Sequence Alignment/Map Format Specification
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De Novo Genomic Analyses for Non-Model Organisms: an Evaluation of Methods Across a Multi-Species Data Set
De novo genomic analyses for non-model organisms: an evaluation of methods across a multi-species data set Sonal Singhal [email protected] Museum of Vertebrate Zoology University of California, Berkeley 3101 Valley Life Sciences Building Berkeley, California 94720-3160 Department of Integrative Biology University of California, Berkeley 1005 Valley Life Sciences Building Berkeley, California 94720-3140 June 5, 2018 Abstract High-throughput sequencing (HTS) is revolutionizing biological research by enabling sci- entists to quickly and cheaply query variation at a genomic scale. Despite the increasing ease of obtaining such data, using these data effectively still poses notable challenges, especially for those working with organisms without a high-quality reference genome. For every stage of analysis – from assembly to annotation to variant discovery – researchers have to distinguish technical artifacts from the biological realities of their data before they can make inference. In this work, I explore these challenges by generating a large de novo comparative transcriptomic dataset data for a clade of lizards and constructing a pipeline to analyze these data. Then, using a combination of novel metrics and an externally validated variant data set, I test the efficacy of my approach, identify areas of improvement, and propose ways to minimize these errors. I arXiv:1211.1737v1 [q-bio.GN] 8 Nov 2012 find that with careful data curation, HTS can be a powerful tool for generating genomic data for non-model organisms. Keywords: de novo assembly, transcriptomes, suture zones, variant discovery, annotation Running Title: De novo genomic analyses for non-model organisms 1 Introduction High-throughput sequencing (HTS) is poised to revolutionize the field of evolutionary genetics by enabling researchers to assay thousands of loci for organisms across the tree of life. -
Gene Discovery and Annotation Using LCM-454 Transcriptome Sequencing Scott J
Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Methods Gene discovery and annotation using LCM-454 transcriptome sequencing Scott J. Emrich,1,2,6 W. Brad Barbazuk,3,6 Li Li,4 and Patrick S. Schnable1,4,5,7 1Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, Iowa 50010, USA; 2Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50010, USA; 3Donald Danforth Plant Science Center, St. Louis, Missouri 63132, USA; 4Interdepartmental Plant Physiology Graduate Major and Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa 50010, USA; 5Department of Agronomy and Center for Plant Genomics, Iowa State University, Ames, Iowa 50010, USA 454 DNA sequencing technology achieves significant throughput relative to traditional approaches. More than 261,000 ESTs were generated by 454 Life Sciences from cDNA isolated using laser capture microdissection (LCM) from the developmentally important shoot apical meristem (SAM) of maize (Zea mays L.). This single sequencing run annotated >25,000 maize genomic sequences and also captured ∼400 expressed transcripts for which homologous sequences have not yet been identified in other species. Approximately 70% of the ESTs generated in this study had not been captured during a previous EST project conducted using a cDNA library constructed from hand-dissected apex tissue that is highly enriched for SAMs. In addition, at least 30% of the 454-ESTs do not align to any of the ∼648,000 extant maize ESTs using conservative alignment criteria. These results indicate that the combination of LCM and the deep sequencing possible with 454 technology enriches for SAM transcripts not present in current EST collections. -
Dealing with Document Size Limits
Dealing with Document Size Limits Introduction The Electronic Case Filing system will not accept PDF documents larger than ten megabytes (MB). If the document size is less than 10 MB, it can be filed electronically just as it is. If it is larger than 10 MB, it will need to be divided into two or more documents, with each document being less than 10 MB. Word Processing Documents Documents created with a word processing program (such as WordPerfect or Microsoft Word) and correctly converted to PDF will generally be smaller than a scanned document. Because of variances in software, usage, and content, it is difficult to estimate the number of pages that would constitute 10 MB. (Note: See “Verifying File Size” below and for larger documents, see “Splitting PDF Documents into Multiple Documents” below.) Scanned Documents Although the judges’ Filing Preferences indicate a preference for conversion of documents rather than scanning, it will be necessary to scan some documents for filing, e.g., evidentiary attachments must be scanned. Here are some things to remember: • Documents scanned to PDF are generally much larger than those converted through a word processor. • While embedded fonts may be necessary for special situations, e.g., trademark, they will increase the file size. • If graphs or color photos are included, just a few pages can easily exceed the 10 MB limit. Here are some guidelines: • The court’s standard scanner resolution is 300 dots per inch (DPI). Avoid using higher resolutions as this will create much larger file sizes. • Normally, the output should be set to black and white. -
Genetic Databases
Stefano Lonardi March, 2000 Compression of Biological Sequences by Greedy Off-line Textual Substitution Alberto Apostolico Stefano Lonardi Purdue University Università di Padova Genetic Databases § Massive § Growing exponentially Example: GenBank contains approximately 4,654,000,000 bases in 5,355,000 sequence records as of December 1999 Data Compression Conference 2000 1 Stefano Lonardi March, 2000 DNA Sequence Records Composed by annotations (in English) and DNA bases (on the alphabet {A,C,G,T,U,M,R,W,S,Y,K,V,H,D,B,X,N}) >RTS2 RTS2 upstream sequence, from -200 to -1 TCTGTTATAGTACATATTATAGTACACCAATGTAAATCTGGTCCGGGTTACACAACACTT TGTCCTGTACTTTGAAAACTGGAAAAACTCCGCTAGTTGAAATTAATATCAAATGGAAAA GTCAGTATCATCATTCTTTTCTTGACAAGTCCTAAAAAGAGCGAAAACACAGGGTTGTTT GATTGTAGAAAATCACAGCG >MEK1 MEK1 upstream sequence, from -200 to -1 TTCCAATCATAAAGCATACCGTGGTYATTTAGCCGGGGAAAAGAAGAATGATGGCGGCTA AATTTCGGCGGCTATTTCATTCATTCAAGTATAAAAGGGAGAGGTTTGACTAATTTTTTA CTTGAGCTCCTTCTGGAGTGCTCTTGTACGTTTCAAATTTTATTAAGGACCAAATATACA ACAGAAAGAAGAAGAGCGGA >NDJ1 NDJ1 upstream sequence, from -200 to -1 ATAAAATCACTAAGACTAGCAACCACGTTTTGTTTTGTAGTTGAGAGTAATAGTTACAAA TGGAAGATATATATCCGTTTCGTACTCAGTGACGTACCGGGCGTAGAAGTTGGGCGGCTA TTTGACAGATATATCAAAAATATTGTCATGAACTATACCATATACAACTTAGGATAAAA ATACAGGTAGAAAAACTATA Problem Textual compression of DNA data is difficult, i.e., “standard” methods do not seem to exploit the redundancies (if any) inherent to DNA sequences cfr. C.Nevill-Manning, I.H.Witten, “Protein is incompressible”, DCC99 Data Compression Conference 2000 2 Stefano Lonardi March, 2000 -
MATCH-G Program
MATCH-G Program The MATCH-G toolset MATCH-G (Mutational Analysis Toolset Comparing wHole Genomes) Download the Toolset The toolset is prepared for use with pombe genomes and a sample genome from Sanger is included. However, it is written in such a way as to allow it to utilize any set or number of chromosomes. Simply follow the naming convention "chromosomeX.contig.embl" where X=1,2,3 etc. For use in terminal windows in Mac OSX or Unix-like environments where Perl is present by default. Toolset Description Version History 2.0 – Bug fixes related to scaling to other genomes. First version of GUI interface. 1.0 – Initial Release. Includes build, alignment, snp, copy, and gap resolution routines in original form. Please note this project in under development and will undergo significant changes. Project Description The MATCH-G toolset has been developed to facilitate the evaluation of whole genome sequencing data from yeasts. Currently, the toolset is written for pombe strains, however the toolset is easily scalable to other yeasts or other sequenced genomes. The included tools assist in the identification of SNP mutations, short (and long) insertion/deletions, and changes in gene/region copy number between a parent strain and mutant or revertant strain. Currently, the toolset is run from the command line in a unix or similar terminal and requires a few additional programs to run noted below. Installation It is suggested that a separate folder be generated for the toolset and generated files. Free disk space proportional to the original read datasets is recommended. The toolset utilizes and requires a few additional free programs: Bowtie rapid alignment software: http://bowtie-bio.sourceforge.net/index.shtml (Langmead B, Trapnell C, Pop M, Salzberg SL. -
Acme: a User Interface for Programmers Rob Pike [email protected]−Labs.Com
Acme: A User Interface for Programmers Rob Pike [email protected]−labs.com ABSTRACT A hybrid of window system, shell, and editor, Acme gives text-oriented applications a clean, expressive, and consistent style of interaction. Tradi tional window systems support interactive client programs and offer libraries of pre-defined operations such as pop-up menus and buttons to promote a consistent user interface among the clients. Acme instead pro vides its clients with a fixed user interface and simple conventions to encourage its uniform use. Clients access the facilities of Acme through a file system interface; Acme is in part a file server that exports device-like files that may be manipulated to access and control the contents of its win dows. Written in a concurrent programming language, Acme is structured as a set of communicating processes that neatly subdivide the various aspects of its tasks: display management, input, file server, and so on. Acme attaches distinct functions to the three mouse buttons: the left selects text; the middle executes textual commands; and the right com bines context search and file opening functions to integrate the various applications and files in the system. Acme works well enough to have developed a community that uses it exclusively. Although Acme discourages the traditional style of interaction based on typescript windowsߞteletypesߞits users find Acmeߣs other ser vices render typescripts obsolete. History and motivation The usual typescript style of interaction with Unix and its relatives is an old one. The typescriptߞan intermingling of textual commands and their outputߞoriginates with the scrolls of paper on teletypes. -
File Formats Exercises
Introduction to high-throughput sequencing file formats – advanced exercises Daniel Vodák (Bioinformatics Core Facility, The Norwegian Radium Hospital) ( [email protected] ) All example files are located in directory “/data/file_formats/data”. You can set your current directory to that path (“cd /data/file_formats/data”) Exercises: a) FASTA format File “Uniprot_Mammalia.fasta” contains a collection of mammalian protein sequences obtained from the UniProt database ( http://www.uniprot.org/uniprot ). 1) Count the number of lines in the file. 2) Count the number of header lines (i.e. the number of sequences) in the file. 3) Count the number of header lines with “Homo” in the organism/species name. 4) Count the number of header lines with “Homo sapiens” in the organism/species name. 5) Display the header lines which have “Homo” in the organism/species names, but only such that do not have “Homo sapiens” there. b) FASTQ format File NKTCL_31T_1M_R1.fq contains a reduced collection of tumor read sequences obtained from The Sequence Read Archive ( http://www.ncbi.nlm.nih.gov/sra ). 1) Count the number of lines in the file. 2) Count the number of lines beginning with the “at” symbol (“@”). 3) How many reads are there in the file? c) SAM format File NKTCL_31T_1M.sam contains alignments of pair-end reads stored in files NKTCL_31T_1M_R1.fq and NKTCL_31T_1M_R2.fq. 1) Which program was used for the alignment? 2) How many header lines are there in the file? 3) How many non-header lines are there in the file? 4) What do the non-header lines represent? 5) Reads from how many template sequences have been used in the alignment process? c) BED format File NKTCL_31T_1M.bed holds information about alignment locations stored in file NKTCL_31T_1M.sam. -
ARC-LEAP User Instructions for The
ARC-LEAP User Instructions Appalachian Regional Commission Local Economic Assessment Package prepared for the Appalachian Regional Commission prepared by Economic Development Research Group, Inc. Glen Weisbrod Teresa Lynch Margaret Collins January, 2004 ARC-LEAP User Instructions Appalachian Regional Commission Local Economic Assessment Package prepared for the Appalachian Regional Commission prepared by Economic Development Research Group, Inc. 2 Oliver Street, 9th Floor, Boston, MA 02109 Telephone 617.338.6775 Fax 617.338.1174 e-mail [email protected] Website www.edrgroup.com January, 2004 ARC-LEAP User Instructions PREFACE LEAP is a software tool that was designed and developed by Economic Development Research Group, Inc. (www.edrgroup.com) to assist practitioners in evaluating local economic development needs and opportunities. ARC-LEAP is a version of this tool developed specifically for the Appalachian Regional Commission (ARC) and it’s Local Development Districts (LDDs). Development of this user guide was funded by ARC as a companion to the ARC-LEAP analysis system. This document presents user instructions and technical documentation for ARC-LEAP. It is organized into three parts: I. overview of the ARC-LEAP tool II. instructions for users to obtain input information and run the analysis model III. interpretation of output tables . A separate Handbook document provides more detailed discussion of the economic development assessment process, including analysis of local economic performance, diagnosis of local strengths and weaknesses, and application of business opportunity information for developing an economic development strategy. Economic Development Research Group i ARC-LEAP User Instructions I. OVERVIEW The ARC-LEAP model serves to three related purposes, each aimed at helping practitioners identify target industries for economic development. -
Tiny Tools Gerard J
Tiny Tools Gerard J. Holzmann Jet Propulsion Laboratory, California Institute of Technology Many programmers like the convenience of integrated development environments (IDEs) when developing code. The best examples are Microsoft’s Visual Studio for Windows and Eclipse for Unix-like systems, which have both been around for many years. You get all the features you need to build and debug software, and lots of other things that you will probably never realize are also there. You can use all these features without having to know very much about what goes on behind the screen. And there’s the rub. If you’re like me, you want to know precisely what goes on behind the screen, and you want to be able to control every bit of it. The IDEs can sometimes feel as if they are taking over every last corner of your computer, leaving you wondering how much bigger your machine would have to be to make things run a little more smoothly. So what follows is for those of us who don’t use IDEs. It’s for the bare metal programmers, who prefer to write code using their own screen editor, and who do everything else with command-line tools. There are no real conveniences that you need to give up to work this way, but what you gain is a better understanding your development environment, and the control to change, extend, or improve it whenever you find better ways to do things. Bare Metal Programming Many developers who write embedded software work in precisely this way. -
Backtrack Parsing Context-Free Grammar Context-Free Grammar
Context-free Grammar Problems with Regular Context-free Grammar Language and Is English a regular language? Bad question! We do not even know what English is! Two eggs and bacon make(s) a big breakfast Backtrack Parsing Can you slide me the salt? He didn't ought to do that But—No! Martin Kay I put the wine you brought in the fridge I put the wine you brought for Sandy in the fridge Should we bring the wine you put in the fridge out Stanford University now? and University of the Saarland You said you thought nobody had the right to claim that they were above the law Martin Kay Context-free Grammar 1 Martin Kay Context-free Grammar 2 Problems with Regular Problems with Regular Language Language You said you thought nobody had the right to claim [You said you thought [nobody had the right [to claim that they were above the law that [they were above the law]]]] Martin Kay Context-free Grammar 3 Martin Kay Context-free Grammar 4 Problems with Regular Context-free Grammar Language Nonterminal symbols ~ grammatical categories Is English mophology a regular language? Bad question! We do not even know what English Terminal Symbols ~ words morphology is! They sell collectables of all sorts Productions ~ (unordered) (rewriting) rules This concerns unredecontaminatability Distinguished Symbol This really is an untiable knot. But—Probably! (Not sure about Swahili, though) Not all that important • Terminals and nonterminals are disjoint • Distinguished symbol Martin Kay Context-free Grammar 5 Martin Kay Context-free Grammar 6 Context-free Grammar Context-free -
Adaptive LL(*) Parsing: the Power of Dynamic Analysis
Adaptive LL(*) Parsing: The Power of Dynamic Analysis Terence Parr Sam Harwell Kathleen Fisher University of San Francisco University of Texas at Austin Tufts University [email protected] [email protected] kfi[email protected] Abstract PEGs are unambiguous by definition but have a quirk where Despite the advances made by modern parsing strategies such rule A ! a j ab (meaning “A matches either a or ab”) can never as PEG, LL(*), GLR, and GLL, parsing is not a solved prob- match ab since PEGs choose the first alternative that matches lem. Existing approaches suffer from a number of weaknesses, a prefix of the remaining input. Nested backtracking makes de- including difficulties supporting side-effecting embedded ac- bugging PEGs difficult. tions, slow and/or unpredictable performance, and counter- Second, side-effecting programmer-supplied actions (muta- intuitive matching strategies. This paper introduces the ALL(*) tors) like print statements should be avoided in any strategy that parsing strategy that combines the simplicity, efficiency, and continuously speculates (PEG) or supports multiple interpreta- predictability of conventional top-down LL(k) parsers with the tions of the input (GLL and GLR) because such actions may power of a GLR-like mechanism to make parsing decisions. never really take place [17]. (Though DParser [24] supports The critical innovation is to move grammar analysis to parse- “final” actions when the programmer is certain a reduction is time, which lets ALL(*) handle any non-left-recursive context- part of an unambiguous final parse.) Without side effects, ac- free grammar. ALL(*) is O(n4) in theory but consistently per- tions must buffer data for all interpretations in immutable data forms linearly on grammars used in practice, outperforming structures or provide undo actions. -
EMBL-EBI Powerpoint Presentation
Processing data from high-throughput sequencing experiments Simon Anders Use-cases for HTS · de-novo sequencing and assembly of small genomes · transcriptome analysis (RNA-Seq, sRNA-Seq, ...) • identifying transcripted regions • expression profiling · Resequencing to find genetic polymorphisms: • SNPs, micro-indels • CNVs · ChIP-Seq, nucleosome positions, etc. · DNA methylation studies (after bisulfite treatment) · environmental sampling (metagenomics) · reading bar codes Use cases for HTS: Bioinformatics challenges Established procedures may not be suitable. New algorithms are required for · assembly · alignment · statistical tests (counting statistics) · visualization · segmentation · ... Where does Bioconductor come in? Several steps: · Processing of the images and determining of the read sequencest • typically done by core facility with software from the manufacturer of the sequencing machine · Aligning the reads to a reference genome (or assembling the reads into a new genome) • Done with community-developed stand-alone tools. · Downstream statistical analyis. • Write your own scripts with the help of Bioconductor infrastructure. Solexa standard workflow SolexaPipeline · "Firecrest": Identifying clusters ⇨ typically 15..20 mio good clusters per lane · "Bustard": Base calling ⇨ sequence for each cluster, with Phred-like scores · "Eland": Aligning to reference Firecrest output Large tab-separated text files with one row per identified cluster, specifying · lane index and tile index · x and y coordinates of cluster on tile · for each