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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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. -
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. -
Full List of PCAWG Consortium Working Groups and Writing
Supplementary information to: Genomics: data sharing needs an international code of conduct To accompany a Comment published in Nature 578, 31–33 (2020) https://www.nature.com/articles/d41586-020-00082-9 By Mark Phillips, Fruzsina Molnár-Gábor, Jan O. Korbel, Adrian Thorogood, Yann Joly, Don Chalmers, David Townend & Bartha M. Knoppers for the PCAWG Consortium. SUPPLEMENTARY INFORMATION | NATURE | 1 The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium Working Groups PCAWG Steering committee 1,2 3,4,5,6 7,8 9,10 Peter J Campbell# , Gad Getz# , Jan O Korbel# , Lincoln D Stein# and Joshua M Stuart#11,12 PCAWG Head of project management Jennifer L Jennings13 PCAWG Executive committee 14 15 16 17 18 Sultan T Al-Sedairy , Axel Aretz , Cindy Bell , Miguel Betancourt , Christiane Buchholz , 19 20 21 22 23 Fabien Calvo , Christine Chomienne , Michael Dunn , Stuart Edmonds , Eric Green , Shailja 24 23 25 13 26 Gupta , Carolyn M Hutter , Karine Jegalian , Jennifer L Jennings , Nic Jones , Hyung-Lae 27 28,29,30 31 32 32 26 Kim , Youyong Lu , Hitoshi Nakagama , Gerd Nettekoven , Laura Planko , David Scott , 3 3,34 35 9,10 1 35 Tatsuhiro Shibata , Kiyo Shimizu , Lincoln D Stein# , Michael R Stratton , Takashi Yugawa , 36,37 24 38 39 Giampaolo Tortora , K VijayRaghavan , Huanming Yang and Jean C Zenklusen PCAWG Ethics and Legal Working Group 40 41 41 42 41 Don Chalmers# , Yann Joly , Bartha M Knoppers# , Fruzsina -
Biological Pathways Exchange Language Level 3, Release Version 1 Documentation
BioPAX – Biological Pathways Exchange Language Level 3, Release Version 1 Documentation BioPAX Release, July 2010. The BioPAX data exchange format is the joint work of the BioPAX workgroup and Level 3 builds on the work of Level 2 and Level 1. BioPAX Level 3 input from: Mirit Aladjem, Ozgun Babur, Gary D. Bader, Michael Blinov, Burk Braun, Michelle Carrillo, Michael P. Cary, Kei-Hoi Cheung, Julio Collado-Vides, Dan Corwin, Emek Demir, Peter D'Eustachio, Ken Fukuda, Marc Gillespie, Li Gong, Gopal Gopinathrao, Nan Guo, Peter Hornbeck, Michael Hucka, Olivier Hubaut, Geeta Joshi- Tope, Peter Karp, Shiva Krupa, Christian Lemer, Joanne Luciano, Irma Martinez-Flores, Zheng Li, David Merberg, Huaiyu Mi, Ion Moraru, Nicolas Le Novere, Elgar Pichler, Suzanne Paley, Monica Penaloza- Spinola, Victoria Petri, Elgar Pichler, Alex Pico, Harsha Rajasimha, Ranjani Ramakrishnan, Dean Ravenscroft, Jonathan Rees, Liya Ren, Oliver Ruebenacker, Alan Ruttenberg, Matthias Samwald, Chris Sander, Frank Schacherer, Carl Schaefer, James Schaff, Nigam Shah, Andrea Splendiani, Paul Thomas, Imre Vastrik, Ryan Whaley, Edgar Wingender, Guanming Wu, Jeremy Zucker BioPAX Level 2 input from: Mirit Aladjem, Gary D. Bader, Ewan Birney, Michael P. Cary, Dan Corwin, Kam Dahlquist, Emek Demir, Peter D'Eustachio, Ken Fukuda, Frank Gibbons, Marc Gillespie, Michael Hucka, Geeta Joshi-Tope, David Kane, Peter Karp, Christian Lemer, Joanne Luciano, Elgar Pichler, Eric Neumann, Suzanne Paley, Harsha Rajasimha, Jonathan Rees, Alan Ruttenberg, Andrey Rzhetsky, Chris Sander, Frank Schacherer, -
Systems Biology Graphical Notation: Process Description Language Level 1
Erschienen in: Journal of integrative bioinformatics ; 16 (2019), 2. - 20190022 https://dx.doi.org/10.1515/jib-2019-0022 DE GRUYTER Journal of Integrative Bioinformatics. 2019; 20190022 Adrien Rougny1,2 / Vasundra Touré3 / Stuart Moodie4 / Irina Balaur5 / Tobias Czauderna 6 / Hanna Borlinghaus7 / Ugur Dogrusoz8,9 / Alexander Mazein5,10,11 / Andreas Dräger12,13,14 / Michael L. Blinov15 / Alice Villéger16 / Robin Haw17 / Emek Demir18,19 / Huaiyu Mi20 / Anatoly Sorokin11 / Falk Schreiber6,7 / Augustin Luna21,22 Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0 1 Biotechnology Research Institute for Drug Discovery, AIST, Tokyo135-0064, Japan, E-mail: [email protected]. https://orcid.org/0000-0002-2118-035X. 2 Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST, Tokyo 169-8555, Japan, E-mail: [email protected]. https://orcid.org/0000-0002-2118-035X. 3 Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. https://orcid.org/0000-0003-4639-4431. 4 Eight Pillars Ltd, 19 Redford Walk, Edinburgh EH13 0AG, UK 5 European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Av- enue Tony Garnier, 69007 Lyon, France. https://orcid.org/0000-0002-3671-895X, https://orcid.org/0000-0001-7137-4171. 6 Faculty of Information Technology, Monash University, Melbourne, Australia. https://orcid.org/0000-0002-1788-9593. 7 Department of Computer and Information Science, University of Konstanz, Konstanz, Germany. https://orcid.org/0000-0002-5410-6877. 8 Computer Engineering Department, Bilkent University, Ankara 06800, Turkey. https://orcid.org/0000-0002-7153-0784. 9 i-Vis Research Lab, Bilkent University, Ankara 06800, Turkey. -
I S C B N E W S L E T T
ISCB NEWSLETTER FOCUS ISSUE {contents} President’s Letter 2 Member Involvement Encouraged Register for ISMB 2002 3 Registration and Tutorial Update Host ISMB 2004 or 2005 3 David Baker 4 2002 Overton Prize Recipient Overton Endowment 4 ISMB 2002 Committees 4 ISMB 2002 Opportunities 5 Sponsor and Exhibitor Benefits Best Paper Award by SGI 5 ISMB 2002 SIGs 6 New Program for 2002 ISMB Goes Down Under 7 Planning Underway for 2003 Hot Jobs! Top Companies! 8 ISMB 2002 Job Fair ISCB Board Nominations 8 Bioinformatics Pioneers 9 ISMB 2002 Keynote Speakers Invited Editorial 10 Anna Tramontano: Bioinformatics in Europe Software Recommendations11 ISCB Software Statement volume 5. issue 2. summer 2002 Community Development 12 ISCB’s Regional Affiliates Program ISCB Staff Introduction 12 Fellowship Recipients 13 Awardees at RECOMB 2002 Events and Opportunities 14 Bioinformatics events world wide INTERNATIONAL SOCIETY FOR COMPUTATIONAL BIOLOGY A NOTE FROM ISCB PRESIDENT This newsletter is packed with information on development and dissemination of bioinfor- the ISMB2002 conference. With over 200 matics. Issues arise from recommendations paper submissions and over 500 poster submis- made by the Society’s committees, Board of sions, the conference promises to be a scientific Directors, and membership at large. Important feast. On behalf of the ISCB’s Directors, staff, issues are defined as motions and are discussed EXECUTIVE COMMITTEE and membership, I would like to thank the by the Board of Directors on a bi-monthly Philip E. Bourne, Ph.D., President organizing committee, local organizing com- teleconference. Motions that pass are enacted Michael Gribskov, Ph.D., mittee, and program committee for their hard by the Executive Committee which also serves Vice President work preparing for the conference. -
Mapping the Protein Universe
SCIENCE Online -- Holm and Sander 273 (5275):595 http://www.sciencemag.org/cgi/content/fu...endit:Search!author1:holm!author2:sander [Go to SCIENCE Online with fewer graphics] MARK GERSTEIN || Change Password || View/Manage User Information || Subscription HELP || Sign Out Other Articles which have Cited this Article PubMed Citation || Related Articles in PubMed || Download to Citation Manager Mapping the Protein Universe Liisa Holm and Chris Sander The comparison of the three-dimensional shapes of protein molecules poses a complex algorithmic problem. Its solution provides biologists with computational tools to organize the rapidly growing set of thousands of known protein shapes, to identify new types of protein architecture, and to discover unexpected evolutionary relations, reaching back billions of years, between protein molecules. Protein shape comparison also improves tools for identifying gene functions in genome databases by defining the essential sequence-structure features of a protein family. Finally, an exhaustive all-on-all shape comparison provides a map of physical attractor regions in the abstract shape space of proteins, with implications for the processes of protein folding and evolution. The authors are in the European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton Hall, Cambridge CB10 1SD, UK. If a living cell is viewed as a biochemical factory, then its main workers are protein molecules, acting as catalysts, transporters, and messengers, among other roles. A human genome encodes about 100,000 of these biological macromolecules. Their functional diversity is made possible by the diversity of three-dimensional (3D) protein shapes, also called "structures," which are capable of highly specific molecular recognition. Understanding or simulating the molecular processes involved in the formation of protein structures and in their biological function is a major challenge of molecular biology. -
A General Mathematical Framework for Understanding the Behavior of Heterogeneous Stem Cell Regeneration Arxiv:1903.11448V1 [Q-B
A general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration Jinzhi Lei Zhou Pei-Yuan Center for Applied Mathematics, MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China. Abstract Stem cell heterogeneity is essential for the homeostasis in tissue development. This paper established a general formulation for understanding the dynamics of stem cell regeneration with cell heterogeneity and random transitions of epigenetic states. The model generalizes the classical G0 cell cycle model, and incorporates the epigenetic states of stem cells that are represented by a contin- uous multidimensional variable and the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, that are dependent on their epigenetic states. Moreover, the random transition of epigenetic states is represented by an inheritance probability that can be described as a conditional beta distribution. This model can be extended to investigate gene mutation-induced tumor develop- ment. The proposed formula is a generalized formula that helps us to understand various dynamic processes of stem cell regeneration, including tissue development, degeneration, and abnormal growth. Keywords: Heterogeneity, stem cell, cell cycle, epigenetic state, development, computational model 1 Introduction Stem cell regeneration is an essential biological process in most self-renewing tis- arXiv:1903.11448v1 [q-bio.PE] 27 Mar 2019 sues during development and the maintenance of tissue homeostasis. Stem cells multiply by cell division, during which DNA is replicated and assigned to the two daughter cells along with the inheritance of epigenetic information and the parti- tion of molecules. Unlike the accumulated process of DNA replication, inherited epigenetic information is often subjected to random perturbations; for example, the 1 reconstruction of histone modifications and DNA methylation are intrinsically ran- dom processes of writing and erasing the modified markers [71, 91]. -
Bioinformatics 2 -- Lecture 3
Bioinformatics 2 -- lecture 3 Rotation and translation Superposition, structure comparison Structure classification Rotation is angular addition y atom starts at (x=|r|cosα, y=|r|sinα) axis of (x’,y’) rotation = Cartesian β (x,y) origin r α x ..rotates to... (x'=|r|cos(α+β), y'=|r|sin(α+β)) Convention: angles are measured counter-clockwise. 1 Sum of angles formula cos (α+β) = cos α cos β − sin α sin β sin (α+β) = sin α cos β + sin β cos α A rotation matrix y α x = |r|cos (x’,y’) y = |r|sin α β (x,y) r α x x' = |r| cos (α+β) y' = |r| sin (α+β) = |r|(cos α cos β − sin α sin β) = |r|(sin α cos β + sin β cos α) = (|r| cos α) cos β − (|r| sin α)sin β = (|r| sin α) cos β + (|r| cos α) sin β = x cos β − y sin β = y cos β + x sin β x' cosββ− sin r cosα cosββ− sin x = = y' sinββ cos rsinα sinββ cos y rotation matrix is the same for any r, any α. 2 A rotation around a principle axis The Z coordinate stays the same. X and Y change. cosββ− sin 0 R = sinββ cos 0 z 001 The Y coordinate stays the same. X and Z change. cosγγ0 sin R = 010 y −sinγγ0 cos The X coordinate stays the same. Y and Z change. 10 0 0 cosαα− sin Rx = 0 sinαα cos A 3D rotation matrix Is the product of 2D rotation matrices. -
Generating Functional Protein Variants with Variational Autoencoders
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.07.029264; this version posted April 7, 2020. 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 4.0 International license. 1 Generating functional protein variants with variational 2 autoencoders 1 1 1 3 Alex Hawkins-Hooker , Florence Depardieu , Sebastien Baur , Guillaume 1 1 1 4 Couairon , Arthur Chen , and David Bikard 1 5 Synthetic Biology Group, Microbiology Department, Institut Pasteur, Paris, France 6 Abstract 7 The design of novel proteins with specified function and controllable biochemical properties 8 is a longstanding goal in bio-engineering with potential applications across medicine and nan- 9 otechnology. The vast expansion of protein sequence databases over the last decades provides 10 an opportunity for new approaches which seek to learn the sequence-function relationship 11 directly from natural sequence variation. Advances in deep generative models have led to 12 the successful modelling of diverse kinds of high-dimensional data, from images to molecules, 13 allowing the generation of novel, realistic samples. While deep models trained on protein 14 sequence data have been shown to learn biologically meaningful representations helpful for 15 a variety of downstream tasks, their potential for direct use in protein engineering remains 16 largely unexplored. Here we show that variational autoencoders trained on a dataset of almost 17 70000 luciferase-like oxidoreductases can be used to generate novel, functional variants of the 18 luxA bacterial luciferase.