Towards a Genome-Scale Coevolutionary Analysis Giancarlo Croce
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Semantic Web
SEMANTIC WEB: REVOLUTIONIZING KNOWLEDGE DISCOVERY IN THE LIFE SCIENCES SEMANTIC WEB: REVOLUTIONIZING KNOWLEDGE DISCOVERY IN THE LIFE SCIENCES Edited by Christopher J. O. Baker1 and Kei-Hoi Cheung2 1Knowledge Discovery Department, Institute for Infocomm Research, Singapore, Singapore; 2Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA Kluwer Academic Publishers Boston/Dordrecht/London Contents PART I: Database and Literature Integration Semantic web approach to database integration in the life sciences KEI-HOI CHEUNG, ANDREW K. SMITH, KEVIN Y. L. YIP, CHRISTOPHER J. O. BAKER AND MARK B. GERSTEIN Querying Semantic Web Contents: A case study LOIC ROYER, BENEDIKT LINSE, THOMAS WÄCHTER, TIM FURCH, FRANCOIS BRY, AND MICHAEL SCHROEDER Knowledge Acquisition from the Biomedical Literature LYNETTE HIRSCHMAN, WILLIAM HAYES AND ALFONSO VALENCIA PART II: Ontologies in the Life Sciences Biological Ontologies PATRICK LAMBRIX, HE TAN, VAIDA JAKONIENE, AND LENA STRÖMBÄCK Clinical Ontologies YVES LUSSIER AND OLIVIER BODENREIDER Ontology Engineering For Biological Applications vi Revolutionizing knowledge discovery in the life sciences LARISA N. SOLDATOVA AND ROSS D. KING The Evaluation of Ontologies: Toward Improved Semantic Interoperability LEO OBRST, WERNER CEUSTERS, INDERJEET MANI, STEVE RAY AND BARRY SMITH OWL for the Novice JEFF PAN PART III: Ontology Visualization Techniques for Ontology Visualization XIAOSHU WANG AND JONAS ALMEIDA On Vizualization of OWL Ontologies SERGUEI KRIVOV, FERDINANDO VILLA, RICHARD WILLIAMS, AND XINDONG WU PART IV: Ontologies in Action Applying OWL Reasoning to Genomics: A Case Study KATY WOLSTENCROFT, ROBERT STEVENS AND VOLKER HAARSLEV Can Semantic Web Technologies enable Translational Medicine? VIPUL KASHYAP, TONYA HONGSERMEIER AND SAMUEL J. ARONSON Ontology Design for Biomedical Text Mining RENÉ WITTE, THOMAS KAPPLER, AND CHRISTOPHER J. -
Simultaneous Identification of Specifically Interacting Paralogs and Interprotein Contacts by Direct Coupling Analysis
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis Thomas Gueudréa,1, Carlo Baldassia,b,1, Marco Zamparoa, Martin Weigtc,2, and Andrea Pagnania,b,2 aDepartment of Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy; bHuman Genetics Foundation, Molecular Biotechnology Center, 10126 Torino, Italy; and cSorbonne Universités, UPMC Université Paris 06, CNRS, Biologie Computationnelle et Quantitative, Institut de Biologie Paris Seine, 75005 Paris, France Edited by Barry Honig, Howard Hughes Medical Institute, Columbia University, New York, NY, and approved September 16, 2016 (received for review May 12, 2016) Understanding protein−protein interactions is central to our under- unphysiological treatment needed for protein purification, enrich- standing of almost all complex biological processes. Computational ment, and crystallization. It is therefore tempting to use the ex- tools exploiting rapidly growing genomic databases to characterize ponentially increasing genomic databases to design in silico protein−protein interactions are urgently needed. Such methods techniques for identifying protein−protein interactions (cf. refs. 3 should connect multiple scales from evolutionary conserved interac- and 4). Prominent techniques, to date, include the search for tions between families of homologous proteins, over the identifica- colocalization of genes on the genome (e.g., operons in bacteria) tion of specifically interacting proteins in the case of multiple -
Inter-Residue, Inter-Protein and Inter-Family Coevolution: Bridging the Scales
Inter-residue, inter-protein and inter-family coevolution: bridging the scales Hendrik Szurmant1+, Martin Weigt2+ Affiliations: 1. Department of Basic Medical Sciences, College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, Pomona CA 91766, USA 2. Sorbonne Universités, UPMC Université Paris 06, CNRS, Biologie Computationnelle et Quantitative - Institut de Biologie Paris Seine, 75005 Paris, France + to whom correspondence might be addressed Corresponding authors: Hendrik Szurmant: 309 E. 2nd Street, Pomona, CA 91766-1854, USA, tel. +1-909-706-3938, email: [email protected] Martin Weigt: LCQB UMR7238, 4 place Jussieu 75005 Paris, France, tel. +33 1 44 27 73 68, email: [email protected] Short title: Coevolutionary modeling of interacting proteins Abstract Interacting proteins coevolve at multiple but interconnected scales, from the residue-residue over the protein-protein up to the family-family level. The recent accumulation of enormous amounts of sequence data allows for the development of novel, data-driven computational approaches. Notably, these approaches can bridge scales within a single statistical framework. While being currently applied mostly to isolated problems on single scales, their immense potential for an evolutionary informed, structural systems biology is steadily emerging. Highlights • Coevolutionary modeling has an immense potential in structural systems biology. • Interaction between two protein families can be detected. • Specific interaction partners inside families can be deduced. • Contact residues between interacting proteins can be predicted. • Predicted inter-protein contacts can guide in silico protein-complex assembly. Introduction The importance of protein-protein interactions (PPI) in living systems is unquestioned. However, concerning PPI, different research communities are interested in differing aspects. -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective . -
Ploidetect Enables Pan-Cancer Analysis of the Causes and Impacts of Chromosomal Instability
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.06.455329; this version posted August 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Ploidetect enables pan-cancer analysis of the causes and impacts of chromosomal instability Luka Culibrk1,2, Jasleen K. Grewal1,2, Erin D. Pleasance1, Laura Williamson1, Karen Mungall1, Janessa Laskin3, Marco A. Marra1,4, and Steven J.M. Jones1,4, 1Canada’s Michael Smith Genome Sciences Center at BC Cancer, Vancouver, British Columbia, Canada 2Bioinformatics training program, University of British Columbia, Vancouver, British Columbia, Canada 3Department of Medical Oncology, BC Cancer, Vancouver, British Columbia, Canada 4Department of Medical Genetics, Faculty of Medicine, Vancouver, British Columbia, Canada Cancers routinely exhibit chromosomal instability, resulting in tumors mutate, these variants are considerably more difficult the accumulation of changes in the abundance of genomic ma- to detect accurately compared to other types of mutations terial, known as copy number variants (CNVs). Unfortunately, and consequently they may represent an under-explored the detection of these variants in cancer genomes is difficult. We facet of tumor biology. 20 developed Ploidetect, a software package that effectively iden- While small mutations can be determined through base tifies CNVs within whole-genome sequenced tumors. Ploidetect changes embedded within aligned sequence reads, CNVs was more sensitive to CNVs in cancer related genes within ad- are variations in DNA quantity and are typically determined vanced, pre-treated metastatic cancers than other tools, while also segmenting the most contiguously. -
BIOINFORMATICS APPLICATIONS NOTE Pages 380-381
Vol. 14 no. 4 1998 BIOINFORMATICS APPLICATIONS NOTE Pages 380-381 MView: a web-compatible database search or multiple alignment viewer NigelP. Brown, ChristopheLeroy and Chris Sander European Bioinformatics Institute (EMBLĆEBI), Wellcome Genome Campus, CambridgeCB10 1SD, UK Received on December 10, 1997; revised and accepted on January 15, 1998 Abstract may be hyperlinked to the SRS system (Etzold et al., 1996), a text field, a field of scoring information from searches, and Summary: MView is a tool for converting the results of a a field reporting the per cent identity of each sequence with sequence database search into the form of a coloured multiple respect to a preferred sequence in the alignment, usually the alignment of hits stacked against the query. Alternatively, an query in the case of a search. existing multiple alignment can be processed. In either case, Multiple alignments require minimal parsing and are the output is simply HTML, so the result is platform independent and does not require a separate application or subjected only to formatting stages. Search hits are first applet to be loaded. stacked against the ungapped query sequence and require Availability: Free from http://www.sander.ebi.ac.uk/mview/ special processing. Ungapped search (e.g. BLAST) hit subject to copyright restrictions. fragments are assembled into a single string by overlaying Contact: [email protected] them preferentially by score onto a template string, while gapped search (e.g. FASTA) hits have columns corresponding Often when running FASTA (Pearson, 1990) or BLAST to query gaps excised. Consequently, the stacked alignment is (Altschul et al., 1990), it is desired to visualize the database a patchwork of reconstituted sequences that nevertheless is hits stacked against the query sequence. -
Aggregation and Correlation Toolbox for Analyses of Genome Tracks Justin Jee Yale University
University of Massachusetts eM dical School eScholarship@UMMS Program in Bioinformatics and Integrative Biology Program in Bioinformatics and Integrative Biology Publications and Presentations 4-15-2011 ACT: aggregation and correlation toolbox for analyses of genome tracks Justin Jee Yale University Joel Rozowsky Yale University Kevin Y. Yip Yale University See next page for additional authors Follow this and additional works at: http://escholarship.umassmed.edu/bioinformatics_pubs Part of the Bioinformatics Commons, Computational Biology Commons, and the Systems Biology Commons Repository Citation Jee, Justin; Rozowsky, Joel; Yip, Kevin Y.; Lochovsky, Lucas; Bjornson, Robert; Zhong, Guoneng; Zhang, Zhengdong; Fu, Yutao; Wang, Jie; Weng, Zhiping; and Gerstein, Mark B., "ACT: aggregation and correlation toolbox for analyses of genome tracks" (2011). Program in Bioinformatics and Integrative Biology Publications and Presentations. Paper 26. http://escholarship.umassmed.edu/bioinformatics_pubs/26 This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in Program in Bioinformatics and Integrative Biology Publications and Presentations by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. ACT: aggregation and correlation toolbox for analyses of genome tracks Authors Justin Jee, Joel Rozowsky, Kevin Y. Yip, Lucas Lochovsky, Robert Bjornson, Guoneng Zhong, Zhengdong Zhang, Yutao Fu, Jie Wang, Zhiping Weng, and Mark B. Gerstein Comments © The Author(s) 2011. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non- Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. -
1 Codon-Level Information Improves Predictions of Inter-Residue Contacts in Proteins 2 by Correlated Mutation Analysis 3
1 Codon-level information improves predictions of inter-residue contacts in proteins 2 by correlated mutation analysis 3 4 5 6 7 8 Etai Jacob1,2, Ron Unger1,* and Amnon Horovitz2,* 9 10 11 1The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat- 12 Gan, 52900, 2Department of Structural Biology 13 Weizmann Institute of Science, Rehovot 7610001, Israel 14 15 16 *To whom correspondence should be addressed: 17 Amnon Horovitz ([email protected]) 18 Ron Unger ([email protected]) 19 1 20 Abstract 21 Methods for analysing correlated mutations in proteins are becoming an increasingly 22 powerful tool for predicting contacts within and between proteins. Nevertheless, 23 limitations remain due to the requirement for large multiple sequence alignments (MSA) 24 and the fact that, in general, only the relatively small number of top-ranking predictions 25 are reliable. To date, methods for analysing correlated mutations have relied exclusively 26 on amino acid MSAs as inputs. Here, we describe a new approach for analysing 27 correlated mutations that is based on combined analysis of amino acid and codon MSAs. 28 We show that a direct contact is more likely to be present when the correlation between 29 the positions is strong at the amino acid level but weak at the codon level. The 30 performance of different methods for analysing correlated mutations in predicting 31 contacts is shown to be enhanced significantly when amino acid and codon data are 32 combined. 33 2 34 The effects of mutations that disrupt protein structure and/or function at one site are often 35 suppressed by mutations that occur at other sites either in the same protein or in other 36 proteins. -
Biocreative II.5 Workshop 2009 Special Session on Digital Annotations
BioCreative II.5 Workshop 2009 Special Session on Digital Annotations The purified IRF-4 was also The main role of BRCA2 shown to be capable of binding appears to involve regulating the DNA in a PU.1-dependent manner function of RAD51 in the repair by by electrophoretic mobility shift homologous recombination . analysis. brca2 irf4 We found that cells ex- Moreover, expression of pressing Olig2, Nkx2.2, and NG2 Carma1 induces phosphorylation were enriched among virus- of Bcl10 and activation of the infected, GFP-positive (GFP+) transcription factor NF-kappaB. cells. carma1 BB I O olig2 The region of VHL medi- The Rab5 effector ating interaction with HIF-1 alpha Rabaptin-5 and its isoform C R E A T I V E overlapped with a putative Rabaptin-5delta differ in their macromolecular binding site within ability to interact with the rsmallab5 the crystal structure. GTPase Rab4. vhl Translocation RCC, bearing We show that ERBB2-dependenterbb2 atf1 TFE3 or TFEB gene fusions, are Both ATF-1 homodimers and tfe3 medulloblastoma cell invasion and ATF-1/CREB heterodimers bind to recently recognized entities for prometastatic gene expression can the CRE but not to the related which risk factors have not been be blocked using the ERBB tyrosine phorbol ester response element. identified. kinase inhibitor OSI-774. C r i t i c a l A s s e s s m e n t o f I n f o r m a t i o n E x t r a c t i o n i n B i o l o g y October 7th - 9th, 2009 www.BioCreative.org BioCreative II.5 Workshop 2009 special session | Digital Annotations Auditorium of the Spanish National -
Assessing the Utility of Residue-Residue Contact Information in a Sequence and Structure Rich Era
Supplementary Information: Assessing the utility of residue-residue contact information in a sequence and structure rich era Hetunandan Kamisetty, Sergey Ovchinnikov, David Baker Learning The basis of methods that predict contact information from Multiple Sequence Alignments is that pro- teins with statistically significant sequence similarity have similar structure and that this common structural constraint affects their compositional variability. Thus, a statistical model of the constraints on the composi- tional variability might recover structural constraints (contacts). Historically, these methods were based on Mutual information (MI) and its variations and tended to suffer from the problem of conflating direct and indirect statistical coupling: if positions (A,B) and (B,C) have a high MI; so do (A,C). Thus, even if only spatially proximal positions co-evolve in composition, non-adjacent positions can display a high MI due to transitivity. More recent methods use a global statistical model as described in Eq. 2. The use of a global statistical model allows disentangling direct interactions versus indirect transitive effects. Here, we briefly review alternate approaches to learning this model. Maximum Likelihood Estimation A common approach to learning statistical models from data is the maximum likelihood procedure [1] which for the case of the model in Eq. 2 having parameters Θ =(v,w) is v^; w^ = arg max ll(v; wjD) − R(v; w) v;w PN n where ll(v; wjD) = n=1 log P (x jv; w) is the log likelihood of the model given a set of protein se- quences D = [x1; x2::xN ] and R(v; w) is an optional penalty term added to prevent over-fitting, especially in high-dimensions. -
Are You an Invited Speaker? a Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics
Are You an Invited Speaker? A Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics Senator Jeong, Sungin Lee, and Hong-Gee Kim Biomedical Knowledge Engineering Laboratory, Seoul National University, 28–22 YeonGeon Dong, Jongno Gu, Seoul 110–749, Korea. E-mail: {senator, sunginlee, hgkim}@snu.ac.kr Participating in scholarly events (e.g., conferences, work- evaluation, but it would be hard to claim that they have pro- shops, etc.) as an elite-group member such as an orga- vided comprehensive lists of evaluation measurements. This nizing committee chair or member, program committee article aims not to provide such lists but to add to the current chair or member, session chair, invited speaker, or award winner is beneficial to a researcher’s career develop- practices an alternative metric that complements existing per- ment.The objective of this study is to investigate whether formance measures to give a more comprehensive picture of elite-group membership for scholarly events is represen- scholars’ performance. tative of scholars’ prominence, and which elite group is By one definition (Jeong, 2008), a scholarly event is the most prestigious. We collected data about 15 global “a sequentially and spatially organized collection of schol- (excluding regional) bioinformatics scholarly events held in 2007. We sampled (via stratified random sampling) ars’ interactions with the intention of delivering and shar- participants from elite groups in each event. Then, bib- ing knowledge, exchanging research ideas, and performing liometric indicators (total citations and h index) of seven related activities.” As such, scholarly events are communica- elite groups and a non-elite group, consisting of authors tion channels from which our new evaluation tool can draw who submitted at least one paper to an event but were its supporting evidence. -
Tporthmm : Predicting the Substrate Class Of
TPORTHMM : PREDICTING THE SUBSTRATE CLASS OF TRANSMEMBRANE TRANSPORT PROTEINS USING PROFILE HIDDEN MARKOV MODELS Shiva Shamloo A thesis in The Department of Computer Science Presented in Partial Fulfillment of the Requirements For the Degree of Master of Computer Science Concordia University Montréal, Québec, Canada December 2020 © Shiva Shamloo, 2020 Concordia University School of Graduate Studies This is to certify that the thesis prepared By: Shiva Shamloo Entitled: TportHMM : Predicting the substrate class of transmembrane transport proteins using profile Hidden Markov Models and submitted in partial fulfillment of the requirements for the degree of Master of Computer Science complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Signed by the final examining commitee: Examiner Dr. Sabine Bergler Examiner Dr. Andrew Delong Supervisor Dr. Gregory Butler Approved Dr. Lata Narayanan, Chair Department of Computer Science and Software Engineering 20 Dean Dr. Mourad Debbabi Faculty of Engineering and Computer Science Abstract TportHMM : Predicting the substrate class of transmembrane transport proteins using profile Hidden Markov Models Shiva Shamloo Transporters make up a large proportion of proteins in a cell, and play important roles in metabolism, regulation, and signal transduction by mediating movement of compounds across membranes but they are among the least characterized proteins due to their hydropho- bic surfaces and lack of conformational stability. There is a need for tools that predict the substrates which are transported at the level of substrate class and the level of specific substrate. This work develops a predictor, TportHMM, using profile Hidden Markov Model (HMM) and Multiple Sequence Alignment (MSA).