High-Throughput Sequence Analysis with R and Bioconductor
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Bioconductor: Open Software Development for Computational Biology and Bioinformatics Robert C
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Collection Of Biostatistics Research Archive Bioconductor Project Bioconductor Project Working Papers Year 2004 Paper 1 Bioconductor: Open software development for computational biology and bioinformatics Robert C. Gentleman, Department of Biostatistical Sciences, Dana Farber Can- cer Institute Vincent J. Carey, Channing Laboratory, Brigham and Women’s Hospital Douglas J. Bates, Department of Statistics, University of Wisconsin, Madison Benjamin M. Bolstad, Division of Biostatistics, University of California, Berkeley Marcel Dettling, Seminar for Statistics, ETH, Zurich, CH Sandrine Dudoit, Division of Biostatistics, University of California, Berkeley Byron Ellis, Department of Statistics, Harvard University Laurent Gautier, Center for Biological Sequence Analysis, Technical University of Denmark, DK Yongchao Ge, Department of Biomathematical Sciences, Mount Sinai School of Medicine Jeff Gentry, Department of Biostatistical Sciences, Dana Farber Cancer Institute Kurt Hornik, Computational Statistics Group, Department of Statistics and Math- ematics, Wirtschaftsuniversitat¨ Wien, AT Torsten Hothorn, Institut fuer Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universitat Erlangen-Nurnberg, DE Wolfgang Huber, Department for Molecular Genome Analysis (B050), German Cancer Research Center, Heidelberg, DE Stefano Iacus, Department of Economics, University of Milan, IT Rafael Irizarry, Department of Biostatistics, Johns Hopkins University Friedrich Leisch, Institut fur¨ Statistik und Wahrscheinlichkeitstheorie, Technische Universitat¨ Wien, AT Cheng Li, Department of Biostatistical Sciences, Dana Farber Cancer Institute Martin Maechler, Seminar for Statistics, ETH, Zurich, CH Anthony J. Rossini, Department of Medical Education and Biomedical Informat- ics, University of Washington Guenther Sawitzki, Statistisches Labor, Institut fuer Angewandte Mathematik, DE Colin Smith, Department of Molecular Biology, The Scripps Research Institute, San Diego Gordon K. -
Statistical Computing with Pathway Tools Using Rcyc
Statistical Computing with Pathway Tools using RCyc Statistical Computing with Pathway Tools using RCyc Tomer Altman [email protected] Biomedical Informatics, Stanford University Statistical Computing with Pathway Tools using RCyc R & BioConductor S: software community over 30 years of statistical computing, data mining, machine learning, and data visualization knowledge R: open-source S with a lazy Scheme interpreter at its heart (including closures, symbols, and even macros!) RCyc: an R package to allow the interaction between Pathway / Genome Databases and the wealth of biostatistics software in the R community Statistical Computing with Pathway Tools using RCyc BioConductor Figure: BioConductor: Thousands of peer-reviewed biostatistics packages. Statistical Computing with Pathway Tools using RCyc Software`R'-chitecture C code extension to R to allow Unix socket access Common Lisp code to hack in XML-based communication Make the life of *Cyc API developers easier. Currently supports exchange of numbers, strings, and lists R code and documentation Provides utilities for starting PTools and marshaling data types Assumes user is familiar with the PTools API: http://bioinformatics.ai.sri.com/ptools/api/ All wrapped up in R package Easily installs via standard command-line R interface Statistical Computing with Pathway Tools using RCyc Simple Example callPToolsFn("so",list("'meta")) callPToolsFn("get-slot-value",list("'proton", "'common-name")) callPToolsFn("get-class-all-instances",list("'|Reactions|")) Statistical Computing with Pathway Tools using RCyc Availability http://github.com/taltman/RCyc Linked from PTools website Statistical Computing with Pathway Tools using RCyc Next Steps Dynamic instantiation of API functions in R Coming next release (coordination with BRG) Make development of *Cyc APIs easier, less boilerplate code Frame to Object import/export Provide \RCelot" functionality to slurp Ocelot frames directly into R S4 reference objects for direct data access Support for more exotic data types Symbols, hash tables, arrays, structures, etc. -
Bioconductor: Open Software Development for Computational Biology and Bioinformatics Robert C
Bioconductor Project Bioconductor Project Working Papers Year 2004 Paper 1 Bioconductor: Open software development for computational biology and bioinformatics Robert C. Gentleman, Department of Biostatistical Sciences, Dana Farber Can- cer Institute Vincent J. Carey, Channing Laboratory, Brigham and Women’s Hospital Douglas J. Bates, Department of Statistics, University of Wisconsin, Madison Benjamin M. Bolstad, Division of Biostatistics, University of California, Berkeley Marcel Dettling, Seminar for Statistics, ETH, Zurich, CH Sandrine Dudoit, Division of Biostatistics, University of California, Berkeley Byron Ellis, Department of Statistics, Harvard University Laurent Gautier, Center for Biological Sequence Analysis, Technical University of Denmark, DK Yongchao Ge, Department of Biomathematical Sciences, Mount Sinai School of Medicine Jeff Gentry, Department of Biostatistical Sciences, Dana Farber Cancer Institute Kurt Hornik, Computational Statistics Group, Department of Statistics and Math- ematics, Wirtschaftsuniversitat¨ Wien, AT Torsten Hothorn, Institut fuer Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universitat Erlangen-Nurnberg, DE Wolfgang Huber, Department for Molecular Genome Analysis (B050), German Cancer Research Center, Heidelberg, DE Stefano Iacus, Department of Economics, University of Milan, IT Rafael Irizarry, Department of Biostatistics, Johns Hopkins University Friedrich Leisch, Institut fur¨ Statistik und Wahrscheinlichkeitstheorie, Technische Universitat¨ Wien, AT Cheng Li, Department -
Decode™ Bioinformatic Analysis
PROTOCOL Decode™ Bioinformatic Analysis An introductory guide for the use of high-throughput sequencing to determine primary hits from an shRNA pooled screen. Table of contents 1. Legal disclaimers 1. Legal disclaimers 1 SOFTWARE LICENSE TERMS. With respect to any software products 2. Summary 2 incorporated in or forming a part of the software provided or described 3. Intended audience 2 hereunder (“Software”), Horizon Discovery (“Seller”) and you, the purchaser, 4. Software requirements 2 licensee and/or end-user of the software (“Buyer”) intend and agree that 5. FASTQ files from high-throughput sequencing 2 such software products are being licensed and not sold, and that the words 6. Align the FASTQ files to reference shRNA FASTA files 3 “purchase”, “sell” or similar or derivative words are understood and agreed to A. Create a Bowtie index 3 mean “license”, and that the word “Buyer” or similar or derivative words are B. Align using Bowtie 3 understood and agreed to mean “licensee”. Notwithstanding anything to the C. Batch process 4 contrary contained herein, Seller or its licensor, as the case may be, retains all D. Alignment summaries 4 rights and interest in software products provided hereunder. 7. Differential expression analysis 5 A. Convert Bowtie files into DESeq input 5 Seller hereby grants to Buyer a royalty-free, non-exclusive, nontransferable B. Run DESeq on .rtable files 7 license, without power to sublicense, to use software provided hereunder 8. Conclusions 9 solely for Buyer’s own internal business purposes and to use the related documentation solely for Buyer’s own internal business purposes. -
R / Bioconductor for High-Throughput Sequence Analysis
R / Bioconductor for High-Throughput Sequence Analysis Nicolas Delhomme1 21 October - 26 October, 2013 [email protected] Contents 1 Day2 of the workshop2 1.1 Introduction............................................2 1.2 Main Bioconductor packages of interest for the day......................2 1.3 A word on High-throughput sequence analysis.........................2 1.4 A word on Integrated Development Environment (IDE)...................2 1.5 Today's schedule.........................................2 2 Prelude 4 2.1 Purpose..............................................4 2.2 Creating GAlignment objects from BAM files.........................4 2.3 Processing the files in parallel..................................4 2.4 Processing the files one chunk at a time............................5 2.5 Pros and cons of the current solution..............................6 2.5.1 Pros............................................6 2.5.2 Cons............................................6 3 Sequences and Short Reads7 3.1 Alignments and Bioconductor packages............................7 3.1.1 The pasilla data set...................................7 3.1.2 Alignments and the ShortRead package........................8 3.1.3 Alignments and the Rsamtools package........................9 3.1.4 Alignments and other Bioconductor packages..................... 13 3.1.5 Resources......................................... 17 4 Interlude 18 5 Estimating Expression over Genes and Exons 20 5.1 Counting reads over known genes and exons.......................... 20 5.1.1 -
Open-Source Statistical Software for the Analysis of Microarray Data
The Bioconductor Project: Open-source Statistical Software for the Analysis of Microarray Data Sandrine Dudoit Division of Biostatistics University of California, Berkeley www.stat.berkeley.edu/~sandrine EMBO Practical Course on Analysis and Informatics of Microarray Data Wellcome Trust Genome Campus, Hinxton, Cambridge, UK March 18, 2003 © Copyright 2003, all rights reserved Materials from Bioconductor short courses developed with Robert Gentleman, Rafael Irizarry. Expanded version of this course: Fred Hutchinson Cancer Research Center, December 2002 Biological question Experimental design Microarray experiment Image analysis Expression quantification Pre-processing A Normalization n a l Testing Estimation Clustering Prediction y s Biological verification i and interpretation s Statistical computing Everywhere … • Statistical design and analysis: – image analysis, normalization, estimation, testing, clustering, prediction, etc. • Integration of experimental data with biological metadata from WWW-resources – gene annotation (GenBank, LocusLink); – literature (PubMed); – graphical (pathways, chromosome maps). Outline • Overview of the Bioconductor project • Annotation • Visualization • Pre-processing: spotted and Affy arrays • Differential gene expression • Clustering and classification Acknowledgments • Bioconductor core team • Vince Carey, Biostatistics, Harvard • Yongchao Ge, Statistics, UC Berkeley • Robert Gentleman, Biostatistics, Harvard • Jeff Gentry, Dana-Farber Cancer Institute • Rafael Irizarry, Biostatistics, Johns Hopkins -
Bioinformatics for High-Throughput Sequencing an Overview
Bioinformatics for High-throughput Sequencing An Overview Simon Anders Nicolas Delhomme EBI is an Outstation of the European Molecular Biology Laboratory. Overview In recent years, new sequencing schemes, also called • high-throughput sequencing • massively parallel sequencing • flow-cell sequencing have been proposed. Commercially available are devices from • Roche (formerly: 454) • Illumina (formerly: Solexa): “GenomeAnalyzer” • Applied Biosystems: “SOLiD system” • Helicos: “Helicoscope” Core ideas Two core differences of HTS to Sanger capillary sequencing: • The library is not constructed by cloning, but by a novel way of doing PCR, where the fragments are separated by physico-chemical means (emulsion PCR or bridge PCR). • Very many fragments are sequenced in parallel in a flow cell (as opposed to a capillary), observed by a microscope with CCD camera. Solexa workflow • Bridge PCD to prepare “clusters” • Sequencing: 35 or more cycles x 4 bases, with micrographs taken in 300 tiles x 8 lanes -> more than 1 terabyte of image data • “SolexaPipeline”: Sequences and alignment Solexa: Flow cell Solexa: sample preparartion Solexa: sample preparartion Solexa: sequencing Solexa: sequencing Roche 454 • presented 2005, first on market • emulsion PCR • pyrosequencing (polymerase-based) • read length: 250 bp • paired read separation: 3 kb • 300 Mb per day • $60 per Mb • error rate: around 5% per bp • dominant type of error: indels, especially in homopolymers Illumina / Solexa • second on the market • bridge PCR • polymerase-based sequencing-by-synthesis -
Contributions to Biostatistics: Categorical Data Analysis, Data Modeling and Statistical Inference Mathieu Emily
Contributions to biostatistics: categorical data analysis, data modeling and statistical inference Mathieu Emily To cite this version: Mathieu Emily. Contributions to biostatistics: categorical data analysis, data modeling and statistical inference. Mathematics [math]. Université de Rennes 1, 2016. tel-01439264 HAL Id: tel-01439264 https://hal.archives-ouvertes.fr/tel-01439264 Submitted on 18 Jan 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. HABILITATION À DIRIGER DES RECHERCHES Université de Rennes 1 Contributions to biostatistics: categorical data analysis, data modeling and statistical inference Mathieu Emily November 15th, 2016 Jury: Christophe Ambroise Professeur, Université d’Evry Val d’Essonne, France Rapporteur Gérard Biau Professeur, Université Pierre et Marie Curie, France Président David Causeur Professeur, Agrocampus Ouest, France Examinateur Heather Cordell Professor, University of Newcastle upon Tyne, United Kingdom Rapporteur Jean-Michel Marin Professeur, Université de Montpellier, France Rapporteur Valérie Monbet Professeur, Université de Rennes 1, France Examinateur Korbinian Strimmer Professor, Imperial College London, United Kingdom Examinateur Jean-Philippe Vert Directeur de recherche, Mines ParisTech, France Examinateur Pour M.H.A.M. Remerciements En tout premier lieu je tiens à adresser mes remerciements à l’ensemble des membres du jury qui m’ont fait l’honneur d’évaluer mes travaux. -
Sequence Analysis Instructions
Sequence Analysis Instructions In order to predict your drug metabolizing phenotype from your CYP2D6 gene sequence, you must determine: 1) The assembled sequence from your two opposing sequencing reactions 2) If your PCR product even represents the human CYP2D6 gene, 3) The location of your sequence within the CYP2D6 gene, 4) Whether differences between your alleles and CYP2D6*1 sequence represents sequencing errors or polymorphisms in your sequence, and 5) The effect(s) of any polymorphisms on CYP2D6 protein sequence. As you analyze your gene sequence, copy and paste your analyses and results into a text file for your final lab report. If some factor, like the quality of your sequence, prevents you from carrying out the complete analysis, your grade will not be penalized—just complete as many of the steps below as possible, and include an explanation of why you could not complete the analysis in your final report. Font appearing in bold green italics describes questions to answer and items to include in your final report. Part 1: Assembling your CYP2D6 sequence from both directions The sequencing reaction only produces 700-800 bases of good sequence. To get most of the sequence of the 1.2 Kbp PCR product, sequencing reactions were performed from both directions on the PCR product. These need to be assembled into one sequence using the overlap of the two sequences. In order to assure that it is going in the forward direction, it is best to derive the reverse complement sequence from the reverse sequencing file. Take the text file and paste it into a program like http://www.bioinformatics.org/sms/rev_comp.html. -
Agenda: Gene Prediction by Cross-Species Sequence Comparison Leila Taher1, Oliver Rinner2,3, Saurabh Garg1, Alexander Sczyrba4 and Burkhard Morgenstern5,*
Nucleic Acids Research, 2004, Vol. 32, Web Server issue W305–W308 DOI: 10.1093/nar/gkh386 AGenDA: gene prediction by cross-species sequence comparison Leila Taher1, Oliver Rinner2,3, Saurabh Garg1, Alexander Sczyrba4 and Burkhard Morgenstern5,* 1International Graduate School for Bioinformatics and Genome Research, University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld, Germany, 2GSF Research Center, MIPS/Institute of Bioinformatics, Ingolsta¨dter Landstrasse 1, 85764 Neuherberg, Germany, 3Brain Research Institute, ETH Zuurich,€ Winterthurerstrasse 190, CH-8057 Zuurich,€ Switzerland, 4Faculty of Technology, Research Group in Practical Computer Science, University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld, Germany and 5Institute of Microbiology and Genetics, University of Go¨ttingen, Goldschmidtstrasse 1, 37077 Go¨ttingen, Germany Received March 5, 2004; Revised and Accepted March 24, 2004 ABSTRACT Thus, with the huge amount of data produced by sequencing projects, the development of high-quality gene-prediction Automatic gene prediction is one of the major tools has become a priority in bioinformatics. For eukaryotic challenges in computational sequence analysis. organisms, gene prediction is particularly challenging, since Traditional approaches to gene finding rely on statis- protein-coding exons are usually separated by introns of vari- tical models derived from previously known genes. By able length. Most traditional methods for gene prediction are contrast, a new class of comparative methods relies intrinsic methods; they use hidden Markov models (HMMs) or on comparing genomic sequences from evolutionary other stochastic approaches describing the statistical composi- related organisms to each other. These methods are tion of introns, exons, etc. Such models are trained with already based on the concept of phylogenetic footprinting: known genes from the same or a closely related species; the they exploit the fact that functionally important most popular of these tools is GenScan (1). -
Introduction to Genomicfiles
Introduction to GenomicFiles Valerie Obenchain, Michael Love, Martin Morgan Last modified: October 2014; Compiled: May 19, 2021 Contents 1 Introduction ..............................1 2 Quick Start ..............................2 3 Overview of classes and functions .................3 3.1 GenomicFiles class ........................3 3.2 Functions ............................3 4 Queries across files: reduceByRange and reduceRanges......5 4.1 Pileup summaries ........................6 4.2 Basepair-level t-test with case / control groups...........7 5 Queries within files: reduceByFile and reduceFiles .......8 5.1 Counting read junctions......................8 5.2 Coverage 1: reduceByFile .................... 10 5.3 Coverage 2: reduceFiles ..................... 12 5.4 Coverage 3: reduceFiles with chunking .............. 13 6 Chunking ............................... 15 6.1 Ranges in a file.......................... 15 6.2 Records in a file ......................... 15 7 sessionInfo() ............................. 17 1 Introduction This vignette illustrates how to use the GenomicFiles package for distributed computing across files. The functions in GenomicFiles manipulate and combine data subsets via two user-supplied functions, MAP and REDUCE. These are similar in spirit to Map and Reduce in base R. Together they provide a flexible interface to extract, manipulate and combine data. Both functions are executed in the distributed step which means results are combined on a single worker, not across workers. Introduction to GenomicFiles We assume the -
Getting Started Deciphering
Getting Started DECIPHERing Erik S. Wright May 19, 2021 Contents 1 About DECIPHER 1 2 Design Philosophy 2 2.1 Curators Protect the Originals . 2 2.2 Don't Reinvent the Wheel . 2 2.3 That Which is the Most Difficult, Make Fastest . 2 2.4 Stay Organized . 2 3 Functionality 3 4 Installation 5 4.1 Typical Installation (recommended) . 5 4.2 Manual Installation . 5 4.2.1 All platforms . 5 4.2.2 MacOSX........................................... 6 4.2.3 Linux ............................................. 6 4.2.4 Windows ........................................... 6 5 Example Workflow 6 6 Session Information 11 1 About DECIPHER DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the R statistical programming language. The program features tools falling into five categories: Sequence databases: import, maintain, view, and export a massive number of sequences. Sequence alignment: accurately align thousands of DNA, RNA, or amino acid sequences. Quickly find and align the syntenic regions of multiple genomes. Oligo design: test oligos in silico, or create new primer and probe sequences optimized for a variety of objectives. Manipulate sequences: trim low quality regions, correct frameshifts, reorient nucleotides, determine consensus, or digest with restriction enzymes. Analyze sequences: find chimeras, classify into a taxonomy of organisms or functions, detect repeats, predict secondary structure, create phylogenetic trees, and reconstruct ancestral states. 1 Gene finding: predict coding and non-coding genes in a genome, extract them from the genome, and export them to a file. DECIPHER is available under the terms of the GNU Public License version 3. 2 Design Philosophy 2.1 Curators Protect the Originals One of the core principles of DECIPHER is the idea of the non-destructive workflow.