early S phase, and late S phase. We replication domains of HeLa compare these datasets with the localized to reveal aim how replication influencingdomains are cells[3]. We Maintaining genome integrity is Maintaining genome crucial for healthy cells to avoid 70,000 occur approximately DNA damages Considering that cancer. these repair of damages is vital for the times per cell per day, the other hand, replication maintenance of genome stability[1]. On is the mechanism causes unrepaired DNA damages to turn into that The role of replication on DNA might lead to cancer. mutations that be clarified. Recently developed methods repair in general is yet to Damage-seqformation and nucleotide map damage and XR-seq , Jinchuan Hu , Jinchuan 1 Sabancı University Orhanlı, Tuzla, 34956,Istanbul Tuzla, Sabancı University Orhanlı, 2.Fifth and Institute University, Fudan People’s Hospital of Shanghai of Biomedical Sciences, Shanghai 200032, China National Chung Hsing University, Taiwan 3. Institute of Biochemistry, 4. University of North Carolina School of Medicine, Department of Biochemistry and Biophysics, Chapel Hill, North Carolina DAMAGES. Cem Azgari Adebali and Ogün Bioengineering, and Genetics Molecular Biology, Natural Sciences, and Engineering 1. Faculty of GENOME-WIDE EFFECT OF DNA REPLICATION ON REPLICATION DNA OF EFFECT GENOME-WIDE UV-INDUCED OF REPAIR EXCISION NUCLEOTIDE POSTER PRESENTATIONS 206 1.Molecular BiologyandGeneticsDepartment,IstanbulUniversity Faruk Üstünel RESISTANT PLASMID SEQUENCE ORGANIZATION OF AMULTI-DRUG EVOLUTIONARY PERSPECTIVE TO UNTANGLE DNA [email protected] Address: Corresponding Author’s doi.org/10.1128/AEM.67.8.3732-3734.2001 Applied andMicrobiology,Environmental 67(8), 3732–3734. https:// coliform bacteriaofhumanandbovineorigininafarmenvironment. resistance plasmidbetween (2001). Horizontal transfer of a multi-drug References: Elements InPlasmidAssembly Keywords: strategies usedtoachievethisresult. our proposal. Inthispresentationwewilldiscussthebioinformatic validate that can organization, andasetofPCRprimers a likely plasmids weareableto proposescaffolds withthesetofreference By comparingour plasmids’ sequences. a setofsimilar we collected sequence. To aconclusive us fromachieving untanglethisscaffold, across theplasmid,thatrestrict of transposableelements instances are multiple ambiguous. There organization is the sequence where De Bruijngraph)toallplaces determine asinglescaffoldrevealing (overlay-layout-consensusstages withtwostrategies assembly and ones wereprobably which part ofarepeatedregion.We usedtwo were likelyto be from theplasmidand not from thechromosome,and an ad hoc approach. We screenedour reads to determine which ones some parts of the host bacterial genome, plasmidassemblyrequired the DNAextractionincluded Since in two NGSlibraries. specimens enable itshorizontaltransfer,characteristics wesequencedthetwo Citrobactreium freundiifound in cow host. To understand which cellsfound in a human host, and from resistant plasmidfrom E.coli of amulti-drug In apreviousstudy we isolatedtwo specimens 1 , Terje M.Steinum GenomeAssembly;ScaffoldUntangling;Transposable [1]Oppegaard, H., Steinum,T. M., & Wasteson, Y. 1 , AndresAravena 1 POSTER PRESENTATIONS 207 . There are . . As far as we [1] [2] . PRIDA had 78.26% accuracy had 78.26% . PRIDA 1 [2] , Ugur Sezerman 2 . However, we observed that there are still deficiencies we observed that . However, [2] , Aslı Yenenler ne crucial step in variant prioritization is functional impact is functional prioritization in variant ne crucial step 1 MD trajectories provide many structural and functional feautures such Rg, as changes RMSD, in RMSF, destabilization tendency (ΔΔG) and time-consumingligand distances if exist. Although MD is a method, there are tricky methods to overcome this such as aMD which lowers activation energy barriers to accelerate changes. conformation we demonstrated that aMD can be used with shorter Furthermore, and the resulted structural-functional trajectories instead of MD, features provided 100% accurate classification on the cases in Table scores of MutationTaster, Condel and CanDrA, and offering a short scores of MutationTaster, finally classify the those unrelaibles to for study (aMD) accelerated MD validate this pipeline, three cases type of were selected mutations. To on the test cases: (1) PRIDA randomly from the false predictions of one (2) known as neutral, but as pathogenic PRIDA one predicted by (3) and known as pathogenic, as neutral but PRIDA by predicted analyses of the resulted 1. Moreover, cases as in Table control/support and considered that these inadequencies can be overcome by using overcome by these inadequencies can be considered that and level structural and functional feautures on protein observed, there is no predictor/pipeline in the literature, which uses molecular dynamics such as such features from complex methods (MD) simulations while evaluating a vast amount of mutations. Pipeline called PRIDA-aMD novel method developed a Therefore, we 1 after deriving a reliability score from the shown in Figure (PRaMP) methods such as decision tree modeling. We formerly developed We methods such as decision tree modeling. which is one of RIsk Detection Algorithm (PRIDA), Pathogenicity those meta-predictors, and it uses the categorical predictions from Condel and CanDrA MutationTaster, enormous amount an on correlation coefficient: 0.61) (with Matthews neutral 42031 pathogenic and as 21290 of test cases (n=63321, variants) diseases or complex genetic diseases such as cystic fibrosis, cancers genetic diseases such as cystic fibrosis, diseases or complex information these pathogenicity Alzheimer’sand disease. Moreover, enlighten the patients and trying to are helpful when diagnosing disease mechanism diagnosed pathogenicity of literature, and each has numerous variant effect predictors in the hand, better predictions different basis and algorithm. On the other machine by creating meta-predictors via can be obtained learning O which prediction for mutations, gives critical directive information patients with genetic of data the variants in genome/exome about Aydinlar University, Istanbul, Turkey Aydinlar University, Istanbul, Biruni Natural Science, and Engineering Faculty of Engineering, Biomedical 2.Department of University,Istanbul, Turkey DYNAMICS FOR FUNCTIONAL IMPACT OF OF IMPACT FUNCTIONAL DYNAMICS FOR MUTATIONS Umut Gerlevik of Medicine, Acibadem Mehmet Ali and Medical Informatics, School 1.Department of Biostatics PRAMP: A PREDICTION PIPELINE COMBINING A COMBINING PIPELINE PRAMP: A PREDICTION MOLECULAR AND ACCELERATED META-PREDICTOR POSTER PRESENTATIONS 208 selected selected for fr a mole therenois predictor c variants c from D machine bas diagnosed information genetic d One [email protected] address author’s Corresponding HIBIT2018; 2018. S mutations of [2] PMID: Turkish familywith autosomal recessive nail dysplasia. BMC Med Genet. [1] References: Table 1. Figure 1. Keywords: of involvement functional be can aMD that demonstrated we this exist if distances ligand structural control pathogenic but known as neutral, (2) one predicted by PRIDA as neutral but known by overcome be an oefficient ir ymposium on om the scores of MutationT etecti novel method novel 2 Type 1 accelerated molecular dynamics for for dynamics molecular accelerated ec

(3) (2) (3) ( ( i Department of Biostatics and MedicalInformatics, Department of Biomedical Engineering, Faculty of Engineering and Natural Science, Biruni University, University, Biruni Science, Natural and Engineering of Faculty Engineering, Biomedical of Department Saygı C, Alanay Y, Sezerman U, YenenlerA, Özören N. A possible founder mutation in FZD6 gene in a Gerlevik those those 3 1 s such as ) ) culardynamics (MD) tiv c

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/support on Algorith on Keywords: of involvement functional be can aMD that demonstrated we this exist if distances ligand structural control pathogenic but known as neutral, (2) one predicted by PRIDA as neutral but known selected for fr a mole therenois predictor c variants c from D machine bas diagnosed information genetic d One [email protected] address author’s Corresponding HIBIT2018; 2018. S mutations of [2] PMID: Turkish familywith autosomal recessive nail dysplasia. BMC Med Genet. [1] References: Table 1. Figure 1. combining a a combining Corresponding Author’s Address: Corresponding Author’s 2018 Oct25-27;Antalya,Turkey. Antalya:HIBIT2018;2018.p.83. International Symposium on Health Informaticsand Bioinformatics; Committees, editors. Student symposium. HIBIT2018: 11th a meta-predictor for functional impact of mutations. In: HIBIT2018 U,[2] Gerlevik O, Cingiler U. BingolC,Sezerman UlgenE, PRIDA: 14;20:15. PubMedPMID:30642273. nail dysplasia.BMCMedGenet. 2019 Jan with autosomal recessive N.a Turkishin FZD6 gene founder mutationin A possible family References: for theclassificationofmutations. Table 1: Figure 1. Dynamics; Meta-predictor;Pipeline Keywords: in varianteffectpredictionprocessesasPRaMP. of structuralandfunctionalfeatures 1. Thus,wesuggestinvolvement 30642273 e ) by overcome be an oefficient disease ir ymposium on

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POSTER PRESENTATIONS 209

[2,3] . At this -tubulin -tubulin , α 1), K226 1), 1 [5] α -tubulin with α -tubulin ( 0) interactions 10). D72N mutant forms β αβ , Fidan Sümbül β 1 5L8) as well as between NL between well as as 5L8) β 1 10 strands of NL;being the N332 10 stabilizes the docked state of NL.of state docked stabilizes the 10 β β -tubulin interaction. N332 residue in -tubulin interaction. N332 9- β αβ -tubulin with -tubulin β 9-CS (cover strand, β , Burcu Aykaç Fas , Burcu Aykaç 1 -tubulin (Figure 1). The magnitude of unbinding of magnitude The 1). (Figure -tubulin -tubulin) and tubulin becomes stronger, while the tubulin becomes stronger, -tubulin) and αβ β 10 (V333-T336) on the motor head (G76 ( (G76 head motor on the 10 (V333-T336) , Türkan Haliloğlu β 2 , Burçin Acar 1 5L8 with β 7)) and initiates formation of parallel beta-sheet called cover-neck 7)) and initiates formation of parallel beta-sheet called cover-neck β highly correlated with switch-2 cluster for D72N. Thus, we propose Thus, we propose highly correlated with switch-2 cluster for D72N. residues in D72N, switch regions and also NL being sensitive to the residues in D72N, possibly reduced response implies to binding and hydrolysis of ATP, S175A/N332A,and WT to compared the Moreover, the nucleotide. correlation between D72N tubulin binding sites (L11 with and L11-switchand correlations between switch 1 between regions and 2 switch regions and NL are weakened. NL; on the other hand, becomes more hydrogen bonding than wild-type and S175A/N332A between and S175A/N332A more hydrogen bonding than wild-type tubulin binding sites switch-2) and (L11; switch-2 cluster; and L11 and the motor head. This implies a stronger attachment of kinesin to the tubulin as well NL to the motor head, which as of disrupts its decrease a contrast, stroke. In power during tubulin dissociation from switch-2 switch-1 and formation between bonding hydrogen in the latch, docks ( bundle (CNB) through force for a walking stroke event, CS is responsible for generating the and while N332 stroke) (power binding of movement of the trailing head requires the tight Forward NL to the motor head (by N332 latch and type and D72N, S175A and N332A mutants of human kinesin human kinesin protein of mutants N332A and S175A D72N, and type in complex with pulling experimentsAFM by forces and unbinding rates measured kinesin are effective in lowering the N332A/S175A that suggest [4] whereas the dissociation constant is largest activation energy barrier, for D72N kinesin the in human resides in between docked (ordered) or undocked (disordered). Switch regions (switch-1 (disordered). Switch undocked (ordered) or docked also responsible and switch-2), are change for conformational aim to explore how global dynamics hydrolysis. We of NL via ATP kinesin-tubulinthe binding interaction of human are coupled to Model (GNM) this, we integrate Gaussian Network complex. To analysis Dynamics and Molecular (MD) simulations with Atomic Force Microscopy (AFM) single-molecule pulling experiments on the wild- Molecular motors are key components of the cell Molecular motors are responsible for all cellular processes in living organisms. movementsrealize kinds of to of molecular motors responsible Kinesin-1 from is a prototype kinesinThe movement of [1]. microtubules carrying cargoes along the to binding (ATP) the nucleotide by on microtubule is mediated nucleotide Depending on the head of kinesin leading motor dimer. of the neck the state linker (NL) is respectively either or ADP, ATP type; , Hamdi Torun 1 Zeynep Erge Akbaş Zeynep Erge Yiğit Kutlu Bogazici University Engineering Department, Research Center and Chemical 1. Polymer UK upon Tyne, Northumbria University, Newcastle 2. Department of Mathematics, ALLOSTERIC TUBULIN BINDING INTERACTIONS IN INTERACTIONS BINDING TUBULIN ALLOSTERIC KINESIN-1 POSTER PRESENTATIONS 210 dynamic cooperativity between kinesin and tubulin propose that the failure, could be associated with SPG10 disease the experimental observations. Lower dissociation rate of D72N, which plausibly result in the kinesin motility cooperative mode th mode cooperative power stroke power are weakened are kinesin to the tubulin as well as ofNL to Keywords: stronger residues cluster; Dynamics Simulations this event, CS is responsible for generating the force for a walking stroke awalking for force the generating for responsible is CS event, this possibly between Figure 1 stabilizes motor head motor S175A/N332A of parallel beta parallel of unbinding forces αβ unbinding with complex in protein kinesin human of mutants N332A and N332 being the latch, D72N kinesinin theαβ for largest is constant dissociation the whereas barrier, energy activation the lowering in effective are kinesin single (AFM) Microscopy Force Atomic integrate how global dynamics are coupled to [4 and microtubules along [ formation. formation. [ E Department Engineering Chemical and Center Research Polymer address author’s Corresponding processes in living organisms. Kinesin Molecular motors are key components of the cellresponsible for all kinds of movements to realize cellular and energy barrier for protein energyand barrier 79(16):3090 [ References: leading the to [ [ Science. single Neurogenetics. neck linker linker neck 3 6 1 5 2 Turkey [email protected] E-mail: University,Department, Bogazici Engineering Chemical Istanbul, Address: Author’s Corresponding PubMed PMID:22466687 a KIF5A-/- mousemodel.Neurogenetics.2012 May; 13(2):169-79. [6] Karle KN, Möckel D, Axonal transport deficit in Schöls L. Reid E, Jan;16(1):62-71. PubMedPMID:18184584. hinges on cover-neckkinesin bundleformation. Structure. 2008 [5] Hwang, W., M.J. Lang, andM. Karplus. Force generationin PMID: 17015017. protein-ligand interaction.Micron,2007;38(5):446-61. Pubmed Determination ofunbindingforce,offrateandenergybarrier for Wang[4] Lee CK, YM,HuangLS, LinS.Atomic forcemicroscopy: Proteins. PhysRevLett. 1997;79(16):3090-93. [3] HalilogluT, BaharI,ErmanB.of Folded GaussianDynamics Fold Des.1997;2(3):173-81.PubMedPMID:9218955. potential. harmonic fluctuations inproteinsusingasingle-parameter [2] BaharI,AtilganAR,ErmanB.evaluation ofthermal Direct Apr 7;288(5463):88-95.PubMedPMID:10753125. 2000 motorproteins.Science. looking underthehood of molecular References: Figure 1. Simulations; Allostery Gaussian NetworkModel;MolecularDynamics Keywords: and tubulindimerrationalizetheireffectsinbinding. of themostcooperativemodethatintegratekinesin at thehingesites be associated with SPG10 disease [6]. The mutation positions being of D72N,motility failure,could plausibly resultinthekinesin which corroborate the experimentalobservations.Lower dissociation rate and tubulin resultinstrongerinteractionswithtubulinthat kinesin to hydrolyze ATP aswellhigherdynamiccooperativitybetween ability and decreased monomer that thestrongerdockingofkinesin - Zeynep Erge Akbaş Erge Zeynep ] Lee CK, Lee CK, ] ] Haliloglu T, T, Haliloglu ] ] Karle KN, Möckel D, Reid E, Schöls L. Schöls E, Reid D, Möckel KN, Karle ] ] ] ] Bahar I,Atilgan AR, Erman DirectB. evaluation mail: mail:

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POSTER PRESENTATIONS 211 . For . For [1] . For the selected 8 different the selected 8 For . 3 . It is still unclear, and subject of a heated debate, and subject of a heated debate, . It is still unclear, 1,2 [2] performance measure. Table 1 shows that the microbiome features shows that 1 performance measure. Table learned to classify a disease can be used to classify other diseases with minimal fine tuning on the classifiers. Our results suggest that a model trained to learn dysbiosis patterns predict another disease accurately to an of a disease can be used to certain chronic disorders extent. Therefore, it is plausible to claim that mechanismsshare similar pathological impacting the homeostasis transferred to the remaining disease classifications and decision tree classifiers were trained solely on these scores to classify the other diseases. predictive model trained to evaluate the Therefore, the dysbiosis of one disease is used to predict another disease outcome. using subsampling The experiments were conducted independently 20% of the samples were used overcome unbalanced subsets, and to as test samples. Area under ROC curve was used as the classification sequencing which contains data the taxonomic profiles of around individuals’ 16000 gut microbiomes as well as their were metadata Project from American Gut obtained 1), disease detection was performed using chronic diseases (Table microbiota gut classifier the Network models, taking Artificial Neural disease/healthy taxonomy vectors as input and returning diagnostics. disease model were later a for The dysbiosis scores generated whether dysbiosis is a specific response, forming uniquely for different different for specific response, forming uniquely whether dysbiosis is a to common follows certain patterns if it individuals, or diseases and to driven approach data proposes a multiple diseases. This study investigate the latter hypothesis, and the results support that certain diseases exhibit shared dysbiotic components. The experimental set up is conducted as follows. The 16S rRNA and signalling pathways, over a complex interactome. However, in over a complex interactome. However, and signalling pathways, imbalance either as is subject to disease, this homeostasis case of or a complicationthe cause, a driver The resulting of the disease. Since microbiome is a gut phenomenon is defined as “dysbiosis”. microorganisms, carrying of thousands complex ecosystem with millions traits, biomarkers or measurement no quantitative of genes, detect even successfully or characterize, indices has been to defined dysbiotic states The last decade has witnessedThe last decade has journey of human the enthusing relation with human health. The current and its microbiome rediscovery, assumption is that human microbiome, especially the gut microbiome 90% of the defined chronic is associated with over disorders microbiome human host and the gut healthy individuals, is in a health via interacting metabolic promoting homeostatic equilibrium, COMPLEX DISEASES NALBANTOĞLU Özkan Ufuk Kayseri, Turkey Erciyes University, Engineering Department, 1. Computer Turkey Center (GenKök), Erciyes University, Kayseri, 2. Genome and Stem Cell INVESTIGATING COMMON PATTERNS OF GUT GUT OF PATTERNS COMMON INVESTIGATING OF A RANGE OVER DYSBIOSIS MICROBIOME POSTER PRESENTATIONS 212 bioinformatics/, [email protected] and Stem CellCenter(GenKök),http://genkok.erciyes.edu.tr/en/ Address: Author’s Corresponding mSystems 3.3(2018):e00031-18. citizen sciencemicrobiomeresearch." gut: an openplatform for et al."American [3] McDonald,Daniel, Nature microbiology1.12(2016):16228. Scott W.,[2] Olesen, andEricJ.is notananswer." Alm."Dysbiosis Journal ofExperimentalMedicine216.1(2019):20-40. microbiome: Relationshipswithdiseaseand opportunities for therapy." References: corresponding row. model ofthe generated bythedisease score only thedysbiosis show theAUC valuesobtained for the remainingdiseases using first columnistheAUC valuefordiagnosis.Theremainingcolumns and the to adisease on gutmicrobiotadata. Each rowcorresponds Table1: Human GutMicrobiota Keywords: interactome. on themicrobiomeinorderto maintain ahealthyhuman-microbiome and theuropathic interventions prospective researchonthepreventive searching forthedefinitionof a healthy gut microbiome, aswellthe of gut microbiome. Further implicationsofthishypothesiscouldbe http://genkok.erciyes.edu.tr/en/bioinformatics/ address author’s Corresponding research." microbiome science citizen for platform open mSystems 3.3(2018): e00031-18. an gut: "American al. et Daniel, McDonald, [3] [2] Olesen, Scott W., Eric and J. isAlm. answer." not "Dysbiosis an Nature 16228. microbiology 1.12(2016): for therapy." Experimental of Medicine Journal 216.1(2019): 20-40. [1] References: disorders share similar pathological mechanisms impacting the homeostasis of gut microbiome. Further microbiome. gut of homeostasis a of definition the for the searching be could hypothesis this impacting of implications mechanisms pathological similar share disorders predict another disease accuratelylearned can a to be classify to disease used classify diseases tuning other with classifiers. fine onthe minimal toclassificationthe as performanceused was curvemeasure. ROC Table anmicrobiome featuresthe that shows 1 extent.using independently conducted Therefore,were samples. test as under used were Area samples the of 20% and experiments subsets, unbalanced overcome to subsampling The outcome. disease another predict to used it is is plausibledisease one of dysbiosis the evaluate to trained model predictive the Therefore, diseases. other these to the classify on to claim scores solely trained were classifiers tree thatdecision and classifications disease certainremaining the to transferred chronicinput returning and disease/healthy diagnostics. The dysbiosisscores generated disease for model a were later as vectors taxonomy microbiota gut the taking models, classifier Network Neural Artificial using performed from selectedtheAmericandifferentProject For8 [3].Gut chronicdiseases (Table disease1), detection was individuals’ 16000 around of profiles taxonomic the were obtained metadata their as wellas microbiomes gut follows it if latter and results support hypothesis, the that certain diseases exhibit shareddysbiotic components. or individuals, and diseases diseases. multiple different to common for patterns certain uniquely forming response, specific a whether debate, is heated dysbiosis a of subject and unclear, still is It [2]. states dysbiotic detect even or characterize, to defined successfully been has indices measurement or biomarkers traits, quantitative no genes, carrying of microorganisms, millions of thousands with ecosystem complex a is microbiome gut Since “dysbiosis”. as gut defined the is phenomenon resulting disease. The the of complication a or driver a cause, the as either imbalance especially microbiome, to subject is homeostasis this disease, of case in However, interactome. human complex a over pathways, signalling that and metabolic interacting via health promoting is equilibrium, homeostatic a in is microbiome gut assumption the and host current The human individuals, healthy For [1]. disorders chronic defined the of 90% over health. with associated is microbiome human with relation model of corresponding row. the disease the by generated score dysbiosis the only using diseases remaining the for obtained values AUC the show columns remaining The diagnosis. for value AUC the is column first the and disease a to corresponds 1. Table Keywords: to order in microbiome the on interventions interactome. theuropathic human-microbiome ahealthy maintain and preventive the on research prospective the Disease Autism Alzheimer Cardiovascular disease Lung Thyroid Cancer IBD Diabetes Durack, Juliana, and Susan V. Lynch. "The gut microbiome: Relationships with disease and opportunities V.Susan and disease and with Juliana, Relationships Durack, microbiome: Lynch. gut "The Investigating Common Patterns of Patterns Common Investigating Gut Microbiome Dysbiosis Over a Detection performance of diseases using predictive models on gut microbiota data. Each row Each data. microbiota gut on models predictive using diseases of performance Detection microbiome, machine microbiome, learning, chronic diseases, gut human microbiota Our results suggest that a model trained to learn dysbiosis patterns of a disease can be used to contains which data rRNAsequencing 16S The follows. as conducted is up set experimental The its and rediscovery, microbiome human of journey enthusing the witnessed has decade last The Range of ComplexDiseases 2 Detection performanceof diseases usingpredictivemodels Genome andStemGenome Center Cell Erciyes (GenKök), University, Kayseri, Turkey 1 Computer Engineering Department, Erciyes University, Kayseri, Turkey 0.89 0.93 0.69 0.80 0.79 0.70 0.89 0.88 Microbiome, MachineLearning, Chronic Diseases, [1] Durack, Juliana,and Susan V. Lynch. "Thegut Diabetes 0.55 0.57 0.73 0.71 0.73 0.69 0.59 - Özkan Ufuk NALBANTOĞLU Ufuk Özkan IBD 0.61 0.50 0.62 0.53 0.61 0.56 - 0.73 ecys Uiest, Gnm n tm Cl etr (GenKök), Center Cell Stem and Genome University, Merciyes Cancer 0.59 0.52 0.66 0.56 0.70 - 0.64 0.69 , [email protected] This study proposes a data driven approach to investigate the investigate to approach driven data a proposes study This Thyroid 0.53 0.54 0.62 0.56 - 0.69 0.53 0.63 Lung disease Lung 0.51 0.58 0.52 - 0.52 0.53 0.65 0.63 Merciyes University, Genome 1,2 healthy gut microbiome gut healthy Cardiovascular 0.56 0.58 - 0.60 0.59 0.68 0.56 0.69 , as well as well as , Alzheimer 0.56 - 0.61 0.57 0.59 0.65 0.60 0.61 Autism - 0.58 0.73 0.63 0.60 0.90 0.56 0.73 POSTER PRESENTATIONS 213 . (2) SimBoost: (2) . [1] . (3) MoleculeNet: . , 4 [2] , Maria Martin 2,3 1 , Tunca Doğan , Tunca 1 Binding Affinity Prediction, Chemogenomics, Receptor, Chemogenomics, Receptor, Binding Affinity Prediction, , Volkan Atalay 2 ere, we propose a multi-channel receptor-ligand binding affinity binding a multi-channelere, we propose receptor-ligand methods in majority of the cases. Pursuing this approach, new models can be constructed by incorporating additional types of input protein channels. Keywords: Convolutional Neural Networks, Pairwise Ligand, Deep Learning, Encoding. Input Neural Network, Protein networks and 1-D protein and compound encoding compound and protein 1-D networks and on gradient based another binding affinity prediction method machines similarityand boosting networks a benchmarking platform designed for evaluating and testing predictions, which methods for molecular property computational convolutional graph popular employ include prediction models that 1. Results networks [3]. The performance results are given in Table than the other performs significantly better method our show that which neural are fed to a feed-forward network. Overall, we created architecture which starts with a hybrid pairwise input neural network separate ligand and protein branches, fully connected layers which a regressor and processes protein+ligand vector, the concatenated We layer. the output binding affinity value at predict the actual to methods: (1) DeepDTA: compared our system with three different on convolutional neural a binding affinity prediction method based the state-of-the-art Davis (3) PDBBind (1) Davis (2) Filtered methods: describe encoding method where each protein is a new protein We represented which as a matrix constitute the base channel of the also created additional input convolutional system. We part of the channels on pre-definedbased amino acid matrices which represent proteins. In the ligand side, various properties of amino acids and ligands, strings of SMILES fingerprints using the ECFP4 generated we prediction method. Our system employs a chemogenomic prediction method. modeling ligand and receptor (target) use both where the aim is to approach ligand and incorporating both of advantage features as inputs. One affinities the system can predict binding of receptor features is that even if the corresponding ligand and/ pair, any given receptor-ligand used We training set. in the point data have any not does receptor or results with our compare to and system train our three datasets to H 2. Cancer Systems Biology Laboratory (Kansil), Graduate School of Informatics, (Kansil), Graduate School of Informatics, Middle East Systems Biology Laboratory 2. Cancer University, 06800 Ankara, Turkey Technical Hacettepe University, 06800 Ankara, Turkey 3. Institute of Informatics, Bioinformatics Institute European Biology Laboratory, 4. European Molecular (EMBL-EBI), CB10 UK 1SD Hinxton, Cambridge, MODELING Rifaioglu Ahmet Sureyya Rengül Çetin-Atalay University, 06800 Ankara, Turkey Engineering, Middle East Technical 1.Department of Computer RECEPTOR-LIGAND BINDING AFFINITY PREDICTION AFFINITY PREDICTION BINDING RECEPTOR-LIGAND CHEMOGENOMIC DEEP MULTI-CHANNEL VIA POSTER PRESENTATIONS 214 [email protected] Address: Author’s Corresponding learning,” Chem.Sci.,vol.9,no.2,pp.513–530,2018. Wu[3] Z. etal.,“MoleculeNet:Abenchmarkformolecularmachine no. 1,pp.1–14,2017. J.using gradientboostingmachines,” affinities vol. 9, Cheminform., approach forpredictingdrug-target“SimBoost: aread-across binding [2] T. He,M.Heidemeyer, F. Cherkasov, Ban,A. andM.Ester, no. 17,pp.i821–i829,2018. Deep drug-targetvol. 34, bindingaffinityprediction,”Bioinformatics, References: Table 1: networks, pairwise input neural network, protein encoding. frac rsls r gvn n al 1 Rsls hw ht u mto performs method our Keywords that show Results 1. constructed by incorporating additional types of Tableinput protein channels. in given are significantly results better than the other methods in majority of the cases. performancePursuing this approach, new models can be The for methods convolutional [3]. graph computational popular networks employ testing that models and prediction include evaluating which predictions, for property molecular designed platform benchmarking machines a boosting MoleculeNet: gradient on based method prediction affinity binding 1- and networks neural convolutional on based system our compared We valu affinity binding actual the predict to regressor a and vector, protein+ligand concatenated the processes which layers connected fully branches, protein and ligand netw neural input pairwise hybrid a created we Overall, network. we generated ECFP4 fingerprints using the SMILES strings of ligands, which are fed to a feed defined amino acid matrices pre on based channels input additionalcreated also We system. the of part convolutional the of channel base We describe a new protein encoding method where each protein is represented as a matrix which constitute the Bioinformatics, vol. 34, no. 17, pp. i821 pp. 17, no. 34, vol. Bioinformatics, [1] H. Öztürk, A. Özgür, and E. Ozkirimli, Deep drug“DeepDTA: References: state binding set. training predict the in point can data system the that is features receptor affinities of any given re and ligand both incorporating of advantage One chemogenomic modeling approach where the aim is to use both receptor (target) and ligand features as inputs. Corresponding author’s address 513– [3] Z. etWu al., “MoleculeNet: A benchmark for molecular machine learning,” Chem. Sci., v 1 predicting drug- Est M. and Cherkasov, A. Ban, F. Heidemeyer, M. He, T. [2] multi a propose we Here, Table 1. 1. Table PINN (Davis) Method ECFP -RF(PDBBind) ECFP4 -RF(PDBBind) Grid Featurizer-DNN(PDBBind) Grid Featurizer-RF(PDBBind) PINN (PDBBind) DeepDTA (filt.Davis) PINN (filt.Davis) SimBoost (Davis) DeepDTA (Davis) – 14, 2017. 4 2 PredictionMultivia

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL - Cancer Cancer Systems Biology Laboratory (Kansil), Graduate School of Informatics, Middle East Technical 530, 2018. Receptor of 1

Department of Computer Engineering, Middle East Technical University, 06800 Ankara, Turkey - the Performance comparison with state with comparison Performance : - Binding affinity prediction, chemogenomics, receptor, ligand, deep learning, convolutional neural convolutional learning, deep ligand, receptor, chemogenomics, prediction,affinity Binding

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AhmetSureyya Rifaioglu

target binding affinities using gradient boosting machines,” J. Cheminform., vol. 9, no. 1, pp. ChemogenomicModeling [1] H. Öztürk, A. Özgür, [1]H. Öztürk, A. “DeepDTA: Ozkirimli, and E. 3

Institute of Informatics, Hac ceptor - channel receptor channel with three different methods: (1) DeepDTA: a binding affinity prediction method prediction affinity binding a DeepDTA: (1) methods: different three with which represent various properties of amino acids and proteins. In the ligand side, RengülÇetin

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- POSTER PRESENTATIONS 215 2 [email protected] , Mehmet Somel , Mehmet 2,3 , Füsun Özer 1,2 admixture in Anatolia of the last 15,000 years. admixture in Anatolia of the last 15,000 Corresponding Author’s Address: dated back to the Mesolithic/Epipaleolithic Period and proceeded into and the Mesolithic/Epipaleolithic Period back to dated gene pools general trend, as a find that, We the Medieval Period. further suggest over time. Our preliminary results have diverged past the during Iran and Anatolia lived in who the populations that gene years may have changed more dramatically than the 15,000 pools of neighbouring regions, most likely as a result of gene flow describing a timeline conclude by of major from external sources. We over time. This observation has led us to hypothesize that the gene that hypothesize to has led us over time. This observation those of be more dynamic compared to pool of Anatolia may intermediate Anatolia’s possibly because of neighbouring regions, this perspective we investigated geographical location. From gene pool by calculating the genetic temporal changes in a region’s published ancient dissimilarity in time using 164 among individuals four neighboring regions: genomes from Anatolia and also from the Aegean. The individuals studied Caucasus, and Iran, the Levant, Past demographic changes and migrations can be traced using migrations changes and demographic Past modern genomes, as well In recent as ancient years, genome data. central west and from populations human of ancient genome analyses a series Anatolia have revealed notable results, of which suggested sources from different geographic into Anatolia continuous gene flow YEARS Dilek Koptekin 06800, Ankara, Turkey University, Middle East Technical of Health Informatics, 1. Department University, 06800, Ankara, Turkey Sciencess, Middle East Technical 2. Department of Biological Ankara, Turkey Hacettepe University, Beytepe, 06800 3. Department of Anthropology, INVESTIGATING GENETIC CONTINUITY OF HUMAN HUMAN OF CONTINUITY GENETIC INVESTIGATING 15,000 THE PAST OVER IN ANATOLIA POPULATIONS POSTER PRESENTATIONS 216 1. İstanbulSehirUniversity,ComputerScienceandEngineeringDepartment Sahin Sarıhan HETEROGENEITY LEVELS ON PATIENT WITHDIFFERENT SAMPLES EVALUATION SEQUENCING OF CANCER PIPELINES compared the mutation lists by pairwise comparisons (Figure 1). Then, Bowtie2_Varscan, Bowtie2_Strelka2”. For thesepipelines, firstwe as “Bwa_Mutect2, Bwa_Varscan, Bwa_Strelka2,Bowtie2_Mutect2, This resultedinsixmapping-variant callingcombinations labeled algortihms: Mutect2, Varscan,and threevariantcalling andStrelka. sample types;weappliedtwo mapping algorithms:Bwa, Bowtie2 for exome sequencing.enough cells Forfrom thesefour 55samples and subsequentculturinguntilthereis isolation ofsinglecells samples wereobtainefrominvitropolyclone through to mousebrainandformedatumor,injected vitro monoclone in (iv) were obtainedfrommousexenograftsafterinvitropolyclones are from parental-tumors,(iii)invivopolyclonesamples lines stem cell of patients tumor, (ii) invitro polyclone samples are cultured tumor parental tumor samples wereobtained astissuesfromdifferentparts The dataset we usedinthisstudyhadfourdifferentsampletypes:(i) heterogeneity levels. us a unique opportunity to test sequencing pipelinesat different samples isalmost impossible. Availabilityof these samplespresented two independent in mutation twice algorithm identifyinganon-existing us to declare thesemutations as validated mutations; since for an These samplessharea substantial portion of mutations which allows that belongs toaglioblastomapatient[3]. samples heterogeneous homogeneous and published adatasethas55high-resolution which of genetic instabilityand high level heterogeneity dynamics and in cancersampleswithcomplexselection especially human sequencingdata constructing realistic is extremelydifficult, from primitiveorganismssuchasbacteria or yeast. Unfortunately and mostofthesestudiesrelyonsimulateddata research or data pipelinesisanactivefieldof these sequencing Benchmarking is crucialforefficientutilizationofsequencingtechnologies. the appropriate algorithms with optimal filters in a sequencing project might varysignificantly.obtained results therefore choosing Therefore, these steps available for and therearemanyalgorithms pipelines analyses these information. Mappingarethetwo main stepsof and variantcalling within apipeline,inorder to convert raw data to meaningful and WES mustbeprocessedby various bioinformaticsalgorithms variant detectionincancerresearch.Thesequencedraw data by WGS Sequencing (WGS)are the most common methods for comprehensive Genome and Whole (WES) Exome Sequencing Whole researchers. research asaccessingthiscentraltechnologybecomeseasierfor The importanceofNextGenerationSequencing(NGS)risesincancer 1 , BatuhanKısakol 1 . Thesealgorithmshavedifferentapproachesand filters, 1 , MehmetBaysan 1 [2] . Recently, we have POSTER PRESENTATIONS 217

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[1] Roberts ND, Kortschak RD, Parker WT, Schreiber AW, Schreiber AW, WT, Parker RD, Kortschak [1] Roberts ND, Sahin Sahin İstanbul Sehir University, Computer Science and Engineering Department Engineering and Science Computer University, Sehir İstanbul hms Clinical Bioinformatics, Next Generation Sequencing, Clinical Bioinformatics, Next Generation Sequencing, t 1 fferent Heterogeneity Levels tumors, (iii) tumors, -

epeated events in primary tumor and recurrence.epeatedin events formation 14;141: J. Int. 2002 2017 Jul Cancer . Clinical Bioinformatics, tion homogeneous andheterogeneous samplesthat belongs to a glioblastomapatient

Di variant calling combinations labeled as “Bwa_Mutect2, Bwa_Varscan, Bwa_Strelka2, Bwa_Strelka2, Bwa_Varscan, “Bwa_Mutect2, as labeled combinations calling variant

- . d mutations. d mutations. Evaluation Cancer Sequencing of Pipelines on Patient Samples with resolu - Roberts ND, Kortschak RD, WT, Parker AW, Schreiber S, Scott Branford G, DL. comparativeHS, Adelson Glonek A ] ] Kumaran M, U, Subramanian Devarajan PerformanceB. assessmentcalling pipelines of variant human whole using , S. , Ahn A. , Linkous D. , Balamatsias S. , Kotliarova H. , Song W. Zhang C., M. , Cam K. , Woolard M. ] Baysan ] [2 exome and sequencing simulated data. Bioinformatics. BMC 17;2 2019 Jun [3 Detailed G.J.Walling A. longitudinal I. and H. samplingFine , of situ reveals stem Belova glioma Chr7 and in cells gain Chr10 asloss r PubMed 28710771. PMID: Corresponding author’saddress Baysan Mehmet Prof. Asst. Kartal Dragos, 21, No: Bulvarı, Özal Turgut Mahallesi, Orhantepe +90Tel: 216559 9000 (ext. 9736) +90 Fax: 474 2165353 Email: validate for differently perform algorithms calling variant and mapping different that observed we analyses, our In different heterogeneity levels. Thissuggests attaching to a single pipeline isnot optimal for cancer sequencing h sample and analyses more accurate variantdetection andbetter resultsin clinical studies. Keywords: Discovery Algori References: [1 algorithmsanalysis of somatic cancer. detection SNV for Bioinformatics.in 2013 Sep 23842810 Benchmarking these sequencing pipelines is an active field of research and most of these studies rely on on rely studies these of most and research of field active an is pipelines sequencing these Benchmarking simulateddata or data fromprimitive organisms such as bacteria dynamics or yeast. Unfortunate selection complex with samples cancer in especially difficult, extremely is data sequencing human instability genetic of level high and high samplesashare substantial portion ofmutations which usdeclare allows tothese mutations as validated non a identifying algorithm an for since mutations; t of Availability impossible. almost at different heterogeneity levels. (i) types: sample different four had study this in used we dataset The tissuesfrom different parts of from parental mouse to injected are polyclones enough is there until culturing subsequent and cells single of isolation through samples polyclone vitro in from mapping algorithms two applied we types; sample four these from 55 samples For sequencing. for exome cells six in resulted This Strelka. and Varscan, Mutect2, algortihms: calling variant three and Bowtie2 Bwa, mapping these For Bowtie2_Strelka2”. Bowtie2_Varscan, Bowtie2_Mutect2, comparisons pairwise by lists independent samplesas “validated” mutations and evaluated the performance of each pipelinedetecting on The importance of Next Generation technology becomes easier for researchers. Whole Exome Sequencing (WGS) are the most common methodsfor comprehensive variant order in pipeline, a within algorithms bioinformatics various by processed be must WES and WGS by data raw these of steps main two the are calling variant and Mapping information. meaningful to data raw convert to the and pipelines analyses approachesand filters, therefore obtained i project sequencing in a filters optimal with algorithms technologies

14;141: 2002-2013. PubMed PMID: 28710771. Corresponding Author’s Address: Orhantepe Mahallesi, Turgut Özal Bulvarı, No: 21, Dragos, Kartal – İstanbul, 34865 Office: AB4-4017 +90 Tel: 216 559 9000 (ext. +90 216 474 53 53 Email: [email protected] 9736) Fax: sequencing and simulated data. BMC Bioinformatics. BMC Jun sequencing data. 2019 and simulated 17;20(1):342. PubMed PMID: 31208315. , , Song H. K. W. C., Zhang , Cam M. , Woolard [3] Baysan M. , J. Linkous A., Walling , Ahn S. , Balamatsias D. , S. Kotliarova A. H. Detailed longitudinal sampling glioma of I. and Fine Belova G. events repeated loss as Chr10 gain and reveals Chr7 stem cells in situ Jul Cancer 2017 in primary tumor formation and recurrence. J. Int. Cancer, Mapping Algorithms, Variant Discovery Algorithms Discovery Variant Mapping Algorithms, Cancer, References: Adelson DL. analysis comparative A Glonek G, HS, Scott S, Branford Bioinformatics. in cancer. of algorithms for somatic SNV detection PMID: 23842810. 2013 Sep 15;29(18):2223-30. PubMed Performance Devarajan B. M, Subramanian U, [2] Kumaran assessment of variant calling pipelines using human whole exome Keywords: In our analyses, we observed that different mapping and variant heterogeneity differently for different perform calling algorithms for optimal pipeline is not single a to suggests attaching levels. This cancer sequencing analyses be heterogeneity should and sample considered to more Hopefully this will lead optimization. in algorithm and better results in clinical studies. accurate variant detection we declared the mutations which are detected in two independent independent in two are detected which mutations the we declared “validated” as samples of performance the evaluated and mutations mutations. on detecting validated each pipeline POSTER PRESENTATIONS 218 1,2. İsra Mavaldi METABOLITES OF GUTMICROBIOTA REDUCTION OF DEPRESSION SYMPTOMS VIATHE Yildiz Technical University,DepartmentofBioengineering,Istanbul,Turkey 1 [email protected] Department ofBioengineering, Davutpasa, Istanbul, Turkey Address: Author’s Corresponding vol. 2015,pp.1–12,2015. Longev., Oxid.Med.Cell. Disorder’, Depression in Neuroprogression Oxidative Stressand [2] M.Vaváková, Z. https://www.who.int/mental_health/management/depression/en/. References: Kynurenine Pathway. Keywords: enzymes expressedbyeightdistinctspeciesofgutbacteria. We endedwiththreedistinctreactionscatalyzedby two distinct Microbiome databases.We usedRfordataanalysissteps. Uniprot,and Brenda, ChEBI,MetanetX, many databases including the catalyzingenzymes.Thesedatasetsaretakenfrom by expressing products thatgutbacteriacancatalyzethem their including reactions about So, wecombinemanydatasetsthatcontaininformationthe pathway withserotoninsynthesisfromtryptophan. degradation which iscalledkynureninepathway that is a competitive microbiota that can by elevate serotoninlevels inhibiting tryptophan In this study, we aimed to reveal possiblemetabolitesproduced by gut same time,thesetreatmentshavemanysideeffects but at the Most ofthetreatmentsaimedtoelevateserotoninlevels controlling neurotransmitter, depression alsocalledmood disorder. low levels of serotonin are detected and because serotoninisa mood patients most depressive unknown, butin is depression behind reason all agessufferfromdepression people of Health Organization,globallymorethan300 million mental disorderandaccordingtoWorlda common is Depression , AlperYılmaz Depression, Serotonin, Tryptophan, Depression, Gut Microbiota, [1] ‘WHO |Depression’. [1]‘WHO [Online].Available: 2 Ď rčoá ad . rbtcá ‘akr of ‘Markers Trebatická, J. and uračková, [1] . Eventhough, until nowthereal YildizTechnical University, [2] . POSTER PRESENTATIONS 219

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Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. “The Ensembl Ensembl “The F. Cunningham P, Flicek A, Thormann GR, HS, Ritchie Riat SE, Hunt L, Gil

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- , M. Hamza MUSLUMANOGLU , M. Hamza NGS, Snakemake, and we [2] practices GATK best from inspired workflow Snakemake an existing We modified user a any As can result, Next Generation Sequencing (NGS) technology has boosted genetic research. especially allowing especially research. genetic boosted has (NGS) technology Sequencing Generation Next 1 [3] Oguzhan Automation of Automation Yildiz Technical University, Faculty of Arts and Sciences, Department of Molecular Biology and Genetics, and Genetics, Biology Molecular of Department and Sciences, of Arts Faculty University, Technical Yildiz 1 A, Flicek P, Cunningham F. “The Ensembl Variant Effect Predictor”. “The Ensembl Variant Cunningham F. A, Flicek P, doi:10.1186/s13059-016- 17(1):122 2016. Genome Biology, M, Guttman Winckler W, H, Thorvaldsdóttir [4] Robinson JT, 0974-4 “Integrative Genomics Viewer”. Nature Mesirov JP. Getz G, ES, Lander Biotechnology 29, 24–26 (2011) [5] https://github.com/igvteam/igv- reports Corresponding Author’s Address: Figure 1. References: 28(19):2520- 2012. bioinformatics workflow engine”, Bioinformatics, 2522, https://doi.org/10.1093/bioinformatics/bts480 [2] https://github.com/snakemake-workflows/dna-seq-gatk-variant- Gil L, Hunt SE, Ritchie GR, Thormann Riat HS, W, calling [3] McLaren analysis and generating reports in automated way. More importantly, More importantly, way. generating reports in automated analysis and using will prevent a workflow approach errors even large though number of samples are processed. Keywords: As a result, any user can clone our code and then with any raw annotation and report generation fastq file, initiate the mapping, This approach is it can be reproducible and portable, steps easily. Since implemented laptop, server in a personal or even a cluster. Snakemake jobs, the SNP analysis can be done in can run parallel approach Such an are available. parallel fashion if multiple CPUs will allow a user or genetic analysis center to save time by running We modified an existing Snakemake workflow inspired from GATK modified an existing Snakemake workflow inspired from GATK We best practices (VEP) integrated Integrative Genome Viewer (IGV) javascipt library end user dynamically analyze the results. R script so that by generated research. especially allowing fast and accurate diagnosis. However, However, accurate diagnosis. research. especially allowing fast and analysis of multiple samples by passing multiple steps them through aim to we this study In error-prone. and in commandline is tedious facilitate analysis of NGS data with Snakemake Snakemake workflows. Additionally, Python-based management of environments benefits from conda thus installation or configuration becomes effortless.. of numerous softwares Next Generation Sequencing (NGS) technology has boosted genetic (NGS) technology has boosted Next Generation Sequencing Corresponding author’s address [email protected] References: [1] Köster J, Rahmann S. “Snakemake 28(19):2520 https://github.com/snakemake [2] [3] McLaren W, Variant Effect Predictor”. G, Me ES, Getz Lander M, Guttman W, Winckler H, Thorvaldsdóttir JT, Robinson [4] Viewer”. Genomics [5] https://github.com/igvteam/igv Keywords: chart 1. Workflow Figure integrated ENSEMBL Variant Effect Predictor (VEP) [3] into annotationwe stepintegrated of the workflow. Integrative results. Genome the analyze Viewer user dynamically end (IGV) that so R script by [4] generated in tables HTML final interactive reportincludes via javascipt library [5]. Final report also annotation and report generation steps easily. This run approachcan Snakemake Since a or cluster. iseven reproducibleserver laptop, a in personal and portable, it can be implemented analysis a user genetic or allow will an approach Such available. are CPUs if multiple fashion in parallel done using importantly, More way. automated in reports generating and analysis running by time save to center workflowapproach willprevent errors eventhough large number of samplesare processed. them t by passing samples multiple of analysis However, diagnosis. accurate and fast commandline is tedious and error Python of management provides which [1] Snakemake thus environments conda from

Oguzhan KALYON Oguzhan 1. Yildiz Technical University, Faculty of Arts and Sciences, Department of Molecular Biology and Department of Faculty of Arts and Sciences, University, 1. Yildiz Technical Turkey Genetics, Istanbul, Turkey Department of Bioengineering, Istanbul, University, 2.Yildiz Technical AUTOMATION OF GENETIC TESTING AND AND TESTING GENETIC OF AUTOMATION SNAKEMAKE WITH REPORTING POSTER PRESENTATIONS 220 Ali AydinlarUniversity 3. Department of Biostatistics and Bioinformatics, HealthSciences Acibadem Institute, Mehmet Ali AydinlarUniversity 2. Department of Biostatistics and Bioinformatics, HealthSciences Acibadem Institute, Mehmet of EngineeringandNaturalSciences,SabanciUniversity Molecular Biology,GeneticsandBioengineering/ComputerScience&Engineering,Faculty 1. Yasemin Utkueri DISEASES USING TF-IDF SCORING SYMPTOM RANKING ALGORITHM FOR RARE the top symptoms ofeachdiseaseare different. TheTF-IDF scoring are variationsofparkinson’spostencephalitic parkinsonism disease, onset parkinson’s.and parkinsonism Althoughatypicaljuvenile disease, atypical and juvenile parkinsonism hereditary late- by out algorithm forthreediseasesthat are very similar, parkinson’s Tablecalculated 1shows thetop10symptomswithlowestscores closely associatedwiththedisease. lowest. These symtpom are ranked the highest becausethey are more of the symptomsthat are most commonly seeninthecorpus arethe TheTF-IDFmade upofabstracts extractedfrompub-med. is scores In ourstudy,by multiplyingthe TFandIDFscores. disease thecorpus we cancalculatethetotalTF-IDFof asymptomforthegiven score will be.Withthetwo scores, the IDFscore the termis,smaller it divided by the total number of documents. [3,4] The morefrequent by takingthelogarithmofnumberdocumentswith termin calculated is of theimportanceaterm. This the score is frequency document divided bythenumberofdocumentsincorpus.Inverse symptom, appearsinadocument.Tothe score,countis normalize frequency isthescoringofhow many timeseachterm,inthiscasethe document frequency.two parts: termfrequencyandinverse Term on theirfrequencyinliterature.Thescoringprocessismadeupof scoring,based to rankthesymptomsofararedisease itispossible With theTF-IDF (TermFrequency Document Frequency) –Inverse ranked asmostimportant. disease and expect symptomsparticularto a rarediseaseareto be Wealgorithms. retrieval to the regard importanceasrelevance diseases that is scoredand sorted by their importanceviainformation eliminating inaccurate diagnosis by creating a list of symptoms for to help study aims preliminary treatments[2].This through incorrect of this,patientscannotbediagnosedaccuratelyandhadto go finding curesand offering treatments especially difficult.Because them fromotherdiseasesremainschallenging. And, this makes Identifying casual symptomsparticular to a disease and differentiating is hardto accomplish. diagnosingrarediseases high prevalence, populations comparedtoEuropeancountries.Inspiteofrelatively in those has relativelyhighprevalence common, rarediseases Turkeylike In countries marriages aremore consanguineous where affect lessthan1in2000 threatening [1]. people, aremostlylife R are diseases, which European Union defines as the diseases that are diseases,whichEuropean Union definesasthediseases 1 , HüseyinOkanSoykam 2 , UğurSezerman 3 POSTER PRESENTATIONS 221

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In countries like Turkey where consanguineous marriages are more common, rare diseases diseases rare common, more are marriages consanguineous where Turkey like countries In [1] Rare Disease UK [Internet]. London: Rare Diseases [1] Rare Disease [Internet]. London: UK . London: Rare Diseases UK. Available from: https://www.raredisease.org.uk.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. (Term Frequency Frequency (Term The more frequent the term is, the smaller the IDF score will be.IDFwillsmallerscorethethe moreThe is, termfrequentthe , , parkinson’s disease, atypical juvenile parkinsonism and hereditary late [1] . ] Ramos, J. (n.d.). Using TF Using (n.d.). J. Ramos, Yasemin Utkueri Yasemin

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32. : Marmiesse A, Gouveia S, TechnologiesCouce ML. NGS as a Turning Point in Rare Disease Research , Diagnosis and Treatment. Cur Table showing the top 10 symp 10 top the showing Table Molecular Biology, Genetics and Bioengineering / Computer Science & Engineering , Engineering & Science Computer / Bioengineering and Genetics Biology, Molecular disease. the with associated losely

1 Symptom Ranking Algorithm for Rare Symptom Ranking threatening Departmentof Biostatistics and Bioinformatics, Departmentof Biostatistics and Bioinformatics, Rare Disease UK [Internet] UK Disease Rare Fernandez-

2 3 our study, the corpus is made up of abstracts extracted from pub from extracted abstracts of up made is corpus the study, our by by taking the logarithm of the documents. calculate the total TF OrtaMahallesi, Sabancı Ünv. No:27, 34956 Tuz [email protected] frequency frequency and inverse document frequency this case the symptom, appears in a document. corpus. the in documents of a rare disease based on their frequency in literature. Rare diseases, which European Union defines as the diseases that affect less than 1 in 2000 people, are mostly mostly are people, 2000 in 1 than less affect that diseases the as defines Union European which diseases, Rare life high relatively of spite In countries. European to compared populations those in prevalence high relatively has is diseases rare diagnosing prevalence, and them differentiating from other diseases remains challenging. And, this makes finding cures and offering diagn be not can patients this, of Because difficult. especially treatments list a creating by diagnosis inaccurate eliminating help to aims study preliminary This treatments[2]. incorrect of symptoms for diseases that is scored and sorted by their importance via information retrieval algorith We regard importance as relevance to the disease and expect symptoms particular to a rare disease are to be important. most as ranked TF the With 2018;25(3):404- Using [3] Ramos,J. [4] Salton, G. & Buckley, C. (1988). Term (1988). C. Buckley, & G. Salton, [4] References: [1] address author’s Corresponding [2] [2]

the corpus are the lowest. lowest. the are corpus mostcommonly are the in seen c more Table 1 shows the top 10 symptoms with the lowest scores calculated by out algorithm for three diseases that are very similar

from each other by highlighting the symptom the highlighting by other each from Keywords disease, disease, the top symptoms of each disease are different. Although Although atypical juvenile parkinsonism and

Table 1. postencephalitic parkinsonism postencephalitic Disease Rankof symptoms Corresponding Author’s Address: [email protected] No:27, 34956 Tuzla/İstanbul UK. Available from: https://www.raredisease.org.uk. A, Couce ML.[2] Fernandez-Marmiesse S, Gouveia NGS Technologies in Rare Disease Research and Treatment. , Diagnosis Point as a Turning Current medicinal chemistry 2018;25(3):404-32. to Determine (n.d.). Using TF-IDF Using [3] Ramos, J. [3] Ramos,J. C. & Buckley, Relevance in Document Queries. [4] Salton, G. Word text retrieval. approaches in automatic In (1988). Term-weighing & Management, 24(5): 513-523. Information Processing Table 1: Table and postencephalitic parkinsonism. atypical juvenile parkinsonism References: Symptom; Disease; Diagnosis Disease; Diagnosis Symptom; correctly correctly distinguishes diseases the other by highlighting each from disease. to that exact are specific that symptoms Keywords: POSTER PRESENTATIONS 222 Genetics, Istanbul,Turkey Technical2.Yildiz and Sciences,DepartmentofMolecularBiology of Arts Faculty University, 1. YildizTechnical University,DepartmentofBioengineering,Istanbul,Turkey Busra NurDarendeli SOLELY ON DNA SEQUENCES WITHWORD2VEC PREDICTION OF MUTATION SUSCEPTIBILITY BASED Keywords: dynamics ofmutationsingenomes. or the wayfor understandingtheunderlyingmechanism might pave or experimental data, solely using the genomesequenceitself. This mutation of or genomes without variation characteristics sequencing and other word embedding approaches canbe used to reveal 80% and70%,respectively.is SNP overlap vs. dna2vec Inconclusion, the SNPcount.Asaresultofour studies, humanand mouse, dna2vec for ak-mersimilarity wasalsoseento be theneighboroverlapping count k-mer.cosine Inotherwords,theneighborwithhighest k-mer, k-mersSNP with highest coincide similarity cosine with highest dna2vec cosinesimilaritydata. Our results showed that for a given 8-mer has been tabulated. These results are incorporated with are aggregated.awaythatnumberofSNPsresults such foreach k-merscoordinate, weextracted8nucleotide SNPsintersecting and coordinates of common and all SNPs fromdbSNP and mouse genomeviadna2vec.. On theother hand, we retrieved mutation susceptibility. Initially,we generatedword vectors forhuman In thisstudy,based predictionof weexaminedDNAsequence vectors revealunusualrelationshipsbetweenDNAk-mers. queries onDNA similarity are representedasvectors.Thecosine applies Word2vec approachtowholegenomesothatDNAk-mers betweenDNAk-mers.relationships yet tobediscovered Dna2vec studies tounravel subject tosimilar being alongtext,is Genome relationships. contextual andsemantic quite robustindiscovering is shallow neuralnetworkthat generates word embeddings. Word2vec Word2vecbeen unraveled. words have between relationships empowered withdeeplearningapproaches,moredetailed With the advent of natural language processing (NLP) techniques Workflow ofstudy 1 , AlperYILMAZ Figure 1. SNP, Word2vec, CosineSimilarity, K-Mer coordinates of common and all SNPs from dbSNP [3]. For each coordinate, we extracted 8 nucleotide 8 extracted we coordinate, each For [3]. dbSNP from SNPs all and common of retrieved we coordinates hand, other the On dna2vec.. via genome mouse and human for vectors word generated k DNA between k relations relationships. semantic and Genome beingcontextual a long text, discovering is in subject similarrobust to quite is studies Word2vec to unravelyet to be embeddings. discovered word generates that network unra been have words between relationships detailed more approaches, [email protected] address author’s Corresponding [3] arXiv:1701.06279. [2] Ng P. (2017). dna2vec: Consistent vector representations of variable arXivpreprint. 2013. arXiv:1301.3781. [1]Mikolov ChenK, T, Corrado G, Dean J.Efficient estimation of References: 1. Figure Keywords: itself. sequence genome the using migh This solely data, experimental or sequencing without genomes of characteristics variation or mutation reveal to be used can approaches embedding word other and dna2vec In conclusion, over SNP vs. dna2vec mouse, and human studies, our of result As a count. k a for similarity cosine highest the with neighbor k given 8 each for tabulated.SNPs of These resultsnumber are incorporated that way with a dna2vec such cosine similarity aggregated. data. Our are results showed results and that forSNPs a intersecting Based Solely Prediction of Mutation Susceptibility 2 - mers are representedvectors. as Thecosinesimilarity queries onvectors DNA reveal unusual relationships Yildiz Technical University, University, Technical Yildiz https://www.ncbi.nlm.nih.gov/snp - mer, k mer, hips between DNA k betweenDNA hips t t pave the way for understanding the underlying mechanism or dynamics of mutations in genomes. Workflow of study In this study, we examined DNA seque DNA examined we study, In this With the adventofnatural languageprocessing (NLP) techniquesempowered with deep learning SNP, Word2vec, cosine similarity, k similarity, cosine Word2vec, SNP,

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POSTER PRESENTATIONS 223 [email protected] [1] Mikolov T, Chen K, Corrado G, Dean J. Efficient Dean J. K, Chen G, Corrado T, [1] Mikolov [2] Ng P. (2017). dna2vec: Consistent dna2vec: Consistent (2017). vector representations of [2] Ng P. arXiv:1701.06279. arXiv preprint. 2017. variable-length k-mers. [3] https://www.ncbi.nlm.nih.gov/snp Author’sCorresponding Address: References: representations of word estimation preprint. arXiv space. in vector 2013. arXiv:1301.3781. POSTER PRESENTATIONS 224 University, 06800,Ankara,Turkey. Graduate School of Informatics, Department of Health Informatics,MiddleEastTechnical Bengi RukenYavuz MUTATIONS INPAN-CANCER DATA IDENTIFICATION OF LATENT DRIVER [email protected] Technical Cankaya/Ankara InformaticsInstituteB-204 University Address: Author’s Corresponding Bioinformatics Keywords: we labelthesemutationsasdriver, latentdriverorpassenger. of thesemutationsonkinaseactivityorDNAbindingand eventually the effects mutations tothe3Dstructureofproteinanddetermine acrossdifferenttumors.Weco-occur mapped these pairwiserecurrent whether anymutationpairs proteins mutations ondifferent we check in differentpatients.Next, in thesameproteincanrarelyco-occur As aresultweobtainedpairs thatmutationson residue relationships. Pan-Cancerthese in mutations exclusive dataset tofindoutepistatic a factor.transcription Additionally, we identifysignificantlymutually activity orfunctionas in morethan3patientsandinvolvedkinase Afterwards, weobtained the genesonwhichmutation pairs observed recurrent mutation pairs onwithinthesamegeneacrossallpatients. across 33 different cancer types. First, we identified thenumber of that containsofmorethan10,000 tumor-normalpairs exome structures. We performa Pan-Cancer AnalysisonMC3 Call Set using protein ones the latentdrivermutationsfrompassenger In thisstudy, weuseacomputational approach to differentiate mutations amongthepassengersisachallengingtask. latent these Differentiating drug resistance. developing networks in of oncogenic mutations canhelpto explain therewiringmechanisms of the proteinsand hence theirinteractions.Moreover, latent driver These cooperativelyactingmutationscanchangetheconformation newly evolvedmutation, they canexpressa cancer cellphenotype. not provide a growth advantage to cancer cellbut together with a defined asmutationsthatdoes is mutations” wasintroducedwhich driver a newconcept,“latent drivers orpassengers.Recently are variousdatabases that already labelled manymutations as There to occurbychance. is unlikely exact positionalrecurrence A central assumption used in discoveringdrivermutations is that tumor.mutations withinagiven from thebackgroundpassenger mutations driver identifying is genomics incancer A majorchallenge andNurcanTunçbağ Latent Driver Mutations; Pan-Cancer Analysis;Structural ucn uça Mdl East Middle Tunçbağ Nurcan POSTER PRESENTATIONS 225 Tıbbi Biyoloji Anabilim Dalı, . Here, human PPI data was integrated with human was integrated data . Here, human PPI [1] [1] Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Systems Biology; Protein-Protein Interactions Systems Biology; Protein-Protein 1 the human proteome. Science. 2015 Jan 23;347(6220):1260419. Science. Jan 23;347(6220):1260419. the human proteome. 2015 PMID: 25613900. Corresponding Author’s Address: Zonguldak Bülent Ecevit Universitesi Tıp Fakültesi Dekanlığı, Kozlu, Zonguldak, 67600, [email protected] References: References: Mardinoglu A, Sivertsson Å, C, Sjöstedt E, Kampf Asplund Oksvold P, A,CA,Szigyarto E, K, Edlund OlssonI, S, Navani Lundberg Odeberg Berling Edqvist PH, Alm T, Hober S, JO, Takanen Djureinovic D, J, Schwenk JM, Hamsten Nilsson P, Rockberg J, Mulder J, H, Tegel H, Zwahlen M, L, F, Johansson M, Persson K,M, von Feilitzen Forsberg von Heijne Proteomics. G, Nielsen Tissue-based J, Pontén map F. of and low number of connections with the remaining members of the was observed; suggesting a network (proteins with low expression) modularity effect. Connectivity analyses of cell type specific protein interaction networks suggest a dominant role for proteins with medium and high expression levels. Keywords: are composed of proteins with low, medium and high expression with low, are composed of proteins selected networks of the same size, randomly levels. to Compared cell type majority of the For specific networks have higher connectivity. specific proteins with medium and high expression networks, have significantly values, based on randomization tests. For higher degree general trend high levels expression, a medium or of the proteins with themselves,connections between significantly high number of of specific molecular interaction networks. Protein-protein interaction specific molecularinteraction networks. Protein-protein contain cell (PPI) datasets do not specific type interactions. Therefore, a cell could be present in data type of interest. only a fraction of PPI Unlike PPI datasets, protein expression datasets for different cell types are available specific expressiongenerate potential protein levels to in order for various cell protein interaction networks Specific types. networks Systems biology view cell of different types requires analysisof Ertuğrul Dalgıç Ertuğrul Dalgıç 1. Department of Medical Biology, Zonguldak Bülent Turkey Zonguldak, Ecevit University School of Medicine, CONNECTIVITY ANALYSIS OF CELL TYPE SPECIFIC TYPE CELL SPECIFIC OF ANALYSIS CONNECTIVITY NETWORKS INTERACTION PROTEIN POSTER PRESENTATIONS 226 1. HacettepeUniversity,FacultyofScience,DepartmentBiology,06800,Ankara-Turkey Yasin Kaya THE LINEAGEINFERENCES. INTERGENIC SPACER (IGS) REGIONS PREDICTING LEARNING A MACHINE APPROACH TO CLASSIFY 1 Mol. Biol.12,695–706. the ribosomalintergenic spacerofthecrucifer Sisymbriumirio.Plant from Isolation andcharacterization ofanunusual repeated sequence F.,[5] Grellet, Delcasso-Tremousaygue, D. and Delseney, M. (1989) 1949–1953. of pea.EMBOJ.of ribosomalDNAreplicons intermediates 10, [4] VanÕt Hof, J. andLamm, S.S.stranded replication (1991)Single Pleurotus cornucopiae.Theor. Appl.Genet. 88,824–830. polymorphism oftherDNAunit inthecultivatedbasidiomycete189. length and site J.Restriction re, Labaré(1994) B. and abal, , Irac [3] 195–210. of thecactophilicyeastClavisporaopuntiae.Can.J. Microbiol.46, structure, andbiogeography species (2000)RibosomalDNA, C.A. Starmer,[2] Lachance, M.A., W.T., Bowles,J.M., Phaff, H.J. and Rosa, Genome 43,597–602. spacer ofLensintergenic Medik.andotherlegumespecies. culinaris la Vega, M.(2000)Acomparativestudy of thestructurerDNA References: phylogenetic inferences. demonstrated that ofboth could be considered hypothesis testingand As a result of the statistical model of intergenic spacerregions the likelihoodoffeaturesgivenclass. given thepriorprobabilityandlearning posterior probabilityof class independent ofeachother. NBprovidedamethodofcalculating the based on theBayestheoremwithassumptionthat the featuresare Naïve Bayes(NB)algorithmis trees. given byindividual of theclasses the testcase basedonthemode trees duringthetrainingandclassifies behind theworkingofRFalgorithmisto construct manydecision explained by Random Forest (RF)and Naïve Bayes(NB).Theprinciple rates arePoisson-Dirichletdistribution suggestedthat they couldbe features.Frequencyof topdecisive list IGS distance of thedifferent model anda classification the final gives which algorithms learning used totrainmachine rates is Amount oftheIGSevolutionarydistance spp. suchasBrassicaspp. promoter region ofsomecrucifers coding DNA havebeendescribedas a motif in the conservative ribosomal DNAaswell processing, transcription terminationandof replication processes RNA of transcription, for theinitiation essential that are sequences They contain DNAsequences. observed betweennon-coding evolving motifswhichcanbe related to length polymorphisms Intergenic spacers(IGS)areshort,tandemlyrepeatedandrapidly [8] andArabidopsis [1] Fernandez, M.,Polanco, andPerez C.,Ruiz,M.L. de [9] [1,4] . . Many of the repeat motifs in the non- . Manyoftherepeatmotifsin [7] , Raphanus POSTER PRESENTATIONS 227 [email protected] gene from a higher plant. Eur. J. Biochem. 172, 767–776. J. plant. Eur. gene from a higher C. (1991) R. and Schweizer, Unfried, I., Pointner, P., [9] Gruendler, ribosomal gene spacer from 25S–18S the sequence of Nucleotide Nucleic Acids Res. 17, 6395–6396. Arabidopsis thaliana. Corresponding Author’s Address: [7] Bhatia, S., Negi, M.S. and Lakshmikumaran, M. (1996) Structural Lakshmikumaran, and Negi, M.S. S., [7] Bhatia, the rDNA intergenic analysis of Evolutionary of Brassica nigra: spacer Mol. divergence species. Brassica diploid spacers of the three of the J. Evol. 43, 460–468. Ananiev, F., Panabieres, Grellet, F., D., [8] Delcasso-Tremousaygue, transcriptional Structural and (1988) M. Delseney, and E.D. RNA nuclear ribosomal a spacer of the external characterization of [6] Barker, R.F., Harberd, N.P., Harvis, M.G. and Flavell, R.B. (1988) (1988) Flavell, R.B. and M.G. Harvis, N.P., Harberd, R.F., [6] Barker, DNA ribosomal a of region intergenic the of evolution and Structure 1–17. 201, Mol. Biol. of wheat. J. repeat unit POSTER PRESENTATIONS 228 University,06800, Ankara,Turkey. 1. GraduateSchool ofInformatics,DepartmentHealthMiddle EastTechnical Habibe CansuDemirel, WITHSTRUCTURALEVENTS INTERACTOME REWIRING BY INTEGRATING ALTERNATIVE SPLICING MODELING THETUMOR SPECIFIC NETWORK showed that patient subgroups can be obtained through clustering only foundin tumor-specificanalysis networks. Theirenrichment we comparedthe resultingnetworksetstofind theproteinsthatare networks based on the two patient-specific interactomes.Finally, Integrator to modeltwosetsof set withOmics the sameterminal isoforms. We favoring thenon-canonical expression increased used and showan one interaction the proteinsthatlostatleast included Terminal andonefilteredbasedontheexpression. “terminal set” sets called of predominant isoforms as aresult based onthelostinterfaces sample; onefiltered Then, weconstructedtwointeractomesforeach tofindpotentialinteractionlosses. known/predicted proteininterfaces multiple sourcesand aligned themissingresiduesonisoformswith Additionally,from wecompiledastructuralhumaninteractome isoforms to detect thelostregionsdifferingfromcanonicalprotein. samples. Atthesametime,wemapped the transcriptsto protein patient to find the transcripts that show increased expressionintumor between tumorandanaveragedpool of normalsamplesforeach using atranscriptomeassembler, wecalculatedthelogfoldchanges Genome Atlas (TCGA). After the detection of expressed transcripts data from theCancer tumors and112normalRNA-seq breast cancer they bringwith.Forinteraction losses thispurpose,wecollected400 and the protein isoformsbyintegrating theproteinstructures specific study,In this with tumor networks patient specific wereconstructed of manydiseasesandcancer. in progression may beeffective in this process abnormalities genes, regulate theactivityofalmostall Because alternativesplicingevents protein interactionnetworks. changes ourviewpointonclassical pathways. Thisfinding on proteininteractionnetworksandsignaling proteins hasadirecteffect in isoform the diversity Hence, interactions. of achangeintheirbindingregion,someothersmaygainnew While someproteinisoformsmay lose theirinteractionsbecause gene cancodemultipleproteinswithpotentiallydifferentstructures. of introns.Inthisway,exons, exonparts or evenwiththeinclusion one of are createdviatheexclusion and differentfinalmRNAsequences This processisregulated alternative splicing during gene expression It has beenestimated that in 90%ofallgeneshumanundergo which increasesthediversityof the proteome is alternativesplicing. of the most important occursat mechanisms transcriptional level are crucialfordevelopingpersonalizedtherapeuticstrategies.One formation and elucidating signaling pathways by using thesedata those triggertumor in proteinexpression changes levels, expression or decreasinggene Analysis ofgeneticvariations,increasing NurcanTunçbağ POSTER PRESENTATIONS 229 Nurcan Tunçbağ Middle East Alternative Splicing; Protein Interaction Network; Network Interaction Network; Protein Alternative Splicing; Keywords: Rewiring Corresponding Author’s Address: University Institute B-204 Informatics Cankaya/Ankara Technical [email protected] spliced isoforms and their lost interactions. We observed that for all for observed that interactions. We their lost spliced isoforms and in cancer to 16 pathways genes. sets include up samples, the terminal networks to reveal we also compare the resulting pathway, Moreover, and protein patterns that can protein-protein interaction cluster the their similarities.analysis will tumors according to The results of our of tumor mechanisms contribute to the elucidation and will help for developing therapeutic strategies. target selection and interactions for all samples. There were 53 unique lost protein – protein lost unique 53 samples. There were all for interactions the At interactions. DNA lost that protein 43 and interactions drug the analysis same time, resulted losing interactions of the proteins in interesting examples including interaction losses cancer between their targets. drugs and In our ongoing analysis, cancer we focus on breast with pathway the are altered given how these pathways in cancer and pathways based on enriched on enriched based sets gene sets related gene telomer and were the result of the as a distinctive most addition, In such clusters. among lost protein-protein we found 12125 of interactions, examination POSTER PRESENTATIONS 230 1. YildizTechnical University,DepartmentofBioengineering,Istanbul,Turkey Derya Alpaydın1,RavzaÖztürk1, FOR RELIABLE NETWORK ANALYSIS CONSTRUCTING ACCURATE COMPOUND NETWORKS Corresponding Author’s Address: Corresponding Author’s physa.2016.02.049. Applications. 2016;452:209-219. its https://doi.org/10.1016/j. and StatisticalMechanics of nodesincomplexnetworks.PhysicaA: [2] LiuJ, XiongQ,ShiW, Wang ShiX, Evaluatingtheimportance K. bioinformatics/btn307 Bioinformatics, 2008; 24(15), 1733-1734. https://doi.org/10.1093/ ChemmineR: acompoundminingframeworkforR. References: Keywords: identify criticalcompoundsinhumanmetabolicnetwork. L-value After constructingmoreaccuratemetabolicnetwork,weapplied package ChemmineR most commonsubstructurefunctionsavailable in and flexible Inourproject,weappliedtanimotosimilarity unrelated molecules. In order to increase accuracywe removed the connections between between currencymetabolitesandactualreactants. network. Thisapproach introduces inaccuracyduetoconnections connect allpossiblereactantpairsofareactiontoconstructmetabolite standard graph theory Availablemetabolicnetworks measurements. identify interactionsbetweennodesinthereactionsby the helpof over humandiseasestudies.Various methodshavebeenproposed to crucial and applied in recentyearswith the help of holistic approaches as anetworkis of metabolicsystem Analysis human metabolicsystem. of many diseases and treatments, it is veryimportant to understand of anyorganism.Tofor thesurvival essential mechanism enlighten are which highly complexprocesses metabolism includes Cellular [2] , a recently developed nodeimportancemeasure,to , arecently MetabolicNetwork; Network Analysis;Tanimoto Similarity [1]CaoY, JiangT,L, Cheng A, Charisi T. Girke [1] Alper Yılmaz1 to construct accurate compoundnetworks. [email protected] POSTER PRESENTATIONS 231 2 Hilal Kazan , 1 [1] Bashashati A, Haffari G, Ding J, Ha G, Lui K,Lui Rosner J Ha G, [1] Bashashati A,Ding J, Haffari G, Driver Gene Prioritization, Bipartite Graph, Betweenness Bipartite Graph, Betweenness Driver Gene Prioritization, 1698 [2] Song J, Peng W, Wang F. A random walk-based method to A random walk-based F. Wang W, Peng [2] Song J, 1698 identify driver genes by integrating the subcellular localization and Bioinformatics, 2019, BMC graph. bipartite frequency into variation Dressler L.,Bortolomeazzi M., Nulsen J., D., [3] Repana 20:238 and Ciccarelli T., A., Palmieri A., Yakovleva K., S. Tourna Venkata comprehensive a catalogue Cancer Genes(NCG): of Network F.D.The of known and candidate cancer genes from cancer sequencing a network diffusion step improve the identification of driver genes on driver genes of network diffusion step improve the identification a breast cancer data. Keywords: Network Diffusion Centrality, References: on somatic driver mutations of impact uncovering the DriverNet: al. et Genome Biol, 2012,13(12):R124 transcriptional networks in cancer. defined using NCG [3] and CancerMine [4] databases. Additionally, Additionally, CancerMine [4] databases. [3] and defined using NCG in the set of the overlap between the GO terms enriched we show that the GO terms enriched in referencepredicted driver genes and XXX gene sets defined using NCG or CancerMine is higher compared same confirm the observation when we to the other methods. We utilizing enrichment. Show that In conclusion, we use KEGG pathway applying and with genomic data theoretical measures together graph calculated from paired patient-specific normal-tumor networks that normal-tumor networks patient-specific paired calculated from data. are perturbed based on gene expression somatic mutation and graph the bipartite on diffusion step network a Second, we apply to approach our applied We graph. results in an edge weighted that as it contains the largest number of normal breast cancer data TCGA known drivers recovers larger number of a XXX samples. / tumor compared to DriverNet and Subdyquency when known drivers are side contains patient-specific “outlier”side patient-specific contains genes are defined as the that functions. Similar the existing approach set of genes with altered to driver gene is determined by of a potential DriverNet [1], the impact differs it regulates. Our approach its effect on the “outlier ” genes that 2] in two ways. First, from DriverNet other related methods [e.g., and differentialon simply based outlier gene sets is not definition of our gene expression on betweenness but centrality measurements Recent cancer genomic molecular studies have generated detailed key remaining problem A cancer patients. large number of a on data propose in cancer genomics of driver is the identification genes. We (gene integrates genomic approach that data a computational expression, with protein-protein interaction somatic mutation data) graph is constructed where As a first step, a bipartite network data. genes mutated one side contains the the other in the cohort and , Aissa Houdjedj 2 Cesim Erten Antalya Bilim Graduate Program, and Computer Engineering 1. Electrical Antalya, University, Turkey Turkey Engineering, Antalya Bilim University, Antalya, 2. Department of Computer AN INTEGRATIVE NETWORK-BASED APPROACH FOR FOR APPROACH NETWORK-BASED INTEGRATIVE AN CANCER BREAST FOR GENES DRIVER PRIORITIZING POSTER PRESENTATIONS 232 [email protected] Address: Corresponding Author’s tumor suppressorsincancer. NatMethods,2019,16:505–507 resourcefordrivers,oncogenesand CancerMine: aliterature-mined 018-1612-0 [4] Lever J, Zhao EY, Grewal J, Jones MR, Jones SJM. Biol, 2019, 20:1 https://doi.org/10.1186/s13059-Genome screens. [email protected] POSTER PRESENTATIONS 233 , 1,2 , Muhammed Kasım Diril , Muhammed 1,2 , Tülay Karakulak 1,2 binding affinities (log(IC50)) the best (Pearson’s R2=0.76) (Figure (Figure R2=0.76) binding affinities (log(IC50)) the best (Pearson’s set, none of forms were included in the data 1). When the mutant the webservers produce a meaningful correlation. In this case, could could relate the calculated affinitiesthe best performer KDEEP to are currently We R2 of 0.32. experimental Ki values with a Pearson’s analyzing the results to understand if any particular characteristics of are responsible for this sharp or ligand protein type, the mutation, As of today, there is no tool specialized functional impact to predict the As of today, of kinase there are web-based mutations. Though, approaches Within this estimate protein-ligand binding affinities. poised to affinity used protein:ligand context, we chose the most commonly predictors (HADDOCK2.2[1](refinement interface), PRODIGY-LIG[2], our affinities of estimate the binding to KDEEP[4]) DSXonline[3], and complexes.wild type and mutant When run only on the wild type andexperimentalcomputed correlated complexes, PRODIGY-LIG functioning in cell cycle, cell growth, DNA damage, metabolism, and damage, DNA functioning in cell cycle, cell growth, to these kinases Ligands bound are mostly transcriptional regulation. compounds (46% are halogenated). nitrogen-rich poly-heterocyclic with the structures, we have also collected experimentally Together or Ki) from Kd, (either IC50, determined kinase:ligand binding data PDBbind. predicting the impact of kinase mutations on kinase:ligand binding on kinase mutations of predicting the impact affinities. kinase structures type wild 12 of set collected a we end, that To These numbers Bank. Data Protein from states mutant 49 their and states of the represent the cases type and mutant wild both in which Our benchmark is the same ligand. to interest are bound kinase of made of cytosolic and membrane-associated protein kinases, mainly important kinase motifs can switch the kinase conformation to kinase motifs can switch important has been many years, it For state. resistant drug constitutively active or of great interest to assess structural/kinetic impact of protein kinase compile accurate binding affinity Thus, it is a requisite to mutations. kinase of estimate the functional impact prediction workflows to mutations in silico. Expanding on this, we compiled the first high- resolution kinase:ligand to assess benchmark the field’s capability in Protein kinases are integral players of cellular metabolism. The players of kinases are integral Protein essentiality of proper kinase functioning is challenged by mutations occurring in kinases, which in often result severe diseases, like cancer. catalytically of in the vicinity occurring within or mutations Point 1,2 2. Izmir Biomedicine and Genome Center 2. Izmir Biomedicine and INTERACTIONS INTERACTIONS Mehmet Ergüven Ezgi Karaca and Genome Institute 1. Izmir International Biomedicine ASSESSMENT OF WEBSERVERS IN THE PREDICTION PREDICTION IN THE WEBSERVERS OF ASSESSMENT KINASE:LIGAND ON IMPACT MUTATIONS’ POINT OF POSTER PRESENTATIONS 234

Dr. Ezgi Karaca address author’s Corresponding Neural Journal Networks. of Chemical Information and Modeling [ Information and Modeling [ smallligand complexes:the PRODIGY Vangone[2] A, Schraarschmidt J,Koukos P, Geng C, Trellet Xue ME, LC, BonvinAM Feb 22;428(4):720 AMJJ. The Web HADDOCK2.2 User Server: [1] References: Keywords mutations kinase designing in values. experimen a on webservers these of capacity predictive the probe protein type, or ligand are responsible for this sharp drop in the prediction accuracy. After this, we plan to 0.32. We are currently analyzing the results to understand if any particul best performerKDEEP could relate the calculated affinities to experimental K were included in the data set, none of (log(IC affinities binding experimental mutant complexes.When run only onthe wild type complexes,PRODIGY PRODIGY chose the most commonly used protein:ligand affinity predictors (HADDOCK2.2[1](refinement interface), web are there IC (either h we structures, the with Together Ligands bound to these kinases are mostly nitrogen kinases,mainly functioning in cell cycle, cell growth, DNA damage, metabolism,and transcriptional regulation. interest of both which in cases the represent numbers These Bank. Data Protein affinities. binding kinase:ligand on mutations kinase of impact we compiled the first high affinity prediction workflowsestimate to the assess structural/kinetic impact of protein kinase mutations.Thus, it is requisitea to compile accurate binding gr of been has it years, For many state. resistant drug or active to constitutively conformation Point mutations occurring within or in the vicinity of catalytically important kinase motifs can switch the kinase functioning is challenged by mutations occurring in kinases, which o 4 3 ] Jiménez J, ŠkaličM, Martínez Neudert] G, KlebeA DSX: G. knowledge karaca-lab/ ibg.edu.tr, https://www.ibg.edu.tr/research-programs/groups/ Address: Author’s Corresponding G.29309725. Fabritiis De and Modeling2018 Feb 26;58(2):287-296. PubMed PMID: G, Martínez-Rosell Information NeuralNetworks.JournalofChemical 3D-Convolutional M, Škalič KDEEP: Protein-Ligand AbsoluteBindingAffinityPrediction via J, Jiménez [4] PMID: 21863864. Information andModeling2011 Oct24;51(10):2731-45. PubMed for theassessmentofprotein-ligandcomplexes.JournalChemical [3] NeudertG, KlebeG.function scoring DSX: Aknowledge-based 2019 May1;35(9):1585-1587.PubMedPMID:31051038 the PRODIGY-LIGligand complexes: small webserver. Bioinformatics LC, BonvinAMJJ. predictionofbindingaffinityinprotein- Large-scale [2] Vangone Schraarschmidt J, A, Koukos P, Geng C,Trellet Xue ME, 26410586. of MolecularBiology2016Feb 22;428(4):720-725.PubMedPMID: Friendly IntegrativeModelingofBiomolecularComplexes.Journal De VriesSJ, BonvinAMJJ. TheHADDOCK2.2Web Server:User- MelquiondASJ, KaracaSchmitz C,Kastritis E, PL, Van DijkM, References: wild typecomplexes(bottom-right). PRODIGY-LIGsticks (top-right). yieldedthehighestcorrelationon substrate specificity, commonlymutatedresiduesareindicatedas Figure 1. Benchmarking Keywords: their experimentalsetups. that can aidindesigningkinasemutations during experimentalists Our ultimate aim isto present an optimal affinity predictionworkflow of the mutant cases normalizedaccordingto their wildtype values. determined bindingaffinities made ofpredictedandexperimentally on aderivedbenchmarkset, predictive capacityofthesewebservers the predictionaccuracy.drop in Afterthis,weplantoprobethe Van Zundert GCP, Rodrigues JPGLM, Trellet M, Schmitz C, Kastritis PL, Karaca E, Melquiond Karaca E, PL, Schmitz Kastritis M, C, Trellet Rodrigues JPGLM, GCP, Van Zundert prediction of point mutations point of prediction Mehmet Ergüven

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POSTER PRESENTATIONS 235

[4] , 1 and CancerMine [3] , Aissa Houdjedj 2 , the impact of a potential driver gene [1] , Cesim Erten 1 [1] Bashashati A, Haffari G, Ding J, Ha G, Lui K, Lui Rosner J G, Ha [1] Bashashati A, Ding J, Haffari G, Driver Gene Prioritization, Bipartite Graph, Betweenness Bipartite Graph, Betweenness Driver Gene Prioritization, et al. DriverNet: uncovering the impact of somatic driver mutations on somatic driver mutations of impact uncovering the DriverNet: al. et Genome Biol, 2012,13(12):R124 transcriptional networks in cancer. 1698 method to identify A random walk-based F. Wang W, Peng [2] Song J, variation the subcellular localization and integrating driver genes by frequency into bipartite graph. BMC Bioinformatics, 2019, 20:238 K., S. Dressler L., Bortolomeazzi M., Venkata Nulsen J., [3] Repana D., In conclusion, we Show that utilizing graph theoretical measures together with genomic a network diffusion and applying data step on breast cancer data. improve the identification of driver genes Keywords: Network Diffusion Centrality, References: number of known drivers compared to DriverNet and Subdyquency Subdyquency DriverNet and number of known drivers compared to when known drivers using NCG are defined the overlap between the GO we show that Additionally, databases. the GO terms driver genes and predicted terms enriched in the set of enriched in reference gene sets defined using NCG or CancerMine is confirm We the other methods. to higher for BetweenNet compared enrichment. pathway use KEGG when we observation same the based on differential gene expression on betweenness but centrality normal-tumor measurements from paired patient-specific calculated somatic gene expression and on based are perturbed networks that step on the Second, we apply a network diffusion mutation data. applied We graph. weighted edge in an results that graph bipartite as it contains the largest breast cancer data to TCGA our approach recovers samples. BetweenNet number of normal / tumor a larger protein interaction network data. As a first step, a bipartite graph bipartite a first step, a As data. protein interaction network genes in the one side contains the mutated is constructed where “outlier” side contains patient-specific cohort and the other genes that functions. Similar are defined as the set of genes with altered the to existing DriverNet approach “outlier”the its effect on is determined by Our regulates. it genes that 2] approach differs from DriverNet and other related methods [e.g., our definition of outlier gene sets is not simply in two ways. First, Recent cancer genomic molecular studies have generated detailed A key remaining large number of cancer patients. on a data genes. genomics is the identification of driver problem in cancer integrates a computational approach that propose BetweenNet, We genomic data (gene expression, data) with protein- somatic mutation 2 Ahmed Amine Taleb Bahmed Taleb Ahmed Amine Hilal Kazan Antalya Bilim Graduate Program, and Computer Engineering 1. Electrical Antalya, University, Turkey Turkey Engineering, Antalya Bilim University, Antalya, 2. Department of Computer AN INTEGRATIVE NETWORK-BASED APPROACH FOR FOR APPROACH NETWORK-BASED INTEGRATIVE AN CANCER BREAST FOR GENES DRIVER PRIORITIZING POSTER PRESENTATIONS 236 [email protected] Address: Corresponding Author’s suppressors incancer. NatMethods,2019,16:505–507 and tumor oncogenes for drivers, resource a literature-mined [4] Lever J, Zhao EY, GrewalJ, Jones MR,SJM.CancerMine: Biol, 2019,20:1https://doi.org/10.1186/s13059-018-1612-0 Genome screens. sequencing from cancer genes candidate cancer catalogue (NCG):a of of comprehensive Cancer Genes known and Tourna Yakovleva A., Palmieri A., T., and F.D.TheCiccarelli Network [email protected] POSTER PRESENTATIONS 237 . [1] . A pathogen-host metabolic metabolic pathogen-host A . [2] , which included 2545 reactions controlled whichincluded 2545 , [3]

was obtained from the public transcriptome was obtained . Dual RNA-seq data of S. enterica infection of RNA-seqDual . S. of data [5] [4]

[1] Su LH, Chiu CH. Salmonella: clinical importance Chiu CH. [1] Su LH, Infection, Genome Scale Metabolic Network; Pathogen- , Tunahan ÇAKIR , Tunahan GM, Dorhoi A, Schoolnik GK, et al. Comprehensive insights into Keywords: Host Metabolic Network; Dual RNA-Seq Data References: May- 2007 J. Med Gung nomenclature. Chang evolution of and Jun;30(3):210-9. [2] Rienksma RA, Suarez-Diez M, Mollenkopf H-J, Dolganov contraintbased modeling approach shows promising results contraintbased modeling approach shows in terms of understanding interactions between a pathogen and its host from metabolic perspective, a list of affected metabolic pathways providing in both organisms. TUBITAK by grant a through financially supported was This study Code: 316S005). (Project and its host. We used the genome-scale metabolic network of S. and its host. We Enterica from literature a recent reconstruction of human genes. As host GMN, by 1271 reactions metabolism, called iHsa, was used, which covered 8336 genes 5627 and human cell lines Gene Expression database, Integrative analysis Omnibus. by a is a convenient way to analyze the effect of infection on the pathogen analyze the effect of infection on the pathogen convenient to is a way Dual RNA sequencing (dual RNA-seq) and host metabolisms together. enables study gene expression It profiles. rather recent method to is a simultaneous measurement of the global transcriptome of both host infection during pathogen, and model can be turned into a condition-specific model by mapping dual RNA-seqleading to elimination data, of inactive reactions in organisms between a pathogen both and identification of interactions Genome scale metabolic network (GMN) models are widely used to models are widely used network (GMN) Genome scale metabolic of cells. understand metabolism models have shown utility in GMN for systemicintegrating omic data analysis of metabolism. Pathogen- host metabolic modeling is a multi-cellular interaction modelling organisms pathogen and host approach, where GMNs of both are integrated in the simulations of of metabolic phenotypes. Integration dual RNA-seq model data with a pathogen-host metabolic network Salmonella enterica is an important pathogen for both humans humans both for pathogen important is an Salmonella enterica is organism, there an as studied thoroughly animals. Being and It has been linked to this pathogen. a lot more to discover about fever and with Typhoid of infectious diseasesvariety along a nontyphoidsalmonellosis, which public health issues cause determine metabolic characteristicsis aimed to of it this study, In based computational methods. by using constraint the pathogen WITH DUAL RNA SEQ DATA RNA DUAL WITH Kadir KOCABAŞ Gebze, Kocaeli of Bioengineering, University, Department 1. Gebze Technical INTEGRATIVE ANALYSIS OF PATHOGEN HOST HOST PATHOGEN OF ANALYSIS INTEGRATIVE SALMONELLA ENTERICA OF NETWORK METABOLIC POSTER PRESENTATIONS 238 Tunahan Çakır, [email protected] Address: Corresponding Author’s Nature 2016Jan28;529(7587):496-501. host-pathogenin noncoding RNAfunctions seq unveils interactions. [5] WestermannAJ, FörstnerKU, AmmanF, BarquistLetal.DualRNA- Commun. 2017Feb 8;8:14250. and biomarkerpredictions.Nat for comparativetoxicogenomics Wallqvist Papin ratandhumanmetabolicnetworks Reconciled A, JA. EM, RawlsKD,[4] Blais DoughertyBV, Ye LiZI,Kolling GL, P, Jan 18;5:8. Typhimuriumhuman pathogenSalmonella LT2. BMCSystBiol.2011 andmathematicalmodel ofthe effort towardsaknowledge-base PalssonIM, ZenglerK, Thijs BO, AdkinsJN, BumannD. Acommunity SI, SigurbjörnsdóttirS, Shin J, Steinmann JL, SudarsanS, SwainstonN, Reed RaghunathanA, E, Özdemir MoML, SC, LiaoYC,K, Marchal Charusanti P, DeKeersmaecker CA, ChenFC,FlemingRM,Hsiung DR, SteebB,I, Hyduke [3] Thiele Fankam G, BazzaniS, DK, Allen enriched dualRNAsequencing. BMCGenomics.2015;16(1):34. transcriptional adaptation of intracellularmycobacteriaby microbe- POSTER PRESENTATIONS 239 Mugla Sitki 2 CGA (The Cancer Genome Atlas) is a vast and comprehensiveGenome Atlas) is a vast and CGA (The Cancer survival of two clusters STK11, probability of patients. Although RB1, WNT3A and VEGFD are known lung cancer related EGFR, KRAS, ANGPT1, PDE3B, PTGER4, ARHGEF6, GNG7, genes; and GPC3, complement C2, CD244, CD74, IL11RA, LEPR, KLRD1, COL4A3, LPL,recently identified lung are SCN4B NR3C2, KLF2, PLA2G3, cancer related genes, there is no evidence of direct interaction genes and COL6A5, ROBO2, CACNA1D RAPGEF3, between PRKCE, VEGFD, GPC3, GNG7, ARHGEF6, PTGER4, PDE3B, ANGPT1, PDE3B, PTGER4, ARHGEF6, GNG7, GPC3, VEGFD, complement CD74, IL11RA,C2, CD244, KLRD1, COL4A3, LEPR, LPL, in active subnetworks, have the SCN4B) NR3C2, KLF2, PLA2G3, these DEGs of heatmap patients. The the survival of on impact most were when the patients and pattern dichotomous showed apparent clustered into two by hierarchical clustering and survival analysis was showed a significant performed, survival plot divergence between Cox Proportional Hazard Regression Analysis order to analysis in Cox Proportional cancer on overall patients survival. effect of them all together Lastly, and alterations impactful detected on based clustered are patients clusters. survival analysis is performed for these patient Kaplan-Meier from dataset adenocarcinoma (LUAD) lung used we this study, In determined that the significantly genes mutated We database. TCGA DEGs (WNT3A,25 KRAS), with expression of and EGFR (RB1, STK11, pipeline is started with downloading of TCGA datasets of cancer pipeline is of TCGA started with downloading Then, significant R package. Simple of interest by TCGAbiolinks R package, SomInaClust by are detected Nucleotide Variations validated by COSMIC mutations. Differentially Expressed Genes (DEGs) are determined by Limma/EdgeR R packages and then active R package. DEsubs by subnetworks of these DEGs are determined (CNVs) are determined by Gaia R package. Copy Number Variations Then all significant multi-variate are integrated through alterations be performed and in accordance with this purpose, we have built in accordance with this purpose, we have be performed and meta-analysis of the mutations an integrated carry out pipeline to a including single-nucleotide (SNVs), variations number the copy variations (CNVs); gene expression detected by RNAseq; and clinical from The Cancer Genome Atlas of cancer patients downloaded data and reported their influence on overall survival through Cox (TCGA) Hazards Model. The workflow of R based designed Proportional T including molecular consisting dataset database genomics, of more of clinical epigenomics and transcriptomics, proteomics, data cases identify 33 tumor types [1]. In order to across than 11,000 molecular nature of cancers, molecular integrated analysis must , Tuğba Süzek , Tuğba 1 Talip Zengin Talip 1. of Bioinformatics, Department of Natural Graduate School Applied Sciences, Mugla and Sitki [email protected] Turkey, Kotekli Mugla Kocman University, 2. of Bioinformatics, Department of Natural and Applied Sciences, Graduate School [email protected] Mugla Turkey, Kocman University, Kotekli META-ANALYSIS PIPELINE FOR GENOMICS AND AND GENOMICS FOR PIPELINE META-ANALYSIS DATA TCGA IN VARIATIONS TRANSCRIPTOMICS POSTER PRESENTATIONS 240 U C P K a S D A T a O C c m G s S o b D t s H a L C T [ c R d d p F s ( M h 1 C h u i n l a c o Kocman University, Kotekli MuglaTurkey, [email protected] Mugla Sitki Graduate School ofNaturalandAppliedSciences, Address: Author’s Corresponding 1120. Atlas Pan-Cancer analysisproject.Nat. Genet. 2013; 45; 1113– I., Shmulevich, Sander,K., C., and Stuart, J.M. The CancerGenome J.N., Mills,G.B.,E.A., Collisson, Shaw, Ozenberger, K.R., Ellrott, B.A., References: Figure 1. Clustering; SurvivalAnalysis Keywords: signature forclinicalpredictionLUAD. lung cancer, yet. These 29 genes are strong candidates as molecular f e e e a a o T h P C i e O g a t n o e R z P E a e e i ] n o r g r a n t t t n e N m s d y e n 2 p e n K z L, f i r t o e e i v 1 C G s l H L4 c w u p r s v - a e e w s i D C n a c r y a c r i D u s V i a a e C I i r s 1 e e - 3 r n r f e A m v r e e r b e s t s G s n e o t b t a r a i P e r d e e 1 e r t e , t s a i A r t a u s i c d e c 2 a f n r m n s s p s 3 o i i e s n d EF ) o L p , s p n l G a o d r n n t 1 n d C 9 i ; c 3 a ( G a 3 n f R a g c w t c n A a o e e y r M e g T . E s o i e The flowchartofthepipeline l i y , N r l s e g m r e e g i n TCGA; LUAD; SNV; CNV; Active Subnetwork; Cox; DEA; f n y m t e n : d 6 , t ta u r T f e t n h 2 G , l f t s u [1] Cancer Genome Atlas Research Network,, Weinstein,Atlas Research Genome [1]Cancer p r o r L e s e t n m o t s G , y m d s r T e a d e , t : e w e G h K p i n m a t f n i G e a o b F d EP t e P g r c d h i s r e 7 C o e a m c e e e n e r n e m y e 3 g B o a R t n TG o e e n C c , c k f n R e s a G l c f o , e t g u i n a o r l t . 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fo p i c a e t r x s b p l l e , s w G c n C o n r l k p c l c i a e r a e a p 7 p z n k d y e n u k ; C e s c c @ @ e l a l l r a p r ( A a i A l l 4 t a r s i N t l b i r s C c e a t s 2 s n t i S i i 2 n G n p r f r e t o , h e , e a , n s l r C n m m d o a e c d u N , s l a d i i r u o n I d d l i a u d e p i c c b u e d r e r r r d i l l L J c o N C s n u u f , i V c m y y t e a s a e S r e R . a l i r S l o a n t w 1 t . . M S c a t h l e D t v r l A s e l s c E e e t e c s w e a a e e p i t t 1 i h o o i e c R o r i i d o E d d g ) n p i e i r s t 2 . 1 b g d v . e s a R e i e o t , n t A f e i a r u u r d e a G x e 4 i D a a r o n t a n s k n w p d n i a l t d 2 i A t T e . f . t n o p s . n 4 o l m c e a g e t t i c t s h a o , e s 5 t e g a e r r f c e h r e f c g g , a n , , ; e o c e s t r o e . o o s e n e ti d D M s l d e V h e e K D s t C k i S h p u s f n r u , s s t o t n , n i W r p c a e c E s s m h a u a a , d D n LR EG e i s M o n m L C e M e o e a e g l v d r t e r i G s g a u l s s e i o 7 r M d i p r v U n e o p a s e o ; s o u e a r , u r n f g A l 4 s y . e i g o , D n a a e s w y o r g n t e A d g v t h u a t , i e G n T f t c n h r t l c h ( G o e a 1 l p a t n c g n n a s n m D e d I m W a a e e T u h o e t t r e l C n l , a d . o l L l u r . h l e r r e u e u l a o n B . S C t e C s l A y o S e n a o m r 1 a N m i A u n s t a e i d m s G e G l i r . a G d x n O 1 t t n s n S i d e s t , i b , T3 m n a e l k o r a s k a e a s R a P t e c g i e A i L4 e n 1 i r e t s l s S h n P i n t n t l n u ( n e d l C e A u r k d t 1 y K i c o A e D d h c K a r o l d o n r s d r i , o s 3 a A a i d u i l i l e , a o v m 0 f m i v o EG w n , u m a i y n p r , g r t b w e g s c a K 0 K i 3 o c n t i V e t s o s c t i o v v e n a h h a r m e p 0 G h n t m , r i c , d m e L o i s r i e s EG a i s e g c d a s t a a e L d f t a a r e e c N r l a K c A n e R R ) r c i e i t , t d m t e s d n EP t o m a a c d i n t T r a p s G B D . y . t o n s t a F t n s t a R u l s l b o e h a h h o b e n c e a I o n o t 1 r e 7 a 1 D s R d y n o n n . t e e e n e y e s s s f t t l t , , t , , . , , POSTER PRESENTATIONS 241 . Using [4] [3], consisting

. To manage this ever-increasing for threat manage this ever-increasing . To [1] . [2] , Müberra Fatma Cesur, Saliha Durmuş, Tunahan Çakır Durmuş, Tunahan Fatma Cesur, Saliha , Müberra commonly used metrics identify the key nodes in a network, which to targets to inactivate networks to be drug have high potential the these metrics, essential nodes (enzymes) were identified through were comparatively They graphs. metabolite and gene, reaction, analysis enabled The comparative literature. the on based analyzed identification of the most suitable graph structure to identify efficient targets Comparison of the results with the candidate drug targets. drug metabolites involved in many reactions were ignored in constructing metabolites involved in many reactions representing the same genome-scaleEach graph the graphs. metabolic network of the K.was separately pneumoniae MGH 78578 betweenness and degree distribution in terms of topologically analyzed number the The degree distribution gives about information centrality. of connections for each node while the betweenness centrality refers to the number of short paths passing over a node. They are of amino acids, fatty acids, and nucleic acids. of amino acids, fatty acids, and nucleic graphs for was converted into three different metabolic iYL1228 graphs). analysis (gene, metabolite, and reaction topological construct were connected to Metabolites involved in the same reaction genes controlling the the gene graph, For the metabolite graph. same or succeeding were connected. The reaction graph reactions Currency by linking reactions with shared metabolites. was obtained platforms provide a significant reduction in the solution space of the targets via in silicoputative drug analyses. Thus, they are especially expensive, time-consuming,need for reduce the promising to and a GMN model In this study, approaches. laboratory labor-intensive of K.called iYL1228 pneumoniae strain MGH 78578, reactions involved in 2,262 metabolites 1,658 genes and 1,229 of metabolism the to dedicated reactions set of includes a It used. was introduced. Therefore, the aim of this study is to determine candidate determine is to this study aim of Therefore, the introduced. the considering approach, topology-based targets using a drug in conservation topology and essentiality role of the network of the genes Genome-scale metabolic network (GMN) models provide a valuable for a systems-basedway understanding of bacterial metabolism, of metabolic networks. These through the mathematical representation Klebsiella pneumoniae is an opportunistic bacterial pathogen, bacterial pathogen, is an opportunistic Klebsiella pneumoniae life-threateningassociated with various nosocomial infections (e.g., liver pyogenic cellulitis, and endophthalmitis, meningitis, pneumonia, counteract the current strains of the pathogen abscess). Hypervirulent dissemination development and the strategies through therapeutic of antibiotic resistance must be economic cost, new treatment efforts public health and global FOR DRUG TARGET DISCOVERY TARGET DRUG FOR Semiz Merve Yaşar Kocaeli, Turkey University, Gebze Technical of Bioengineering, 1. Department TOPOLOGICAL ANALYSIS OF GENOME-SCALE OF ANALYSIS TOPOLOGICAL KLEBSIELLA OF PNEUMONIAE NETWORK METABOLIC POSTER PRESENTATIONS 242 Corresponding Author’s Address: Corresponding Author’s pneumoniae atgenomescale.Front CellInfectMicrobiol(InRev.) of potential screening drug targets in Klebsiella metabolism-centered T.Network-based Çakır S, Durmuş R, Uddin B, MF,Siraj Cesur [5] BMC SystBiol.2010;4. HIV-1into asystemhijack. subtle insights hostinteractionsreveals Pinney JW, JE, [4] Dickerson The biological context of Robertson DL. iYL1228. JBacteriol.2011;193(7):1710–7. pneumoniae MGH78578, of Klebsiella metabolic reconstruction Palsson BO,validated genome-scale Anexperimentally HsiungCA. [3] Liao Y, Huang T, Chen F, Charusanti P, Hong JSJ, Chang H, Tsai S, Mol Biosyst.2009;5(12):1672–8. Predictinganalysis. genesbasedonnetworkandsequence essential [2] Hwang YC, LinCC,ChangJY, MoriH, JuanHF, Huang HC. 2016;80(3):629–61. Going on the Offensewitha Strong Defense. MicrobiolMol Biol Rev. References: Metabolic Networks;NetworkTopology; DrugTarget Keywords: of metabolicmechanisms. the samemicroorganism from ourrecentworkonconstraint-based computationalfor analysis Klebsiella pneumoniae; Infection; Genome-scale Klebsiellapneumoniae;Infection;Genome-scale [1]Paczosa MecsasJ. MK, Klebsiellapneumoniae: [5] provided a more comprehensive picture provided a more comprehensive [email protected] Gold Sponsors

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