An Introduction to Linux and Bowtie
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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. -
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. -
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. -
Large Scale Genomic Rearrangements in Selected Arabidopsis Thaliana T
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.03.433755; this version posted March 7, 2021. 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 4.0 International license. 1 Large scale genomic rearrangements in selected 2 Arabidopsis thaliana T-DNA lines are caused by T-DNA 3 insertion mutagenesis 4 5 Boas Pucker1,2+, Nils Kleinbölting3+, and Bernd Weisshaar1* 6 1 Genetics and Genomics of Plants, Center for Biotechnology (CeBiTec), Bielefeld University, 7 Sequenz 1, 33615 Bielefeld, Germany 8 2 Evolution and Diversity, Department of Plant Sciences, University of Cambridge, Cambridge, 9 United Kingdom 10 3 Bioinformatics Resource Facility, Center for Biotechnology (CeBiTec, Bielefeld University, 11 Sequenz 1, 33615 Bielefeld, Germany 12 + authors contributed equally 13 * corresponding author: Bernd Weisshaar 14 15 BP: [email protected], ORCID: 0000-0002-3321-7471 16 NK: [email protected], ORCID: 0000-0001-9124-5203 17 BW: [email protected], ORCID: 0000-0002-7635-3473 18 19 20 21 22 23 24 25 26 27 28 29 30 page 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.03.03.433755; this version posted March 7, 2021. 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 4.0 International license. -
An Open-Sourced Bioinformatic Pipeline for the Processing of Next-Generation Sequencing Derived Nucleotide Reads
bioRxiv preprint doi: https://doi.org/10.1101/2020.04.20.050369; this version posted May 28, 2020. 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 4.0 International license. An open-sourced bioinformatic pipeline for the processing of Next-Generation Sequencing derived nucleotide reads: Identification and authentication of ancient metagenomic DNA Thomas C. Collin1, *, Konstantina Drosou2, 3, Jeremiah Daniel O’Riordan4, Tengiz Meshveliani5, Ron Pinhasi6, and Robin N. M. Feeney1 1School of Medicine, University College Dublin, Ireland 2Division of Cell Matrix Biology Regenerative Medicine, University of Manchester, United Kingdom 3Manchester Institute of Biotechnology, School of Earth and Environmental Sciences, University of Manchester, United Kingdom [email protected] 5Institute of Paleobiology and Paleoanthropology, National Museum of Georgia, Tbilisi, Georgia 6Department of Evolutionary Anthropology, University of Vienna, Austria *Corresponding Author Abstract The emerging field of ancient metagenomics adds to these Bioinformatic pipelines optimised for the processing and as- processing complexities with the need for additional steps sessment of metagenomic ancient DNA (aDNA) are needed in the separation and authentication of ancient sequences from modern sequences. Currently, there are few pipelines for studies that do not make use of high yielding DNA cap- available for the analysis of ancient metagenomic DNA ture techniques. These bioinformatic pipelines are tradition- 1 4 ally optimised for broad aDNA purposes, are contingent on (aDNA) ≠ The limited number of bioinformatic pipelines selection biases and are associated with high costs. -
Sequence Alignment/Map Format Specification
Sequence Alignment/Map Format Specification The SAM/BAM Format Specification Working Group 3 Jun 2021 The master version of this document can be found at https://github.com/samtools/hts-specs. This printing is version 53752fa from that repository, last modified on the date shown above. 1 The SAM Format Specification SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. If present, the header must be prior to the alignments. Header lines start with `@', while alignment lines do not. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. This specification is for version 1.6 of the SAM and BAM formats. Each SAM and BAMfilemay optionally specify the version being used via the @HD VN tag. For full version history see Appendix B. Unless explicitly specified elsewhere, all fields are encoded using 7-bit US-ASCII 1 in using the POSIX / C locale. Regular expressions listed use the POSIX / IEEE Std 1003.1 extended syntax. 1.1 An example Suppose we have the following alignment with bases in lowercase clipped from the alignment. Read r001/1 and r001/2 constitute a read pair; r003 is a chimeric read; r004 represents a split alignment. Coor 12345678901234 5678901234567890123456789012345 ref AGCATGTTAGATAA**GATAGCTGTGCTAGTAGGCAGTCAGCGCCAT +r001/1 TTAGATAAAGGATA*CTG +r002 aaaAGATAA*GGATA +r003 gcctaAGCTAA +r004 ATAGCT..............TCAGC -r003 ttagctTAGGC -r001/2 CAGCGGCAT The corresponding SAM format is:2 1Charset ANSI X3.4-1968 as defined in RFC1345. -
Assembly Exercise
Assembly Exercise Turning reads into genomes Where we are • 13:30-14:00 – Primer Design to Amplify Microbial Genomes for Sequencing • 14:00-14:15 – Primer Design Exercise • 14:15-14:45 – Molecular Barcoding to Allow Multiplexed NGS • 14:45-15:15 – Processing NGS Data – de novo and mapping assembly • 15:15-15:30 – Break • 15:30-15:45 – Assembly Exercise • 15:45-16:15 – Annotation • 16:15-16:30 – Annotation Exercise • 16:30-17:00 – Submitting Data to GenBank Log onto ILRI cluster • Log in to HPC using ILRI instructions • NOTE: All the commands here are also in the file - assembly_hands_on_steps.txt • If you are like me, it may be easier to cut and paste Linux commands from this file instead of typing them in from the slides Start an interactive session on larger servers • The interactive command will start a session on a server better equipped to do genome assembly $ interactive • Switch to csh (I use some csh features) $ csh • Set up Newbler software that will be used $ module load 454 A norovirus sample sequenced on both 454 and Illumina • The vendors use different file formats unknown_norovirus_454.GACT.sff unknown_norovirus_illumina.fastq • I have converted these files to additional formats for use with the assembly tools unknown_norovirus_454_convert.fasta unknown_norovirus_454_convert.fastq unknown_norovirus_illumina_convert.fasta Set up and run the Newbler de novo assembler • Create a new de novo assembly project $ newAssembly de_novo_assembly • Add read data to the project $ addRun de_novo_assembly unknown_norovirus_454.GACT.sff -
Supplemental Material Nanopore Sequencing of Complex Genomic
Supplemental Material Nanopore sequencing of complex genomic rearrangements in yeast reveals mechanisms of repeat- mediated double-strand break repair McGinty, et al. Contents: Supplemental Methods Supplemental Figures S1-S6 Supplemental References Supplemental Methods DNA extraction: The DNA extraction protocol used is a slight modification of a classic ethanol precipitation. First, yeast cultures were grown overnight in 2 ml of complete media (YPD) and refreshed for 4 hours in an additional 8 ml YPD to achieve logarithmic growth. The cultures were spun down, and the pellets were resuspended in 290 µl of solution containing 0.9M Sorbitol and 0.1M EDTA at pH 7.5. 10 µl lyticase enzyme was added, and the mixture was incubated for 30 minutes at 37oC in order to break down the yeast cell wall. The mixture was centrifuged at 8,000 rpm for two minutes, and the pellet was resuspended in 270 µl of solution containing 50mM Tris 20mM EDTA at pH 7.5, and 30 µl of 10% SDS. Following five minutes incubation at room temperature, 150 µl of chilled 5M potassium acetate solution was added. This mixture was incubated for 10 min at 4oC and centrifuged for 10 min at 13,000 rpm. The supernatant was then combined with 900 µl of pure ethanol, which had been chilled on ice. This solution was stored overnight at -20oC, and then centrifuged for 20 minutes at 4,000 rpm in a refrigerated centrifuge. The pellet was then washed twice with 70% ethanol, allowed to dry completely, and then resuspended in 50 µl TE (10mM Tris, 1mM EDTA, pH 8). -
Revenues 1010 1020 1030 1040 1045 1060 1090 82,958 139,250
FCC Paper Report 43-01 ARMIS Annual Summary Report COMPANY Northern New England Telephone Telephone Operallons LLC ST UDY AREA. New Hempsh1 re PERIOD From: Jan 201 1 To Dec 2011 COSA FPNH A ACCOUNT LEVEL REPORTING (Dollars on thousands) ROW CLASSIFICATION T otal Nonreg AdJu stments Subject To Slate Interstate (a) (b) (C) (d) Separations (g) (h) (f) Revenues 1010 Besoc Local Servoces 82,958 N/A 0 82,958 82,958 0 1020 Network Access Services 139,250 N/A 0 139,250 9,443 129,807 1030 Toil Network Servoces 13,911 N/A 0 13,911 13,881 31 1040 Mosceltaneous 33,250 N/A 0 33,250 22,165 11,084 1045 Nonregutated 7,540 7,540 N/A N/A N/A N/A 1060 Uncoltecllbles 3,597 101 0 3,497 1,615 1,882 1090 Total Operalong Revenues 273,312 7,439 0 265,872 126,832 139,040 Imputation or Dtrectory Revenue 23,300 Total Assessed Revenue 296,612 Assessment 942,999 403,229 Assessment Factor $0.00318 $ 0.0031 8 The revenue data above os avaolable on the FCC ARMIS websote at http)/fjallrossJcc.gov/eafs7/adhoc/table year tal Profile and Historic Information! AT&T Labs! AT&T Page 1 of 5 Personal Business About AT&T ·~ ~ at&t Backgrounder In today's rapidly changing business environment, many of the most exciting innovations are being spearheaded by AT&T Labs, the long-respected research and development arm of AT&T. History The year was 1901...the beginning of a new century. -
Providing Bioinformatics Services on Cloud
Providing Bioinformatics Services on Cloud Christophe Blanchet, Clément Gauthey InfrastructureC. Blanchet Distributed and C. Gauthey for Biology IDB-IBCPEGI CF13, CNRS Manchester, FR3302 - LYON 9 April - FRANCE2013 http://idee-b.ibcp.fr Infrastructure Distributed for Biology - IDB CNRS-IBCP FR3302, Lyon, FRANCE IDB acknowledges co-funding by the European Community's Seventh Framework Programme (INFSO-RI-261552) and the French National Research Agency's Arpege Programme (ANR-10-SEGI-001) Bioinformatics Today • Biological data are big data • 1512 online databases (NAR Database Issue 2013) • Institut Sanger, UK, 5 PB • Beijing Genome Institute, China, 4 sites, 10 PB ➡ Big data in lot of places • Analysing such data became difficult • Scale-up of the analyses : gene/protein to complete genome/ proteome, ... • Lot of different daily-used tools • That need to be combined in workflows • Usual interfaces: portals, Web services, federation,... ➡ Datacenters with ease of access/use • Distributed resources ADN Experimental platforms: NGS, imaging, ... • ADN • Bioinformatics platforms BI Federation of datacenters M ➡ BI CC ADN ADN BI ADN EGI CF13, Manchester, 9 April 2013 ADN ADN BI ADN ADN M M Sequencing Genomes Complete genome sequencing become a lab commodity with NGS (cheap and efficient) source: www.genomesonline.org source: www.politigenomics.com/next-generation-sequencing-informatics EGI CF13, Manchester, 9 April 2013 Infrastructures in Biology Lot of tools and web services to treat and vizualize lot of data EGI CF13, Manchester, 9 April 2013 -
Syntax Highlighting for Computational Biology Artem Babaian1†, Anicet Ebou2, Alyssa Fegen3, Ho Yin (Jeffrey) Kam4, German E
bioRxiv preprint doi: https://doi.org/10.1101/235820; this version posted December 20, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors. bioSyntax: Syntax Highlighting For Computational Biology Artem Babaian1†, Anicet Ebou2, Alyssa Fegen3, Ho Yin (Jeffrey) Kam4, German E. Novakovsky5, and Jasper Wong6. 5 10 15 Affiliations: 1. Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada. [[email protected]] 2. Departement de Formation et de Recherches Agriculture et Ressources Animales, Institut National Polytechnique Felix Houphouet-Boigny, Yamoussoukro, Côte d’Ivoire. [[email protected]] 20 3. Faculty of Science, University of British Columbia, Vancouver, BC, Canada [[email protected]] 4. Faculty of Mathematics, University of Waterloo, Waterloo, ON, Canada. [[email protected]] 5. Department of Medical Genetics, University of British Columbia, Vancouver, BC, 25 Canada. [[email protected]] 6. Genome Science and Technology, University of British Columbia, Vancouver, BC, Canada. [[email protected]] Correspondence†: 30 Artem Babaian Terry Fox Laboratory BC Cancer Research Centre 675 West 10th Avenue Vancouver, BC, Canada. V5Z 1L3. 35 Email: [[email protected]] bioRxiv preprint doi: https://doi.org/10.1101/235820; this version posted December 20, 2017. The copyright holder has placed this preprint (which was not certified by peer review) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors.