Cold Spring Harbor Asia Bioinformatics of Human and Animal
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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 . -
Curriculum Vitae – Prof. Anders Krogh Personal Information
Curriculum Vitae – Prof. Anders Krogh Personal Information Date of Birth: May 2nd, 1959 Private Address: Borgmester Jensens Alle 22, st th, 2100 København Ø, Denmark Contact information: Dept. of Biology, Univ. of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark. +45 3532 1329, [email protected] Web: https://scholar.google.com/citations?user=-vGMjmwAAAAJ Education Sept 1991 Ph.D. (Physics), Niels Bohr Institute, Univ. of Copenhagen, Denmark June 1987 Cand. Scient. [M. Sc.] (Physics and mathematics), NBI, Univ. of Copenhagen Professional / Work Experience (since 2000) 2018 – Professor of Bionformatics, Dept of Computer Science (50%) and Dept of Biology (50%), Univ. of Copenhagen 2002 – 2018 Professor of Bionformatics, Dept of Biology, Univ. of Copenhagen 2009 – 2018 Head of Section for Computational and RNA Biology, Dept. of Biology, Univ. of Copenhagen 2000–2002 Associate Prof., Technical Univ. of Denmark (DTU), Copenhagen Prices and Awards 2017 – Fellow of the International Society for Computational Biology https://www.iscb.org/iscb- fellows-program 2008 – Fellow, Royal Danish Academy of Sciences and Letters Public Activities & Appointments (since 2009) 2014 – Board member, Elixir, European Infrastructure for Life Science. 2014 – Steering committee member, Danish Elixir Node. 2012 – 2016 Board member, Bioinformatics Infrastructure for Life Sciences (BILS), Swedish Research Council 2011 – 2016 Director, Centre for Computational and Applied Transcriptomics (COAT) 2009 – Associate editor, BMC Bioinformatics Publications § Google Scholar: https://scholar.google.com/citations?user=-vGMjmwAAAAJ § ORCID: 0000-0002-5147-6282. ResearcherID: M-1541-2014 § Co-author of 130 peer-reviewed papers and 2 monographs § 63,000 citations and h-index of 74 (Google Scholar, June 2019) § H-index of 54 in Web of science (June 2019) § Publications in high-impact journals: Nature (5), Science (1), Cell (1), Nature Genetics (2), Nature Biotechnology (2), Nature Communications (4), Cell (1, to appear), Genome Res. -
PREDICTD: Parallel Epigenomics Data Imputation with Cloud-Based Tensor Decomposition
bioRxiv preprint doi: https://doi.org/10.1101/123927; this version posted April 4, 2017. 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 4.0 International license. PREDICTD: PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition Timothy J. Durham Maxwell W. Libbrecht Department of Genome Sciences Department of Genome Sciences University of Washington University of Washington J. Jeffry Howbert Jeff Bilmes Department of Genome Sciences Department of Electrical Engineering University of Washington University of Washington William Stafford Noble Department of Genome Sciences Department of Computer Science and Engineering University of Washington April 4, 2017 Abstract The Encyclopedia of DNA Elements (ENCODE) and the Roadmap Epigenomics Project have produced thousands of data sets mapping the epigenome in hundreds of cell types. How- ever, the number of cell types remains too great to comprehensively map given current time and financial constraints. We present a method, PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD), to address this issue by computationally im- puting missing experiments in collections of epigenomics experiments. PREDICTD leverages an intuitive and natural model called \tensor decomposition" to impute many experiments si- multaneously. Compared with the current state-of-the-art method, ChromImpute, PREDICTD produces lower overall mean squared error, and combining methods yields further improvement. We show that PREDICTD data can be used to investigate enhancer biology at non-coding human accelerated regions. PREDICTD provides reference imputed data sets and open-source software for investigating new cell types, and demonstrates the utility of tensor decomposition and cloud computing, two technologies increasingly applicable in bioinformatics. -
Predicting Transmembrane Topology and Signal Peptides with Hidden Markov Models
i i “thesis” — 2006/3/6 — 10:55 — page i — #1 i i From the Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden Predicting transmembrane topology and signal peptides with hidden Markov models Lukas Käll Stockholm, 2006 i i i i i i “thesis” — 2006/3/6 — 10:55 — page ii — #2 i i ©Lukas Käll, 2006 Except previously published papers which were reproduced with permission from the publisher. Paper I: ©2002 Federation of European Biochemical Societies Paper II: ©2004 Elsevier Ltd. Paper III: ©2005 Federation of European Biochemical Societies Paper IV: ©2005 Lukas Käll, Anders Krogh and Erik Sonnhammer Paper V: ©2006 ¿e Protein Society Published and printed by Larserics Digital Print, Sundbyberg ISBN 91-7140-719-7 i i i i i i “thesis” — 2006/3/6 — 10:55 — page iii — #3 i i Abstract Transmembrane proteins make up a large and important class of proteins. About 20% of all genes encode transmembrane proteins. ¿ey control both substances and information going in and out of a cell. Yet basic knowledge about membrane insertion and folding is sparse, and our ability to identify, over-express, purify, and crystallize transmembrane proteins lags far behind the eld of water-soluble proteins. It is dicult to determine the three dimensional structures of transmembrane proteins. ¿ere- fore, researchers normally attempt to determine their topology, i.e. which parts of the protein are buried in the membrane, and on what side of the membrane are the other parts located. Proteins aimed for export have an N-terminal sequence known as a signal peptide that is in- serted into the membrane and cleaved o. -
Biological Sequence Analysis Probabilistic Models of Proteins and Nucleic Acids
This page intentionally left blank Biological sequence analysis Probabilistic models of proteins and nucleic acids The face of biology has been changed by the emergence of modern molecular genetics. Among the most exciting advances are large-scale DNA sequencing efforts such as the Human Genome Project which are producing an immense amount of data. The need to understand the data is becoming ever more pressing. Demands for sophisticated analyses of biological sequences are driving forward the newly-created and explosively expanding research area of computational molecular biology, or bioinformatics. Many of the most powerful sequence analysis methods are now based on principles of probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the significance of sequence alignments, the use of hidden Markov models as the basis for profile searches to identify distant members of sequence families, and the inference of phylogenetic trees using maximum likelihood approaches. This book provides the first unified, up-to-date, and tutorial-level overview of sequence analysis methods, with particular emphasis on probabilistic modelling. Pairwise alignment, hidden Markov models, multiple alignment, profile searches, RNA secondary structure analysis, and phylogenetic inference are treated at length. Written by an interdisciplinary team of authors, the book is accessible to molecular biologists, computer scientists and mathematicians with no formal knowledge of each others’ fields. It presents the state-of-the-art in this important, new and rapidly developing discipline. Richard Durbin is Head of the Informatics Division at the Sanger Centre in Cambridge, England. Sean Eddy is Assistant Professor at Washington University’s School of Medicine and also one of the Principle Investigators at the Washington University Genome Sequencing Center. -
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). -
Cancer Immunology of Cutaneous Melanoma: a Systems Biology Approach
Cancer Immunology of Cutaneous Melanoma: A Systems Biology Approach Mindy Stephania De Los Ángeles Muñoz Miranda Doctoral Thesis presented to Bioinformatics Graduate Program at Institute of Mathematics and Statistics of University of São Paulo to obtain Doctor of Science degree Concentration Area: Bioinformatics Supervisor: Prof. Dr. Helder Takashi Imoto Nakaya During the project development the author received funding from CAPES São Paulo, September 2020 Mindy Stephania De Los Ángeles Muñoz Miranda Imunologia do Câncer de Melanoma Cutâneo: Uma abordagem de Biologia de Sistemas VERSÃO CORRIGIDA Esta versão de tese contém as correções e alterações sugeridas pela Comissão Julgadora durante a defesa da versão original do trabalho, realizada em 28/09/2020. Uma cópia da versão original está disponível no Instituto de Matemática e Estatística da Universidade de São Paulo. This thesis version contains the corrections and changes suggested by the Committee during the defense of the original version of the work presented on 09/28/2020. A copy of the original version is available at Institute of Mathematics and Statistics at the University of São Paulo. Comissão Julgadora: • Prof. Dr. Helder Takashi Imoto Nakaya (Orientador, Não Votante) - FCF-USP • Prof. Dr. André Fujita - IME-USP • Profa. Dra. Patricia Abrão Possik - INCA-Externo • Profa. Dra. Ana Carolina Tahira - I.Butantan-Externo i FICHA CATALOGRÁFICA Muñoz Miranda, Mindy StephaniaFicha de Catalográfica los Ángeles M967 Cancer immunology of cutaneous melanoma: a systems biology approach = Imuno- logia do câncer de melanoma cutâneo: uma abordagem de biologia de sistemas / Mindy Stephania de los Ángeles Muñoz Miranda, [orientador] Helder Takashi Imoto Nakaya. São Paulo : 2020. 58 p. -
A Jumping Profile HMM for Remote Protein Homology Detection
A Jumping Profile HMM for Remote Protein Homology Detection Anne-Kathrin Schultz and Mario Stanke Institut fur¨ Mikrobiologie und Genetik, Abteilung Bioinformatik, Universit¨at G¨ottingen, Germany contact: faschult2, [email protected] Abstract Our Generalization: Jumping Profile HMM We address the problem of finding new members of a given protein family in a database of protein sequences. We are given a MSA of k rows and a candidate sequence. At each position the candidate sequence is either Given a multiple sequence alignment (MSA) of the sequences in the protein family, we would like to score each aligned to the whole column of the MSA or to a certain reference sequence: We say that we are in the column candidate sequence in the database with respect to how likely it is that it belongs to the family. Successful mode or in a row mode of the HMM. methods for this task are profile Hidden Markov Models (HMM), like HMMER [Eddy, 1998] and SAM [Hughey • Column mode: (red part of Figure 1) and Krogh, 1996], and a so-called jumping alignment (JALI) [Spang et al., 2002]. As in a profile HMM each consensus column of the MSA is modeled by three states: match (M), insert (I) and delete (D).Match states model the distribution of residues in this column, they emit the amino acids We developed a Hidden Markov Model which can be regarded as a generalization of these two methods: At each with a probability which depends on all residues in this column. position the candidate sequence is either aligned to the whole column of the MSA or to a certain reference sequence. -
Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements
Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements A thesis presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial fulfillment of the requirements for the degree Master of Science Liang Chen August 2018 © 2018 Liang Chen. All Rights Reserved. 2 This thesis titled Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements by LIANG CHEN has been approved for the Department of Electrical Engineering and Computer Science and the Russ College of Engineering and Technology by Lonnie Welch Professor of Electrical Engineering and Computer Science Dennis Irwin Dean, Russ College of Engineering and Technology 3 Abstract CHEN, LIANG, M.S., August 2018, Computer Science Master Program Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements (106 pp.) Director of Thesis: Lonnie Welch Modern research on gene regulation and disorder-related pathways utilize the tools such as microarray and RNA-Seq to analyze the changes in the expression levels of large sets of genes. In silico motif discovery was performed based on the gene expression profile data, which generated a large set of candidate motifs (usually hundreds or thousands of motifs). How to pick a set of biologically meaningful motifs from the candidate motif set is a challenging biological and computational problem. As a computational problem it can be modeled as motif selection problem (MSP). Building solutions for motif selection problem will give biologists direct help in finding transcription factors (TF) that are strongly related to specific pathways and gaining insights of the relationships between genes. -
Dear Delegates,History of Productive Scientific Discussions of New Challenging Ideas and Participants Contributing from a Wide Range of Interdisciplinary fields
3rd IS CB S t u d ent Co u ncil S ymp os ium Welcome To The 3rd ISCB Student Council Symposium! Welcome to the Student Council Symposium 3 (SCS3) in Vienna. The ISCB Student Council's mis- sion is to develop the next generation of computa- tional biologists. We would like to thank and ac- knowledge our sponsors and the ISCB organisers for their crucial support. The SCS3 provides an ex- citing environment for active scientific discussions and the opportunity to learn vital soft skills for a successful scientific career. In addition, the SCS3 is the biggest international event targeted to students in the field of Computational Biology. We would like to thank our hosts and participants for making this event educative and fun at the same time. Student Council meetings have had a rich Dear Delegates,history of productive scientific discussions of new challenging ideas and participants contributing from a wide range of interdisciplinary fields. Such meet- We are very happy to welcomeings have you proved all touseful the in ISCBproviding Student students Council and postdocs Symposium innovative inputsin Vienna. and an Afterincreased the network suc- cessful symposiums at ECCBof potential 2005 collaborators. in Madrid and at ISMB 2006 in Fortaleza we are determined to con- tinue our efforts to provide an event for students and young researchers in the Computational Biology community. Like in previousWe ar yearse extremely our excitedintention to have is toyou crhereatee and an the opportunity vibrant city of Vforienna students welcomes to you meet to our their SCS3 event. peers from all over the world for exchange of ideas and networking. -
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. -
Bio::Graphics HOWTO Lincoln Stein Cold Spring Harbor Laboratory1 [email protected]
Bio::Graphics HOWTO Lincoln Stein Cold Spring Harbor Laboratory1 [email protected] This HOWTO describes how to render sequence data graphically in a horizontal map. It applies to a variety of situations ranging from rendering the feature table of a GenBank entry, to graphing the positions and scores of a BLAST search, to rendering a clone map. It describes the programmatic interface to the Bio::Graphics module, and discusses how to create dynamic web pages using Bio::DB::GFF and the gbrowse package. Table of Contents Introduction ...........................................................................................................................3 Preliminaries..........................................................................................................................3 Getting Started ......................................................................................................................3 Adding a Scale to the Image ...............................................................................................5 Improving the Image............................................................................................................7 Parsing Real BLAST Output...............................................................................................8 Rendering Features from a GenBank or EMBL File ....................................................12 A Better Version of the Feature Renderer ......................................................................14 Summary...............................................................................................................................18