The Pan-NLR'ome of Arabidopsis Thaliana
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120421-24Recombschedule FINAL.Xlsx
Friday 20 April 18:00 20:00 REGISTRATION OPENS in Fira Palace 20:00 21:30 WELCOME RECEPTION in CaixaForum (access map) Saturday 21 April 8:00 8:50 REGISTRATION 8:50 9:00 Opening Remarks (Roderic GUIGÓ and Benny CHOR) Session 1. Chair: Roderic GUIGÓ (CRG, Barcelona ES) 9:00 10:00 Richard DURBIN The Wellcome Trust Sanger Institute, Hinxton UK "Computational analysis of population genome sequencing data" 10:00 10:20 44 Yaw-Ling Lin, Charles Ward and Steven Skiena Synthetic Sequence Design for Signal Location Search 10:20 10:40 62 Kai Song, Jie Ren, Zhiyuan Zhai, Xuemei Liu, Minghua Deng and Fengzhu Sun Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads 10:40 11:00 178 Yang Li, Hong-Mei Li, Paul Burns, Mark Borodovsky, Gene Robinson and Jian Ma TrueSight: Self-training Algorithm for Splice Junction Detection using RNA-seq 11:00 11:30 coffee break Session 2. Chair: Bonnie BERGER (MIT, Cambrige US) 11:30 11:50 139 Son Pham, Dmitry Antipov, Alexander Sirotkin, Glenn Tesler, Pavel Pevzner and Max Alekseyev PATH-SETS: A Novel Approach for Comprehensive Utilization of Mate-Pairs in Genome Assembly 11:50 12:10 171 Yan Huang, Yin Hu and Jinze Liu A Robust Method for Transcript Quantification with RNA-seq Data 12:10 12:30 120 Zhanyong Wang, Farhad Hormozdiari, Wen-Yun Yang, Eran Halperin and Eleazar Eskin CNVeM: Copy Number Variation detection Using Uncertainty of Read Mapping 12:30 12:50 205 Dmitri Pervouchine Evidence for widespread association of mammalian splicing and conserved long range RNA structures 12:50 13:10 169 Melissa Gymrek, David Golan, Saharon Rosset and Yaniv Erlich lobSTR: A Novel Pipeline for Short Tandem Repeats Profiling in Personal Genomes 13:10 13:30 217 Rory Stark Differential oestrogen receptor binding is associated with clinical outcome in breast cancer 13:30 15:00 lunch break Session 3. -
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. -
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. -
UC San Diego UC San Diego Electronic Theses and Dissertations
UC San Diego UC San Diego Electronic Theses and Dissertations Title Analysis and applications of conserved sequence patterns in proteins Permalink https://escholarship.org/uc/item/5kh9z3fr Author Ie, Tze Way Eugene Publication Date 2007 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Analysis and Applications of Conserved Sequence Patterns in Proteins A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science by Tze Way Eugene Ie Committee in charge: Professor Yoav Freund, Chair Professor Sanjoy Dasgupta Professor Charles Elkan Professor Terry Gaasterland Professor Philip Papadopoulos Professor Pavel Pevzner 2007 Copyright Tze Way Eugene Ie, 2007 All rights reserved. The dissertation of Tze Way Eugene Ie is ap- proved, and it is acceptable in quality and form for publication on microfilm: Chair University of California, San Diego 2007 iii DEDICATION This dissertation is dedicated in memory of my beloved father, Ie It Sim (1951–1997). iv TABLE OF CONTENTS Signature Page . iii Dedication . iv Table of Contents . v List of Figures . viii List of Tables . x Acknowledgements . xi Vita, Publications, and Fields of Study . xiii Abstract of the Dissertation . xv 1 Introduction . 1 1.1 Protein Homology Search . 1 1.2 Sequence Comparison Methods . 2 1.3 Statistical Analysis of Protein Motifs . 4 1.4 Motif Finding using Random Projections . 5 1.5 Microbial Gene Finding without Assembly . 7 2 Multi-Class Protein Classification using Adaptive Codes . 9 2.1 Profile-based Protein Classifiers . 14 2.2 Embedding Base Classifiers in Code Space . -
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 -
UCLA UCLA Electronic Theses and Dissertations
UCLA UCLA Electronic Theses and Dissertations Title Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods Permalink https://escholarship.org/uc/item/0tr9j01r Author Sun, Yidan Publication Date 2021 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics by Yidan Sun 2021 © Copyright by Yidan Sun 2021 ABSTRACT OF THE DISSERTATION Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods by Yidan Sun Doctor of Philosophy in Statistics University of California, Los Angeles, 2021 Professor Jingyi Li, Chair In a bipartite network, nodes are divided into two types, and edges are only allowed to connect nodes of different types. Bipartite network clustering problems aim to identify node groups with more edges between themselves and fewer edges to the rest of the network. The approaches for community detection in the bipartite network can roughly be classified into algorithmic and model-based methods. The algorithmic methods solve the problem either by greedy searches in a heuristic way or optimizing based on some criteria over all possible partitions. The model-based methods fit a generative model to the observed data and study the model in a statistically principled way. In this dissertation, we mainly focus on bipartite clustering under two scenarios: incorporation of node covariates and detection of mixed membership communities. -
Stein Gives Bioinformatics Ten Years to Live
O'Reilly Network: Stein Gives Bioinformatics Ten Years to Live http://www.oreillynet.com/lpt/a/3205 Published on O'Reilly Network (http://www.oreillynet.com/) http://www.oreillynet.com/pub/a/network/biocon2003/stein.html See this if you're having trouble printing code examples Stein Gives Bioinformatics Ten Years to Live by Daniel H. Steinberg 02/05/2003 Lincoln Stein's keynote at the O'Reilly Bioinformatics Technology Conference was provocatively titled "Bioinformatics: Gone in 2012." Despite the title, Stein is optimistic about the future for people doing bioinformatics. But he explained that "the field of bioinformatics will be gone by 2012. The field will be doing the same thing but it won't be considered a field." His address looked at what bioinformatics is and what its future is likely to be in the context of other scientific disciplines. He also looked at career prospects for people doing bioinformatics and provided advice for those looking to enter the field. What Is Bioinformatics: Take One Stein, of the Cold Spring Harbor Laboratory, began his keynote by examining what is meant by bioinformatics. In the past such a talk would begin with a definition displayed from an authoritative dictionary. The modern approach is to appeal to an FAQ from an authoritative web site. Take a look at the FAQ at bioinformatics.org and you'll find several definitions. Stein summarized Fedj Tekaia of the Institut Pasteur--that bioinformatics is DNA and protein analysis. Stein also summarized Richard Durbin of the Sanger Institute--that bioinformatics is managing data sets. Stein's first pass at a definition of bioinformatics is that it is "Biologists using computers or the other way around." He followed by observing that whatever it is, it's growing. -
Thesis Reference
Thesis Analysis of large biological data: metabolic network modularization and prediction of N-terminal acetylation CHARPILLOZ, Christophe Abstract During last decades, biotechnology advances allowed to gather a huge amount of biological data. This data ranges from genome composition to the chemical interactions occurring in the cell. Such huge amount of information requires the application of complex algorithms to reveal how they are organized in order to understand the underlying biology. The metabolism forms a class of very complex data and the graphs that represent it are composed of thousands of nodes and edges. In this thesis we propose an approach to modularize such networks to reveal their internal organization. We have analyzed red blood cells' networks corresponding to pathological states and the obtained in-silico results were corroborated by known in-vitro analysis. In the second part of the thesis we describe a learning method that analyzes thousands of sequences from the UniProt database to predict the N-alpha-terminal acetylation. This is done by automatically discovering discriminant motifs that are combined in a binary decision tree manner. Prediction performances on N-alpha-terminal acetylation are higher than the other published classifiers. Reference CHARPILLOZ, Christophe. Analysis of large biological data: metabolic network modularization and prediction of N-terminal acetylation. Thèse de doctorat : Univ. Genève, 2015, no. Sc. 4883 URN : urn:nbn:ch:unige-860463 DOI : 10.13097/archive-ouverte/unige:86046 Available -
Computational Modeling to Design and Analyze Synthetic Metabolic Circuits
Computational modeling S467 to design and SACL 9 analyze : 201 synthetic NNT metabolic circuits Thèse de doctorat de l'Université Paris-Saclay préparée à l’Université Paris -Sud École doctorale n°577 Structure et Dynamique des Systèmes Vivants (SDSV) Spécialité de doctorat: Sciences de la Vie et de la Santé Thèse présentée et soutenue à Jouy-en-Josas, le 28/11/19, par Mathilde Koch Composition du Jury : Joerg Stelling Professeur, ETH Zurich (Department of Biosystems Science and Engineering) Président et Rapporteur Daniel Kahn Directeur de Recherche, INRA (UAR 1203, Département Mathématiques et Informatique Appliquées) Rapporteur Gregory Batt Directeur de Recherche, INRIA Examinateur Olivier Sperandio Directeur de Recherche, Institut Pasteur Examinateur Wolfram Liebermeister Chargé de Recherche, INRA (UR1404, MaIAGE, Université Paris-Saclay, Jouy-en-Josas) Examinateur Jean-Loup Faulon Directeur de Recherche, INRA (UMR 1319, Micalis, AgroParisTech, Université Paris-Saclay) Directeur de thèse Abstract The aims of this thesis are two-fold, and centered on synthetic metabolic cir- cuits, which perform sensing and computation using enzymes. The first part consisted in developing reinforcement and active learning tools to improve the design of metabolic circuits and optimize biosensing and bioproduction. In order to do this, a novel algorithm (RetroPath3.0) based on similarity- guided Monte Carlo Tree Search to improve the exploration of the search space is presented. This algorithm, combined with data-derived reaction rules and varying levels of enzyme promiscuity, allows to focus exploration toward the most promising compounds and pathways for bio-retrosynthesis. As retrosynthesis-based pathways can be implemented in whole cell or cell- free systems, an active learning method to efficiently explore the combinato- rial space of components for rational buffer optimization was also developed, to design the best buffer maximizing cell-free productivity. -
An Introduction to Perl for Bioinformatics
An Introduction to Perl for bioinformatics C. Tranchant-Dubreuil, F. Sabot UMR DIADE, IRD June 2015 1 / 98 Sommaire 1 Introduction Objectives What is programming ? What is perl ? Why used Perl ? Perl in bioinformatics Running a perl script 2 Perl in detail 2 / 98 • To empower you with the basic knowledge required to quickly and effectively create simple scripts to process biological data • Write your own programs once time, use many times Objectives • To demonstrate how Perl can be used in bioinformatics 3 / 98 • Write your own programs once time, use many times Objectives • To demonstrate how Perl can be used in bioinformatics • To empower you with the basic knowledge required to quickly and effectively create simple scripts to process biological data 3 / 98 Objectives • To demonstrate how Perl can be used in bioinformatics • To empower you with the basic knowledge required to quickly and effectively create simple scripts to process biological data • Write your own programs once time, use many times 3 / 98 • Programming languages : bridges between human languages (A-Z) and machine languages (0&1) • Compilers convert programming languages to machine languages What is programming • Programs : a set of instructions telling computer what to do 4 / 98 • Compilers convert programming languages to machine languages What is programming • Programs : a set of instructions telling computer what to do • Programming languages : bridges between human languages (A-Z) and machine languages (0&1) 4 / 98 What is programming • Programs : a set of instructions -
The Bioperl Toolkit: Perl Modules for the Life Sciences
Resource The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich,1,18,19 David Block,2,18 Kris Boulez,3 Steven E. Brenner,4 Stephen A. Chervitz,# Chris Dagdigian,% Georg Fuellen,( James G.R. Gilbert,8 Ian Korf,9 Hilmar Lapp,10 Heikki Lehva1slaiho,11 Chad Matsalla,12 Chris J. Mungall,13 Brian I. Osborne,14 Matthew R. Pocock,8 Peter Schattner,15 Martin Senger,11 Lincoln D. Stein,16 Elia Stupka,17 Mark D. Wilkinson,2 and Ewan Birney11 1University Program in Genetics, Duke University, Durham, North Carolina 27710, USA; 2National Research Council of Canada, Plant Biotechnology Institute, Saskatoon, SK S7N &'( Canada; )AlgoNomics, # 9052 Gent, Belgium; +Department of Plant and Molecular Biology, University of California, Berkeley, California 94720, USA; *Affymetrix, Inc., Emeryville, California 94608, USA; 1Open Bioinformatics Foundation, Somerville, Massachusetts 02144, USA; 7Integrated Functional Genomics, IZKF, University Hospital Muenster, 48149 Muenster, Germany; 2The Welcome Trust Sanger Institute, Welcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA UK; (Department of Computer Science, Washington University, St. Louis, Missouri 63130, USA; 10Genomics Institute of the Novartis Research Foundation (GNF), San Diego, California 92121, USA; 11European Bioinformatics Institute, Welcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK; 12Agriculture and Agri-Food Canada, Saskatoon Research Centre, Saskatoon SK, S7N 0X2 Canada; 13Berkeley Drosophila Genome Project, University of California, Berkeley, California 94720, -
Identification of Functional Elements and Regulatory Circuits By
RESEARCH ARTICLES 78. Funding for this work came from the NHGRI of the Biology and Genetics. Raw microarray data are available fellow curator P. Davis for reviewing and hand-checking NIH as part of the modENCODE project, NIH (grant from the Gene Expression Omnibus archive, and raw the list of pseudogenes. R01GM088565), Muscular Dystrophy Association, and the sequencing data are available from the SRA archive Supporting Online Material Pew Charitable Trusts (J.K.K.); the Helmholtz-Alliance on (accessions are in table S18). We appreciate help from www.sciencemag.org/cgi/content/science.1196914/DC1 Systems Biology (Max Delbrück Centrum Systems S. Anthony, K. Bell, C. Davis, C. Dieterich, Y. Field, Materials and Methods Biology Network) (S.D.M.); the Wellcome Trust (J.A.); A.S.Hammonds,J.Jo,N.Kaplan,A.Manrai,B.Mathey-Prevot, Figs. S1 to S50 the William H. Gates III Endowed Chair of Biomedical R. McWhirter, S. Mohr, S. Von Stetina, J. Watson, Tables S1 to S18 Sciences (R.H.W.); and the A. L. Williams Professorship K. Watkins, C. Xue, and Y. Zhang, and B. Carpenter. We References (M.B.G.). M. Snyder has an advisory role with DNANexus, thank C. Jan and D. Bartel for sharing data on poly(A) a DNA sequence storage and analysis company. Transfer sites before publication, WormBase curator G. Williams 24 August 2010; accepted 18 November 2010 of GFP-tagged fosmids requires a Materials Transfer for assistance in quality checking and preparing the Published online 22 December 2010; Agreement with the Max Planck Institute of Molecular Cell transcriptomics data sets for publication, as well as his 10.1126/science.1196914 biology.