ISMB 99 August 6 – 10, 1999 Heidelberg, Germany the Seventh
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Proquest Dissertations
Automated learning of protein involvement in pathogenesis using integrated queries Eithon Cadag A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2009 Program Authorized to Offer Degree: Department of Medical Education and Biomedical Informatics UMI Number: 3394276 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI Dissertation Publishing UMI 3394276 Copyright 2010 by ProQuest LLC. All rights reserved. This edition of the work is protected against unauthorized copying under Title 17, United States Code. uest ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 University of Washington Graduate School This is to certify that I have examined this copy of a doctoral dissertation by Eithon Cadag and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. Chair of the Supervisory Committee: Reading Committee: (SjLt KJ. £U*t~ Peter Tgffczy-Hornoch In presenting this dissertation in partial fulfillment of the requirements for the doctoral degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholarly purposes, consistent with "fair use" as prescribed in the U.S. -
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. -
Trancep: Predicting Transmembrane Transport Proteins Using Composition, Evolutionary, and Positional Information
bioRxiv preprint doi: https://doi.org/10.1101/293159; this version posted April 2, 2018. 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. TranCEP: Predicting transmembrane transport proteins using composition, evolutionary, and positional information Munira Alballa1, Faizah Aplop2, Gregory Butler1,3* 1 Department of Computer Science and Software Engineering, Concordia University, Montr´eal, Qu´ebec, Canada 2 School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia 3 Centre for Structural and Functional Genomics, Concordia University, Montr´eal,Qu´ebec, Canada * [email protected] Abstract Transporters mediate the movement of compounds across the membranes that separate the cell from its environment, and across inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computation tools that predict the substrates which are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment, and restriction to important positions of the alignment that play a role in determining specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state of the art. -
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. -
Department of Energy Office of Health and Environmental Research SEQUENCING the HUMAN GENOME Summary Report of the Santa Fe Workshop March 3-4, 1986
Department of Energy Office of Health and Environmental Research SEQUENCING THE HUMAN GENOME Summary Report of the Santa Fe Workshop March 3-4, 1986 Los Alamos National Laboratory Los Alamos Los Alamos, New Mexico 87545 Los Alamos National Laboratory is operated by the University of California for the United States Department of Energy under contract W-7405-ENG-36. DEPARTMENT OF ENERGY OFFICE OF HEALTH AND ENVIRONMENTAL RESEARCH SEQUENCING THE HUMAN GENOME SUMMARY REPORT ON THE SANTA FE WORKSHOP (MARCH 3-4, 1986) Executive Summary. The following is a summary of the Santa Fe Workshop held on March 3 and 4, 1986. The workshop was sponsored by the Office of Health and Environmental Research (OHER) and Los Alamos National Laboratory (LANL) and dedicated to examining the feasibility, advisability, and approaches to sequencing the human genome. The workshop considered four principal topics: I. Technologies to be employed. II. Expected benefits. III. Architecture of the enterprise. IV. Participants and funding. I . Technology The participants of the workshop foresaw extraordinary and continuing progress in the efficiency and accuracy of mapping, ordering , and sequencing technologies. They suggested that a coordinated analysis of the human genome begin with the task of ordering overlapping recombinant DNA fragments obtained from purified human chromosomes that would provide an infrastructure for sequencing activity. At the same time, they support in-depth evaluation of current and developing strategies for sequencing including possible applications of automation and robotics that would minimize the time and cost of sequencing. II. Benefits The socio-political and health benefits, and the benefit:cost ratio were seen as highly favorable not only for human health, but in addition for the development of new diagnostic, preventative and therapeutic tools, jobs, and industries. -
Mapping Our Genes—Genome Projects: How Big? How Fast?
Mapping Our Genes—Genome Projects: How Big? How Fast? April 1988 NTIS order #PB88-212402 Recommended Citation: U.S. Congress, Office of Technology Assessment, Mapping Our Genes-The Genmne Projects.’ How Big, How Fast? OTA-BA-373 (Washington, DC: U.S. Government Printing Office, April 1988). Library of Congress Catalog Card Number 87-619898 For sale by the Superintendent of Documents U.S. Government Printing Office, Washington, DC 20402-9325 (order form can be found in the back of this report) Foreword For the past 2 years, scientific and technical journals in biology and medicine have extensively covered a debate about whether and how to determine the function and order of human genes on human chromosomes and when to determine the sequence of molecular building blocks that comprise DNA in those chromosomes. In 1987, these issues rose to become part of the public agenda. The debate involves science, technol- ogy, and politics. Congress is responsible for ‘(writing the rules” of what various Federal agencies do and for funding their work. This report surveys the points made so far in the debate, focusing on those that most directly influence the policy options facing the U.S. Congress, The House Committee on Energy and Commerce requested that OTA undertake the project. The House Committee on Science, Space, and Technology, the Senate Com- mittee on Labor and Human Resources, and the Senate Committee on Energy and Natu- ral Resources also asked OTA to address specific points of concern to them. Congres- sional interest focused on several issues: ● how to assess the rationales for conducting human genome projects, ● how to fund human genome projects (at what level and through which mech- anisms), ● how to coordinate the scientific and technical programs of the several Federal agencies and private interests already supporting various genome projects, and ● how to strike a balance regarding the impact of genome projects on international scientific cooperation and international economic competition in biotechnology. -
Microblogging the ISMB: a New Approach to Conference Reporting
Message from ISCB Microblogging the ISMB: A New Approach to Conference Reporting Neil Saunders1*, Pedro Beltra˜o2, Lars Jensen3, Daniel Jurczak4, Roland Krause5, Michael Kuhn6, Shirley Wu7 1 School of Molecular and Microbial Sciences, University of Queensland, St. Lucia, Brisbane, Queensland, Australia, 2 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, United States of America, 3 Novo Nordisk Foundation Center for Protein Research, Panum Institute, Copenhagen, Denmark, 4 Department of Bioinformatics, University of Applied Sciences, Hagenberg, Freistadt, Austria, 5 Max-Planck-Institute for Molecular Genetics, Berlin, Germany, 6 European Molecular Biology Laboratory, Heidelberg, Germany, 7 Stanford Medical Informatics, Stanford University, Stanford, California, United States of America Cameron Neylon entitled FriendFeed for Claire Fraser-Liggett opened the meeting Scientists: What, Why, and How? (http:// with a review of metagenomics and an blog.openwetware.org/scienceintheopen/ introduction to the human microbiome 2008/06/12/friendfeed-for-scientists-what- project (http://friendfeed.com/search?q = why-and-how/) for an introduction. room%3Aismb-2008+microbiome+OR+ We—a group of science bloggers, most fraser). The subsequent Q&A session of whom met in person for the first time at covered many of the exciting challenges The International Conference on Intel- ISMB 2008—found FriendFeed a remark- for those working in this field. Clearly, ligent Systems for Molecular Biology -
Characterizing the Dna-Binding Site Specificities of Cis2his2 Zinc Fingers
MQP-ID-DH-UM1 C H A R A C T E RI Z IN G T H E DN A-BINDIN G SI T E SPE C I F I C I T I ES O F C IS2H IS2 Z IN C F IN G E RS A Major Qualifying Project Report Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degrees of Bachelor of Science in Biochemistry and Biology and Biotechnology by _________________________ Heather Bell April 26, 2012 APPROVED: ____________________ ____________________ ____________________ Scot Wolfe, PhD Destin Heilman, PhD David Adams, PhD Gene Function and Exp. Biochemistry Biology and Biotech UMass Medical School WPI Project Advisor WPI Project Advisor MAJOR ADVISOR A BST R A C T The ability to modularly assemble Zinc Finger Proteins (ZFPs) as well as the wide variety of DNA sequences they can recognize, make ZFPs an ideal framework to design novel DNA-binding proteins. However, due to the complexity of the interactions between residues in the ZF recognition helix and the DNA-binding site there is currently no comprehensive recognition code that would allow for the accurate prediction of the DNA ZFP binding motifs or the design of novel ZFPs for a desired target site. Through the analysis of the DNA-binding site specificities of 98 ZFP clones, determined through a bacterial one-hybrid selection system, a predictive model was created that can accurately predict the binding site motifs of novel ZFPs. 2 T A B L E O F C O N T E N TS Signature Page ««««««««««««««««««««««««««« $EVWUDFW«««««««««««««««««««««««««««««« 7DEOHRI&RQWHQWV«««««««««««««««««««««««««« $FNQRZOHGJHPHQWV««««««««««««««««««««««««« %DFNJURXQG«««««««««««««««««««««««««««« Project Purpose «««««««««««««««««««««««««««15 0HWKRGV««««««««««««««««««««««««««««««16 5HVXOWV««««««««««««««««««««««««««««««21 'LVFXVVLRQ«««««««««««««««««««««««««««««28 Bibliograph\«««««««««««««««««««««««««««« 6XSSOHPHQWDO««««««««««««««««««««««««««« 3 A C K N O W L E D G E M E N TS I would like to thank Dr. -
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Topics in Signal Processing: applications in genomics and genetics Abdulkadir Elmas Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2016 c 2016 Abdulkadir Elmas All Rights Reserved ABSTRACT Topics in Signal Processing: applications in genomics and genetics Abdulkadir Elmas The information in genomic or genetic data is influenced by various complex processes and appropriate mathematical modeling is required for studying the underlying processes and the data. This dissertation focuses on the formulation of mathematical models for certain problems in genomics and genetics studies and the development of algorithms for proposing efficient solutions. A Bayesian approach for the transcription factor (TF) motif discovery is examined and the extensions are proposed to deal with many interdependent parameters of the TF-DNA binding. The problem is described by statistical terms and a sequential Monte Carlo sampling method is employed for the estimation of unknown param- eters. In particular, a class-based resampling approach is applied for the accurate estimation of a set of intrinsic properties of the DNA binding sites. Through statistical analysis of the gene expressions, a motif-based computational approach is developed for the inference of novel regulatory networks in a given bacterial genome. To deal with high false-discovery rates in the genome-wide TF binding predictions, the discriminative learning approaches are examined in the context of sequence classification, and a novel mathematical model is introduced to the family of kernel-based Support Vector Machines classifiers. Furthermore, the problem of haplotype phasing is examined based on the genetic data obtained from cost-effective genotyping technologies. -
Baldi Bioinformatics
Vol. 15 no. 11 1999 BIOINFORMATICS Pages 865–866 A.M. Shmatkov, A.A. Melikyan, F.L. Chernousko and Editorial M. Borodovsky’s paper, ‘Finding prokaryotic genes by the “frame-by-frame” algorithm: targeting gene starts THE SECOND GEORGIA TECH INTERNA- and overlapping genes’, addresses the important practical TIONAL CONFERENCE ON BIOINFORMATICS: problem of gene recognition in prokaryotic genomes, SEQUENCE, STRUCTURE AND FUNCTION of which over a dozen have already been completely (NOVEMBER 11–14, 1999, ATLANTA, GEORGIA, sequenced. The authors suggest an approach to one of USA) the few remaining open problems in prokaryotic gene finding: accurate prediction of gene starts allowing for Steering & Program Committee: Pierre Baldi, Mark the possibility of overlapping protein-coding regions, a Borodovsky, Soren Brunak, Chris Burge, Jim Fickett, relatively common occurrence in prokaryotic genomes Steven Henikoff, Eugene Koonin, Andrej Sali, Chris which seems to be rare in eukaryotes. Their algorithm Sander, Gary Stormo involves application of a hidden Markov model of gene structure to each of the six global reading frames (three on This issue of Bioinformatics contains reports on selected each strand) of a genome separately, followed by a simple papers presented at the international conference in At- post-processing step to remove completely overlapping lanta. The conference was held at one of the midtown genes which rarely occur in nature. Promising results are hotels offering a magnificent bird’s-eye view to the obtained in identifying gene starts, and the possibility of cosmopolitan capital of the Southeast of the USA. The systematic biases in the annotation of several bacterial conference agenda included keynote lectures by Russell genomes is raised. -
Gene Regulatory Network Inference Using Machine Learning Techniques
GENE REGULATORY NETWORK INFERENCE USING MACHINE LEARNING TECHNIQUES Stephanie Kamgnia Wonkap A Thesis in the department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy(Computer Science) Concordia University Montreal,´ Quebec,´ Canada August 26 2020 c Stephanie Kamgnia Wonkap, 2020 Concordia University School of Graduate Studies This is to certify that the thesis prepared By: Miss. Stephanie Kamgnia Wonkap Entitled: Gene Regulatory Network Inference using Machine Learning Techniques and submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (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 committee: Chair Dr. Liangzhu Wang External Examiner Dr. Mathieu Blanchette Examiner Dr. Leila Kosseim Examiner Dr. Malcolm Whiteway Examiner Dr. Volker Haarslev Supervisor Dr. Gregory Butler Approved Dr. Leila Kosseim, Graduate Program Director August 26th, 2020 Dr. Amir Asif, Dean Date of Defence Faculty of Engineering and Computer Science Abstract Gene Regulatory Network Inference using Machine Learning Techniques Stephanie Kamgnia Wonkap, Ph.D. Concordia University, 2020 Systems Biology is a field that models complex biological systems in order to better understand the working of cells and organisms. One of the systems modeled is the gene regulatory network that plays the critical role of controlling an organism's response to changes in its environment. Ideally, we would like a model of the complete gene regulatory network. In recent years, several advances in technology have permitted the collection of an unprecedented amount and variety of data such as genomes, gene expression data, time-series data, and perturbation data. -
Highlights (PDF)
GENETICS A PERIODICAL RECORD OF INVESTIGATIONS BEARING ON HEREDITY AND VARIATION Founded in 1916 and published by The Genetics Society of America VOLUME 179, MAY–AUGUST 2008 GENETICS VOLUME 179, MAY–AUGUST 2008 EDITORIAL BOARD Elizabeth W. Jones, Editor-in-Chief Carnegie Mellon University Mark Johnston, Acting Editor-in-Chief Washington University School of Medicine Montserrat Aguade´ Kent Golic Rasmus Nielsen Universitat de Barcelona University of Utah University of Copenhagen, Centre for Bioinformatics Eric E. Alani Susan Gottesman Cornell University National Institutes of Health-NCI Michael Nonet Washington University School of Medicine Kathryn V. Anderson David I. Greenstein Sloan-Kettering Institute University of Minnesota Magnus Nordborg University of Southern California Brenda J. Andrews David Jonah Grunwald University of Utah University of Toronto Peter J. Oefner hris aley Robert R. H. Anholt C H Stanford University Roslin Institute (Edinburgh) North Carolina State University Andrew Paterson ichael ampsey Elja Arjas M H University of Georgia University of Helsinki Robert Wood Johnson Medical School-UMDNJ David Rand orman rnheim Brown University N A awrence arshman University of Southern California L G. H University of Nebraska, Lincoln Eric J. Richards onnie artel Washington University B B ancy ollingsworth Rice University N H Stony Brook University Mark D. Rose David Begun afri umayun Princeton University University of California, Davis M. Z H UMDNJ-New Jersey Medical School Paul Russell ames irchler J A. B ancy enkins The Scripps Research Institute University of Missouri N A. J National Cancer Institute-FCRDC Matthew S. Sachs arl roman K W. B homas aufman Texas A&M University University of Wisconsin, Madison T C.