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Improving the Prediction of Transcription Factor Binding Sites To
Improving the prediction of transcription factor binding sites to aid the interpretation of non-coding single nucleotide variants. Narayan Jayaram Research Department of Structural and Molecular Biology University College London A thesis submitted to University College London for the degree of Doctor of Philosophy 1 Declaration I, Narayan Jayaram confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Narayan Jayaram 2 Abstract Single nucleotide variants (SNVs) that occur in transcription factor binding sites (TFBSs) can disrupt the binding of transcription factors and alter gene expression which can cause inherited diseases and act as driver SNVs in cancer. The identification of SNVs in TFBSs has historically been challenging given the limited number of experimentally characterised TFBSs. The recent ENCODE project has resulted in the availability of ChIP-Seq data that provides genome wide sets of regions bound by transcription factors. These data have the potential to improve the identification of SNVs in TFBSs. However, as the ChIP-Seq data identify a broader range of DNA in which a transcription factor binds, computational prediction is required to identify the precise TFBS. Prediction of TFBSs involves scanning a DNA sequence with a Position Weight Matrix (PWM) using a pattern matching tool. This thesis focusses on the prediction of TFBSs by: (a) evaluating a set of locally-installable pattern-matching tools and identifying the best performing tool (FIMO), (b) using the ENCODE ChIP-Seq data to evaluate a set of de novo motif discovery tools that are used to derive PWMs which can handle large volumes of data, (c) identifying the best performing tool (rGADEM), (d) using rGADEM to generate a set of PWMs from the ENCODE ChIP-Seq data and (e) by finally checking that the selection of the best pattern matching tool is not unduly influenced by the choice of PWMs. -
Applied Category Theory for Genomics – an Initiative
Applied Category Theory for Genomics { An Initiative Yanying Wu1,2 1Centre for Neural Circuits and Behaviour, University of Oxford, UK 2Department of Physiology, Anatomy and Genetics, University of Oxford, UK 06 Sept, 2020 Abstract The ultimate secret of all lives on earth is hidden in their genomes { a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side. arXiv:2009.02822v1 [q-bio.GN] 6 Sep 2020 1 Introduction DNA, the genetic material of all living beings on this planet, holds the secret of life. The complete set of DNA sequences in an organism constitutes its genome { the blueprint and instruction manual of that organism, be it a human or fly [1]. Therefore, genomics, which studies the contents and meaning of genomes, has been standing in the central stage of scientific research since its birth. The twentieth century witnessed three milestones of genomics research [1]. It began with the discovery of Mendel's laws of inheritance [2], sparked a climax in the middle with the reveal of DNA double helix structure [3], and ended with the accomplishment of a first draft of complete human genome sequences [4]. -
Enhanced Representation of Natural Product Metabolism in Uniprotkb
H OH metabolites OH Article Diverse Taxonomies for Diverse Chemistries: Enhanced Representation of Natural Product Metabolism in UniProtKB Marc Feuermann 1,* , Emmanuel Boutet 1,* , Anne Morgat 1 , Kristian B. Axelsen 1, Parit Bansal 1, Jerven Bolleman 1 , Edouard de Castro 1, Elisabeth Coudert 1, Elisabeth Gasteiger 1,Sébastien Géhant 1, Damien Lieberherr 1, Thierry Lombardot 1,†, Teresa B. Neto 1, Ivo Pedruzzi 1, Sylvain Poux 1, Monica Pozzato 1, Nicole Redaschi 1 , Alan Bridge 1 and on behalf of the UniProt Consortium 1,2,3,4,‡ 1 Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 Michel-Servet, CH-1211 Geneva 4, Switzerland; [email protected] (A.M.); [email protected] (K.B.A.); [email protected] (P.B.); [email protected] (J.B.); [email protected] (E.d.C.); [email protected] (E.C.); [email protected] (E.G.); [email protected] (S.G.); [email protected] (D.L.); [email protected] (T.L.); [email protected] (T.B.N.); [email protected] (I.P.); [email protected] (S.P.); [email protected] (M.P.); [email protected] (N.R.); [email protected] (A.B.); [email protected] (U.C.) 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK 3 Protein Information Resource, University of Delaware, 15 Innovation Way, Suite 205, Newark, DE 19711, USA 4 Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven Street NorthWest, Suite 1200, Washington, DC 20007, USA * Correspondence: [email protected] (M.F.); [email protected] (E.B.); Tel.: +41-22-379-58-75 (M.F.); +41-22-379-49-10 (E.B.) † Current address: Centre Informatique, Division Calcul et Soutien à la Recherche, University of Lausanne, CH-1015 Lausanne, Switzerland. -
The EMBL-European Bioinformatics Institute the Hub for Bioinformatics in Europe
The EMBL-European Bioinformatics Institute The hub for bioinformatics in Europe Blaise T.F. Alako, PhD [email protected] www.ebi.ac.uk What is EMBL-EBI? • Part of the European Molecular Biology Laboratory • International, non-profit research institute • Europe’s hub for biological data, services and research The European Molecular Biology Laboratory Heidelberg Hamburg Hinxton, Cambridge Basic research Structural biology Bioinformatics Administration Grenoble Monterotondo, Rome EMBO EMBL staff: 1500 people Structural biology Mouse biology >60 nationalities EMBL member states Austria, Belgium, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom Associate member state: Australia Who we are ~500 members of staff ~400 work in services & support >53 nationalities ~120 focus on basic research EMBL-EBI’s mission • Provide freely available data and bioinformatics services to all facets of the scientific community in ways that promote scientific progress • Contribute to the advancement of biology through basic investigator-driven research in bioinformatics • Provide advanced bioinformatics training to scientists at all levels, from PhD students to independent investigators • Help disseminate cutting-edge technologies to industry • Coordinate biological data provision throughout Europe Services Data and tools for molecular life science www.ebi.ac.uk/services Browse our services 9 What services do we provide? Labs around the -
Ismb/Eccb 2015
Research Collection Journal Article ISMB/ECCB 2015 Author(s): Moreau, Yves; Beerenwinkel, Niko Publication Date: 2015 Permanent Link: https://doi.org/10.3929/ethz-b-000102416 Originally published in: Bioinformatics 31(12), http://doi.org/10.1093/bioinformatics/btv303 Rights / License: Creative Commons Attribution-NonCommercial 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Bioinformatics, 31, 2015, i1–i2 doi: 10.1093/bioinformatics/btv303 ISMB/ECCB 2015 Editorial ISMB/ECCB 2015 This special issue of Bioinformatics serves as the proceedings of the 175 external reviewers recruited as sub-reviewers by program com- joint 23rd annual meeting of Intelligent Systems for Molecular mittee members. Table 1 provides a summary of the areas, area Biology (ISMB) and 14th European Conference on Computational chairs and a review summary by area. The conference used a two- Biology (ECCB), which took place in Dublin, Ireland, July 10–14, tier review system—a continuation and refinement of a process that 2015 (http://www.iscb.org/ismbeccb2015). ISMB/ECCB 2015, the begun with ISMB/ECCB 2013 in an effort to better ensure thorough official conference of the International Society for Computational and fair reviewing. Under the revised process, each of the 241 sub- Biology (ISCB, http://www.iscb.org/), was accompanied by nine missions was first reviewed by at least three expert referees, with a Special Interest Group meetings of 1 or 2 days each, and two satel- subset receiving between four and six reviews, as needed. lite meetings. -
Algorithms for Computational Biology 8Th International Conference, Alcob 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings
Lecture Notes in Bioinformatics 12715 Subseries of Lecture Notes in Computer Science Series Editors Sorin Istrail Brown University, Providence, RI, USA Pavel Pevzner University of California, San Diego, CA, USA Michael Waterman University of Southern California, Los Angeles, CA, USA Editorial Board Members Søren Brunak Technical University of Denmark, Kongens Lyngby, Denmark Mikhail S. Gelfand IITP, Research and Training Center on Bioinformatics, Moscow, Russia Thomas Lengauer Max Planck Institute for Informatics, Saarbrücken, Germany Satoru Miyano University of Tokyo, Tokyo, Japan Eugene Myers Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Marie-France Sagot Université Lyon 1, Villeurbanne, France David Sankoff University of Ottawa, Ottawa, Canada Ron Shamir Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Terry Speed Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia Martin Vingron Max Planck Institute for Molecular Genetics, Berlin, Germany W. Eric Wong University of Texas at Dallas, Richardson, TX, USA More information about this subseries at http://www.springer.com/series/5381 Carlos Martín-Vide • Miguel A. Vega-Rodríguez • Travis Wheeler (Eds.) Algorithms for Computational Biology 8th International Conference, AlCoB 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings 123 Editors Carlos Martín-Vide Miguel A. Vega-Rodríguez Rovira i Virgili University University of Extremadura Tarragona, Spain Cáceres, Spain Travis Wheeler University of Montana Missoula, MT, USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Bioinformatics ISBN 978-3-030-74431-1 ISBN 978-3-030-74432-8 (eBook) https://doi.org/10.1007/978-3-030-74432-8 LNCS Sublibrary: SL8 – Bioinformatics © Springer Nature Switzerland AG 2021 This work is subject to copyright. -
Computational Pan-Genomics: Status, Promises and Challenges
bioRxiv preprint doi: https://doi.org/10.1101/043430; this version posted March 12, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Computational Pan-Genomics: Status, Promises and Challenges Tobias Marschall1,2, Manja Marz3,60,61,62, Thomas Abeel49, Louis Dijkstra6,7, Bas E. Dutilh8,9,10, Ali Ghaffaari1,2, Paul Kersey11, Wigard P. Kloosterman12, Veli M¨akinen13, Adam Novak15, Benedict Paten15, David Porubsky16, Eric Rivals17,63, Can Alkan18, Jasmijn Baaijens5, Paul I. W. De Bakker12, Valentina Boeva19,64,65,66, Francesca Chiaromonte20, Rayan Chikhi21, Francesca D. Ciccarelli22, Robin Cijvat23, Erwin Datema24,25,26, Cornelia M. Van Duijn27, Evan E. Eichler28, Corinna Ernst29, Eleazar Eskin30,31, Erik Garrison32, Mohammed El-Kebir5,33,34, Gunnar W. Klau5, Jan O. Korbel11,35, Eric-Wubbo Lameijer36, Benjamin Langmead37, Marcel Martin59, Paul Medvedev38,39,40, John C. Mu41, Pieter Neerincx36, Klaasjan Ouwens42,67, Pierre Peterlongo43, Nadia Pisanti44,45, Sven Rahmann29, Ben Raphael46,47, Knut Reinert48, Dick de Ridder50, Jeroen de Ridder49, Matthias Schlesner51, Ole Schulz-Trieglaff52, Ashley Sanders53, Siavash Sheikhizadeh50, Carl Shneider54, Sandra Smit50, Daniel Valenzuela13, Jiayin Wang70,71,72, Lodewyk Wessels56, Ying Zhang23,5, Victor Guryev16,12, Fabio Vandin57,34, Kai Ye68,69,72 and Alexander Sch¨onhuth5 1Center for Bioinformatics, Saarland University, Saarbr¨ucken, Germany; 2Max Planck Institute for Informatics, Saarbr¨ucken, -
Article Reference
Article Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines BABIC, Zeljana, et al. Abstract The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines. Reference BABIC, Zeljana, et al. Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines. eLife, 2019, vol. 8, p. e41676 DOI : 10.7554/eLife.41676 PMID : 30693867 Available at: http://archive-ouverte.unige.ch/unige:119832 Disclaimer: layout of this document may differ from the published version. 1 / 1 FEATURE ARTICLE META-RESEARCH Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines Abstract The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. -
Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2
Cancer Focus on Computer Resources Research Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2 Cancer is a remarkably adaptable and formidable foe. Cancer Precision Medicine Initiative highlighted the importance of data- exploits many biological mechanisms to confuse and subvert driven cancer research, translational research, and its application normal physiologic and cellular processes, to adapt to thera- to decision making in cancer treatment (https://www.cancer.gov/ pies, and to evade the immune system. Decades of research and research/key-initiatives/precision-medicine). And the National significant national and international investments in cancer Strategic Computing Initiative highlighted the importance of research have dramatically increased our knowledge of the computing as a national competitive asset and included a focus disease, leading to improvements in cancer diagnosis, treat- on applying computing in biomedical research. Articles in the ment, and management, resulting in improved outcomes for mainstream media, such as that by Siddhartha Mukherjee in many patients. the New Yorker in April of 2017 (http://www.newyorker.com/ In melanoma, the V600E mutation in the BRAF gene is now magazine/2017/04/03/ai-versus-md), have emphasized the targetable by a specific therapy. BRAF is a serine/threonine protein growing importance of computing, machine learning, and data kinase activating the MAP kinase (MAPK)/ERK signaling pathway, in biomedicine. and both BRAF and MEK inhibitors, such as vemurafenib and The NCI (Rockville, MD) recognized the need to invest in dabrafenib, have shown dramatic responses in patients carrying informatics. In 2011, it established a funding opportunity, the mutation. -
Methodology for Predicting Semantic Annotations of Protein Sequences by Feature Extraction Derived of Statistical Contact Potentials and Continuous Wavelet Transform
Universidad Nacional de Colombia Sede Manizales Master’s Thesis Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform Author: Supervisor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez A thesis submitted in fulfillment of the requirements for the degree of Master’s on Engineering - Industrial Automation in the Department of Electronic, Electric Engineering and Computation Signal Processing and Recognition Group June 2014 Universidad Nacional de Colombia Sede Manizales Tesis de Maestr´ıa Metodolog´ıapara predecir la anotaci´on sem´antica de prote´ınaspor medio de extracci´on de caracter´ısticas derivadas de potenciales de contacto y transformada wavelet continua Autor: Tutor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez Tesis presentada en cumplimiento a los requerimientos necesarios para obtener el grado de Maestr´ıaen Ingenier´ıaen Automatizaci´onIndustrial en el Departamento de Ingenier´ıaEl´ectrica,Electr´onicay Computaci´on Grupo de Procesamiento Digital de Senales Enero 2014 UNIVERSIDAD NACIONAL DE COLOMBIA Abstract Faculty of Engineering and Architecture Department of Electronic, Electric Engineering and Computation Master’s on Engineering - Industrial Automation Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform by Gustavo Alonso Arango Argoty In this thesis, a method to predict semantic annotations of the proteins from its primary structure is proposed. The main contribution of this thesis lies in the implementation of a novel protein feature representation, which makes use of the pairwise statistical contact potentials describing the protein interactions and geometry at the atomic level. -
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.