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Current Topics in Computational Molecular Biology Edited by Tao Jiang Ying Xu Michael Q. Zhang a Bradford Book the MIT Press
Current Topics in Computational Molecular Biology edited by Tao Jiang Ying Xu Michael Q. Zhang A Bradford Book The MIT Press Cambridge, Massachusetts London, England Contents Preface vii I INTRODUCTION 1 1 The Challenges Facing Genomic Informatics 3 Temple F. Smith H COMPARATIVE SEQUENCE AND GENOME ANALYSIS 9 2 Bayesian Modeling and Computation in Bioinformatics Research 11 Jun S. Liu 3 Bio-Sequence Comparison and Applications 45 Xiaoqiu Huang 4 Algorithmic Methods for Multiple Sequence Alignment 71 Tao Jiang and Lusheng Wang 5 Phylogenetics and the Quartet Method 111 Paul Kearney 6 Genome Rearrangement 135 David Sankoff and Nadia El-Mabrouk 7 Compressing DNA Sequences 157 Ming Li HI DATA MINING AND PATTERN DISCOVERY 173 8 Linkage Analysis of Quantitative Traits 175 Shizhong Xu , 9 Finding Genes by Computer: Probabilistic and Discriminative Approaches 201 Victor V. Solovyev 10 Computational Methods for Promoter Recognition 249 Michael Q. Zhang 11 Algorithmic Approaches to Clustering Gene Expression Data 269 Ron Shamir and Roded Sharan 12 KEGG for Computational Genomics 301 Minoru Kanehisa and Susumu Goto vi Contents 13 Datamining: Discovering Information from Bio-Data 317 Limsoon Wong IV COMPUTATIONAL STRUCTURAL BIOLOGY 343 14 RNA Secondary Structure Prediction 345 Zhuozhi Wang and Kaizhong Zhang 15 Properties and Prediction of Protein Secondary Structure 365 Victor V. Solovyev and Ilya N. Shindyalov 16 Computational Methods for Protein Folding: Scaling a Hierarchy of Complexities 403 Hue Sun Chan, Huseyin Kaya, and Seishi Shimizu 17 Protein Structure Prediction by Comparison: Homology-Based Modeling 449 Manuel C. Peitsch, Torsten Schwede, Alexander Diemand, and Nicolas Guex 18 Protein Structure Prediction by Protein Threading and Partial Experimental Data 467 Ying Xu and Dong Xu 19 Computational Methods for Docking and Applications to Drug Design: Functional Epitopes and Combinatorial Libraries 503 Ruth Nussinov, Buyong Ma, and Haim J. -
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. -
Bonnie Berger Named ISCB 2019 ISCB Accomplishments by a Senior
F1000Research 2019, 8(ISCB Comm J):721 Last updated: 09 APR 2020 EDITORIAL Bonnie Berger named ISCB 2019 ISCB Accomplishments by a Senior Scientist Award recipient [version 1; peer review: not peer reviewed] Diane Kovats 1, Ron Shamir1,2, Christiana Fogg3 1International Society for Computational Biology, Leesburg, VA, USA 2Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel 3Freelance Writer, Kensington, USA First published: 23 May 2019, 8(ISCB Comm J):721 ( Not Peer Reviewed v1 https://doi.org/10.12688/f1000research.19219.1) Latest published: 23 May 2019, 8(ISCB Comm J):721 ( This article is an Editorial and has not been subject https://doi.org/10.12688/f1000research.19219.1) to external peer review. Abstract Any comments on the article can be found at the The International Society for Computational Biology (ISCB) honors a leader in the fields of computational biology and bioinformatics each year with the end of the article. Accomplishments by a Senior Scientist Award. This award is the highest honor conferred by ISCB to a scientist who is recognized for significant research, education, and service contributions. Bonnie Berger, Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) is the 2019 recipient of the Accomplishments by a Senior Scientist Award. She is receiving her award and presenting a keynote address at the 2019 Joint International Conference on Intelligent Systems for Molecular Biology/European Conference on Computational Biology in Basel, Switzerland on July 21-25, 2019. Keywords ISCB, Bonnie Berger, Award This article is included in the International Society for Computational Biology Community Journal gateway. -
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 . -
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. -
Computational Biology and Bioinformatics
Vol. 30 ISMB 2014, pages i1–i2 BIOINFORMATICS EDITORIAL doi:10.1093/bioinformatics/btu304 Editorial This special issue of Bioinformatics serves as the proceedings of The conference used a two-tier review system, a continuation the 22nd annual meeting of Intelligent Systems for Molecular and refinement of a process begun with ISMB 2013 in an effort Biology (ISMB), which took place in Boston, MA, July 11–15, to better ensure thorough and fair reviewing. Under the revised 2014 (http://www.iscb.org/ismbeccb2014). The official confer- process, each of the 191 submissions was first reviewed by at least ence of the International Society for Computational Biology three expert referees, with a subset receiving between four and (http://www.iscb.org/), ISMB, was accompanied by 12 Special eight reviews, as needed. These formal reviews were frequently Interest Group meetings of one or two days each, two satellite supplemented by online discussion among reviewers and Area meetings, a High School Teachers Workshop and two half-day Chairs to resolve points of dispute and reach a consensus on tutorials. Since its inception, ISMB has grown to be the largest each paper. Among the 191 submissions, 29 were conditionally international conference in computational biology and bioinfor- accepted for publication directly from the first round review Downloaded from matics. It is expected to be the premiere forum in the field for based on an assessment of the reviewers that the paper was presenting new research results, disseminating methods and tech- clearly above par for the conference. A subset of 16 papers niques and facilitating discussions among leading researchers, were viewed as potentially in the top tier but raised significant practitioners and students in the field. -
ISMB 99 August 6 – 10, 1999 Heidelberg, Germany the Seventh
______________________________________ Welcome to ISMB 99 August 6 – 10, 1999 Heidelberg, Germany The Seventh International Conference on Intelligent Systems for Molecular Biology ______________________________________ Final Program and Detailed Schedule Friday, August 6, 1999 Tutorial Day The tutorials will take place in the following rooms: 8:30 – 12:30 (Coffee break around 10:30) Tutorial #1 Trübnersaal Piere Baldi Probabilistic graphical models Tutorial #2 Robert-Schumann-Zimmer Douglas L. Brutlag Bioinformatics and Molecular Biology Tutorial #3 Ballsaal Martin Reese The challenge of annotating a complete eukaryotic genome: A case study in Drosophila melanogaster Tutorial #4 Gustav-Mahler-Zimmer Tandy Warnow Computational and statistical Junhyong Kim challenges involved in reconstructing evolutionary trees Tutorial #5 Sebastian-Münster-Saal Thomas Werner The biology and bioinformatics of regulatory regions in genomes Lunch (on this day served in "Grosser Saal" on the ground floor) 13:30 – 17:30 (Coffee break around 15:30) Tutorial #6 Sebastian-Münster-Saal Rob Miller EST Clustering Alan Christoffels Winston Hide Tutorial #7 Trübnersaal Kevin Karplus Getting the most out of hidden Markov Melissa Cline models Christian Barrett Tutorial #8 Robert-Schumann-Zimmer Arthur Lesk Sequence-structure relationships and evolutionary structure changes in proteins Tutorial #9 Gustav-Mahler-Zimmer David States PERL abstractions for databases and Brian Dunford distributed computing Shore Tutorial # 10 Ballsaal Zoltan Szallasi Genetic network analysis -
Daniel Aalberts Scott Aa
PLOS Computational Biology would like to thank all those who reviewed on behalf of the journal in 2015: Daniel Aalberts Jeff Alstott Benjamin Audit Scott Aaronson Christian Althaus Charles Auffray Henry Abarbanel Benjamin Althouse Jean-Christophe Augustin James Abbas Russ Altman Robert Austin Craig Abbey Eduardo Altmann Bruno Averbeck Hermann Aberle Philipp Altrock Ferhat Ay Robert Abramovitch Vikram Alva Nihat Ay Josep Abril Francisco Alvarez-Leefmans Francisco Azuaje Luigi Acerbi Rommie Amaro Marc Baaden Orlando Acevedo Ettore Ambrosini M. Madan Babu Christoph Adami Bagrat Amirikian Mohan Babu Frederick Adler Uri Amit Marco Bacci Boris Adryan Alexander Anderson Stephen Baccus Tinri Aegerter-Wilmsen Noemi Andor Omar Bagasra Vera Afreixo Isabelle Andre Marc Baguelin Ashutosh Agarwal R. David Andrew Timothy Bailey Ira Agrawal Steven Andrews Wyeth Bair Jacobo Aguirre Ioan Andricioaei Chris Bakal Alaa Ahmed Ioannis Androulakis Joseph Bak-Coleman Hasan Ahmed Iris Antes Adam Baker Natalie Ahn Maciek Antoniewicz Douglas Bakkum Thomas Akam Haroon Anwar Gabor Balazsi Ilya Akberdin Stefano Anzellotti Nilesh Banavali Eyal Akiva Miguel Aon Rahul Banerjee Sahar Akram Lucy Aplin Edward Banigan Tomas Alarcon Kevin Aquino Martin Banks Larissa Albantakis Leonardo Arbiza Mukul Bansal Reka Albert Murat Arcak Shweta Bansal Martí Aldea Gil Ariel Wolfgang Banzhaf Bree Aldridge Nimalan Arinaminpathy Lei Bao Helen Alexander Jeffrey Arle Gyorgy Barabas Alexander Alexeev Alain Arneodo Omri Barak Leonidas Alexopoulos Markus Arnoldini Matteo Barberis Emil Alexov -
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. -
Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by Pickeringhutchins Ltd
European Bioinformatics Institute EMBL-EBI Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by PickeringHutchins Ltd www.pickeringhutchins.com EMBL member states: Austria, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom. Associate member state: Australia EMBL-EBI is a part of the European Molecular Biology Laboratory (EMBL) EMBL-EBI EMBL-EBI EMBL-EBI EMBL-European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton Cambridge CB10 1SD United Kingdom Tel. +44 (0)1223 494 444, Fax +44 (0)1223 494 468 www.ebi.ac.uk EMBL Heidelberg Meyerhofstraße 1 69117 Heidelberg Germany Tel. +49 (0)6221 3870, Fax +49 (0)6221 387 8306 www.embl.org [email protected] EMBL Grenoble 6, rue Jules Horowitz, BP181 38042 Grenoble, Cedex 9 France Tel. +33 (0)476 20 7269, Fax +33 (0)476 20 2199 EMBL Hamburg c/o DESY Notkestraße 85 22603 Hamburg Germany Tel. +49 (0)4089 902 110, Fax +49 (0)4089 902 149 EMBL Monterotondo Adriano Buzzati-Traverso Campus Via Ramarini, 32 00015 Monterotondo (Rome) Italy Tel. +39 (0)6900 91402, Fax +39 (0)6900 91406 © 2012 EMBL-European Bioinformatics Institute All texts written by EBI-EMBL Group and Team Leaders. This publication was produced by the EBI’s Outreach and Training Programme. Contents Introduction Foreword 2 Major Achievements 2011 4 Services Rolf Apweiler and Ewan Birney: Protein and nucleotide data 10 Guy Cochrane: The European Nucleotide Archive 14 Paul Flicek: -
Parallelization of Dynamic Programming Recurrences in Computational Biology Arpith Jacob Washington University in St
Washington University in St. Louis Washington University Open Scholarship All Theses and Dissertations (ETDs) January 2010 Parallelization of dynamic programming recurrences in computational biology Arpith Jacob Washington University in St. Louis Follow this and additional works at: https://openscholarship.wustl.edu/etd Recommended Citation Jacob, Arpith, "Parallelization of dynamic programming recurrences in computational biology" (2010). All Theses and Dissertations (ETDs). 169. https://openscholarship.wustl.edu/etd/169 This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact [email protected]. WASHINGTON UNIVERSITY IN ST. LOUIS School of Engineering and Applied Science Department of Computer Science and Engineering Thesis Examination Committee: Jeremy Buhler, Chair Michael Brent Ron Cytron Mark Franklin Robert Morley Bill Smart David Taylor PARALLELIZATION OF DYNAMIC PROGRAMMING RECURRENCES IN COMPUTATIONAL BIOLOGY by Arpith Chacko Jacob A dissertation presented to the Graduate School of Arts and Sciences of Washington University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY December 2010 Saint Louis, Missouri copyright by Arpith Chacko Jacob 2010 ABSTRACT OF THE THESIS Parallelization of dynamic programming recurrences in computational biology by Arpith Chacko Jacob Doctor of Philosophy in Computer Science Washington University in St. Louis, 2010 Research Advisor: Professor Jeremy Buhler The rapid growth of biosequence databases over the last decade has led to a per- formance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. -
PLOS Computational Biology Associate Editor Handbook Second Edition Version 2.6
PLOS Computational Biology Associate Editor Handbook Second Edition Version 2.6 Welcome and thank you for your support of PLOS Computational Biology and the Open Access movement. PLOS Computational Biology is one of the four community run, Open Access journals published by the Public Library of Science. The journal features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Since its launch in 2005, PLOS Computational Biology has grown not only in the number of papers published, but also in recognition as a quality publication and community resource. We have every reason to expect that this trend will continue and our team of Associate Editors play an invaluable role in this process. Associate Editors oversee the peer review process for the journal, including evaluating submissions, selecting reviewers, assessing reviewer comments, and making editorial decisions. Together with fellow Editorial Board members and the PLOS CB Team, Associate Editors uphold journal policies and ethical standards and work to promote the PLOS mission to provide free public access to scientific research. PLOS Computational Biology is one of a suite of influential journals published by PLOS. Information about the other journals, the PLOS business model, PLOS innovations in scientific publishing, and Open Access copyright and licensure can be found in Appendix IX. Table of Contents PLOS Computational Biology Editorial Board Membership ................................................................................... 2 Manuscript Evaluation .....................................................................................................................................