Which Genome Browser to Use for My Data ? July 2019
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T-Coffee Documentation Release Version 13.45.47.Aba98c5
T-Coffee Documentation Release Version_13.45.47.aba98c5 Cedric Notredame Aug 31, 2021 Contents 1 T-Coffee Installation 3 1.1 Installation................................................3 1.1.1 Unix/Linux Binaries......................................4 1.1.2 MacOS Binaries - Updated...................................4 1.1.3 Installation From Source/Binaries downloader (Mac OSX/Linux)...............4 1.2 Template based modes: PSI/TM-Coffee and Expresso.........................5 1.2.1 Why do I need BLAST with T-Coffee?.............................6 1.2.2 Using a BLAST local version on Unix.............................6 1.2.3 Using the EBI BLAST client..................................6 1.2.4 Using the NCBI BLAST client.................................7 1.2.5 Using another client.......................................7 1.3 Troubleshooting.............................................7 1.3.1 Third party packages......................................7 1.3.2 M-Coffee parameters......................................9 1.3.3 Structural modes (using PDB)................................. 10 1.3.4 R-Coffee associated packages................................. 10 2 Quick Start Regressive Algorithm 11 2.1 Introduction............................................... 11 2.2 Installation from source......................................... 12 2.3 Examples................................................. 12 2.3.1 Fast and accurate........................................ 12 2.3.2 Slower and more accurate.................................... 12 2.3.3 Very Fast........................................... -
ENCODE Genome-Wide Data on the UCSC Genome Browser Melissa Cline ENCODE Data Coordination Center (DCC) UC Santa Cruz
ENCODE Genome-Wide Data on the UCSC Genome Browser Melissa Cline ENCODE Data Coordination Center (DCC) UC Santa Cruz http://encodeproject.org Slides at http://genome-preview.ucsc.edu/ What is ENCODE? • International consortium project with the goal of cataloguing the functional regions of the human genome GTTTGCCATCTTTTG! CTGCTCTAGGGAATC" CAGCAGCTGTCACCA" TGTAAACAAGCCCAG" GCTAGACCAGTTACC" CTCATCATCTTAGCT" GATAGCCAGCCAGCC" ACCACAGGCATGAGT" • A gold mine of experimental data for independent researchers with available disk space ENCODE covers diverse regulatory processes ENCODE experiments are planned for integrative analysis Example of ENCODE data Genes Dnase HS Chromatin Marks Chromatin State Transcription Factor Binding Transcription RNA Binding Translation ENCODE tracks on the UCSC Genome Browser ENCODE tracks marked with the NHGRI helix There are currently 2061 ENCODE experiments at the ENCODE DCC How to find the data you want Finding ENCODE tracks the hard way A better way to find ENCODE tracks Finding ENCODE metadata descriptions Visualizing: Genome Browser tricks that every ENCODE user should know Turning ENCODE subtracks and views on and off Peaks Signal View on/off Subtrack on/off Right-click to the subtrack display menu Subtrack Drag and Drop Sessions: the easy way to save and share your work Downloading data with less pain 1. Via the Downloads button on the track details page 2. Via the File Selection tool Publishing: the ENCODE data release policy Every ENCODE subtrack has a “Restricted Until” date Key points of the ENCODE data release policy • Anyone is free to download and analyze data. • One cannot submit publications involving ENCODE data unless – the data has been at the ENCODE DCC for at least nine months, or – the data producers have published on the data, or – the data producers have granted permission to publish. -
How to Generate a Publication-Quality Multiple Sequence Alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012)
Tutorial: How to generate a publication-quality multiple sequence alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012) 1) Get your sequences in FASTA format: • Go to the NCBI website; find your sequences and display them in FASTA format. Each sequence should look like this (http://www.ncbi.nlm.nih.gov/protein/6678177?report=fasta): >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG 2) In a text editor, paste all your sequences together (in the order that you would like them to appear in the end). It should look like this: >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG >gi|151554658|gb|AAI47965.1| STX3 protein [Bos taurus] MKDRLEQLKAKQLTQDDDTDEVEIAVDNTAFMDEFFSEIEETRVNIDKISEHVEEAKRLYSVILSAPIPE PKTKDDLEQLTTEIKKRANNVRNKLKSMERHIEEDEVQSSADLRIRKSQHSVLSRKFVEVMTKYNEAQVD FRERSKGRIQRQLEITGKKTTDEELEEMLESGNPAIFTSGIIDSQISKQALSEIEGRHKDIVRLESSIKE LHDMFMDIAMLVENQGEMLDNIELNVMHTVDHVEKAREETKRAVKYQGQARKKLVIIIVIVVVLLGILAL IIGLSVGLK -
No Evidence for Recent Selection at FOXP2 Among Diverse Human Populations
Article No Evidence for Recent Selection at FOXP2 among Diverse Human Populations Graphical Abstract Authors Elizabeth Grace Atkinson, Amanda Jane Audesse, Julia Adela Palacios, Dean Michael Bobo, Ashley Elizabeth Webb, Sohini Ramachandran, Brenna Mariah Henn Correspondence [email protected] (E.G.A.), [email protected] (B.M.H.) In Brief An in-depth examination of diverse sets of human genomes argues against a recent selective evolutionary sweep of FOXP2, a gene that was believed to be critical for speech evolution in early hominins. Highlights d No support for positive selection at FOXP2 in large genomic datasets d Sample composition and genomic scale significantly affect selection scans d An intronic ROI within FOXP2 is expressed in human brain cells and cortical tissue d This ROI contains a large amount of constrained, human- specific polymorphisms Atkinson et al., 2018, Cell 174, 1424–1435 September 6, 2018 ª 2018 Elsevier Inc. https://doi.org/10.1016/j.cell.2018.06.048 Article No Evidence for Recent Selection at FOXP2 among Diverse Human Populations Elizabeth Grace Atkinson,1,8,9,10,* Amanda Jane Audesse,2,3 Julia Adela Palacios,4,5 Dean Michael Bobo,1 Ashley Elizabeth Webb,2,6 Sohini Ramachandran,4 and Brenna Mariah Henn1,7,* 1Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, USA 2Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA 3Neuroscience Graduate Program, Brown University, Providence, RI 02912, USA 4Department of Ecology and Evolutionary -
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. -
Identification of Transcribed Sequences in Arabidopsis Thaliana by Using High-Resolution Genome Tiling Arrays
Identification of transcribed sequences in Arabidopsis thaliana by using high-resolution genome tiling arrays Viktor Stolc*†‡§, Manoj Pratim Samanta‡§¶, Waraporn Tongprasitʈ, Himanshu Sethiʈ, Shoudan Liang*, David C. Nelson**, Adrian Hegeman**, Clark Nelson**, David Rancour**, Sebastian Bednarek**, Eldon L. Ulrich**, Qin Zhao**, Russell L. Wrobel**, Craig S. Newman**, Brian G. Fox**, George N. Phillips, Jr.**, John L. Markley**, and Michael R. Sussman**†† *Genome Research Facility, National Aeronautics and Space Administration Ames Research Center, Moffett Field, CA 94035; †Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520; ¶Systemix Institute, Cupertino, CA 94035; ʈEloret Corporation at National Aeronautics and Space Administration Ames Research Center, Moffett Field, CA 94035; and **Center for Eukaryotic Structural Genomics, University of Wisconsin, Madison, WI 53706 Edited by Sidney Altman, Yale University, New Haven, CT, and approved January 28, 2005 (received for review November 4, 2004) Using a maskless photolithography method, we produced DNA Genome-wide tiling arrays can overcome many of the shortcom- oligonucleotide microarrays with probe sequences tiled through- ings of the previous approaches by comprehensively probing out the genome of the plant Arabidopsis thaliana. RNA expression transcription in all regions of the genome. This technology has was determined for the complete nuclear, mitochondrial, and been used successfully on different organisms (5–12). A recent chloroplast genomes by tiling 5 million 36-mer probes. These study on A. thaliana reported measuring transcriptional activities probes were hybridized to labeled mRNA isolated from liquid of four different cell lines by using 25-mer-based tiling arrays that grown T87 cells, an undifferentiated Arabidopsis cell culture line. -
Databases/Resources on the Web
Jon K. Lærdahl, Structural Bioinforma�cs Databases/Resources on the web Jon K. Lærdahl [email protected] Jon K. Lærdahl, A lot of biological databases Structural Bioinforma�cs available on the web... MetaBase, the database of biological bioinforma�cs.ca – links directory databases (1801 entries) (620 databases) -‐ h�p://metadatabase.org -‐ h�p://bioinforma�cs.ca/links_directory Jon K. Lærdahl, Structural Bioinforma�cs btw, the bioinforma�cs.ca links directory is an excellent resource bioinforma�cs.ca – links directory h�p://bioinforma�cs.ca/links_directory Currently 1459 tools 620 databases 164 “resources” The problem is not to find a tool or database, but to know what is “gold” and what is “junk” Jon K. Lærdahl, Some important centres for Structural Bioinforma�cs bioinforma�cs Na�onal Center for Biotechnology Informa�on (NCBI) – part of the US Na�onal Library of Medicine (NLM), a branch of the Na�onal Ins�tutes of Health – located in Bethesda, Maryland European Bioinforma�cs Ins�tute (EMBL-‐EBI) – part of part of European Molecular Biology Laboratory (EMBL) – located in Hinxton, Cambridgeshire, UK Jon K. Lærdahl, NCBI databases Structural Bioinforma�cs Provided the GenBank DNA sequence database since 1992 Online Mendelian Inheritance in Man (OMIM) -‐ known diseases with a gene�c component and links to genes – started early 1960s as a book – online version, OMIM, since 1987 – on the WWW by NCBI in 1995 – currently >22,000 entries (14,400 genes) EST -‐ nucleo�de database subset that contains only Expressed Sequence Tag -
The UCSC Genome Browser Database: 2021 Update Jairo Navarro Gonzalez 1,*, Ann S
D1046–D1057 Nucleic Acids Research, 2021, Vol. 49, Database issue Published online 22 November 2020 doi: 10.1093/nar/gkaa1070 The UCSC Genome Browser database: 2021 update Jairo Navarro Gonzalez 1,*, Ann S. Zweig1, Matthew L. Speir1, Daniel Schmelter1, Kate R. Rosenbloom1, Brian J. Raney1, Conner C. Powell1, Luis R. Nassar1, Nathan D. Maulding1, Christopher M. Lee 1, Brian T. Lee 1,AngieS.Hinrichs1, Alastair C. Fyfe1, Jason D. Fernandes1, Mark Diekhans 1, Hiram Clawson1, Jonathan Casper1, Anna Benet-Pages` 1,2, Galt P. Barber1, David Haussler1,3, Robert M. Kuhn1, Maximilian Haeussler1 and W. James Kent1 Downloaded from https://academic.oup.com/nar/article/49/D1/D1046/5998393 by guest on 27 September 2021 1Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA, 2Medical Genetics Center (MGZ), Munich, Germany and 3Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA Received September 21, 2020; Revised October 19, 2020; Editorial Decision October 20, 2020; Accepted November 18, 2020 ABSTRACT pires to quickly incorporate and contextualize vast amounts of genomic information. For more than two decades, the UCSC Genome Apart from incorporating data from researchers and con- Browser database (https://genome.ucsc.edu)has sortia, the Browser also provides tools available for users to provided high-quality genomics data visualization view and compare their own data with ease. Custom tracks and genome annotations to the research community. allow users to quickly view a dataset, and track hubs allow As the field of genomics grows and more data be- users to extensively organize their data and share it privately come available, new modes of display are required using a URL. -
Introduction to Linux for Bioinformatics – Part II Paul Stothard, 2006-09-20
Introduction to Linux for bioinformatics – part II Paul Stothard, 2006-09-20 In the previous guide you learned how to log in to a Linux account, and you were introduced to some basic Linux commands. This section covers some more advanced commands and features of the Linux operating system. It also introduces some command-line bioinformatics programs. One important aspect of using a Linux system from a Windows or Mac environment that was not discussed in the previous section is how to transfer files between computers. For example, you may have a collection of sequence records on your Windows desktop that you wish to analyze using a Linux command-line program. Alternatively, you may want to transfer some sequence analysis results from a Linux system to your Mac so that you can add them to a PowerPoint presentation. Transferring files between Mac OS X and Linux Recall that Mac OS X includes a Terminal application (located in the Applications >> Utilities folder), which can be used to log in to other systems. This terminal can also be used to transfer files, thanks to the scp command. Try transferring a file from your Mac to your Linux account using the Terminal application: 1. Launch the Terminal program. 2. Instead of logging in to your Linux account, use the same basic commands you learned in the previous section (pwd, ls, and cd) to navigate your Mac file system. 3. Switch to your home directory on the Mac using the command cd ~ 4. Create a text file containing your home directory listing using ls -l > myfiles.txt 5. -
Efficient Storage and Analysis of Genome Data in Relational Database Systems
Efficient Storage and Analysis of Genome Data in Relational Database Systems D I S S E R T A T I O N zur Erlangung des akademischen Grades Doktoringenieur (Dr.-Ing.) angenommen durch die Fakultät für Informatik der Otto-von-Guericke-Universität Magdeburg von M.Sc. Sebastian Dorok geb. am 09.10.1986 in Haldensleben Gutachterinnen/Gutachter Prof. Dr. Gunter Saake Prof. Dr. Jens Teubner Prof. Dr. Ralf Hofestädt Magdeburg, den 27.04.2017 Dorok, Sebastian: Efficient storage and analysis of genome data in relational database systems Dissertation, University of Magdeburg, 2017. Abstract Genome analysis allows researchers to reveal insights about the genetic makeup of living organisms. In the near future, genome analysis will become a key means in the detection and treatment of diseases that are based on variations of the genetic makeup. To this end, powerful variant detection tools were developed or are still under development. However, genome analysis faces a large data deluge. The amounts of data that are produced in a typical genome analysis experiment easily exceed several 100 gigabytes. At the same time, the number of genome analysis experiments increases as the costs drop. Thus, the reliable and efficient management and analysis of large amounts of genome data will likely become a bottleneck, if we do not improve current genome data management and analysis solutions. Currently, genome data management and analysis relies mainly on flat-file based storage and command-line driven analysis tools. Such approaches offer only limited data man- agement capabilities that can hardly cope with future requirements such as annotation management or provenance tracking. -
A Tool to Sanity Check and If Needed Reformat FASTA Files
bioRxiv preprint doi: https://doi.org/10.1101/024448; this version posted August 13, 2015. 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. Fasta-O-Matic: a tool to sanity check and if needed reformat FASTA files Jennifer Shelton Kansas State University August 11, 2015 Abstract As the shear volume of bioinformatic sequence data increases the only way to take advantage of this content is to more completely automate ro- bust analysis workflows. Analysis bottlenecks are often mundane and overlooked processing steps. Idiosyncrasies in reading and/or writing bioinformatics file formats can halt or impair analysis workflows by in- terfering with the transfer of data from one informatics tools to another. Fasta-O-Matic automates handling of common but minor format issues that otherwise may halt pipelines. The need for automation must be balanced by the need for manual confirmation that any formatting error is actually minor rather than indicative of a corrupt data file. To that end Fasta-O-Matic reports any issues detected to the user with optionally color coded and quiet or verbose logs. Fasta-O-Matic can be used as a general pre-processing tool in bioin- formatics workflows (e.g. to automatically wrap FASTA files so that they can be read by BioPerl). It was also developed as a sanity check for bioinformatic core facilities that tend to repeat common analysis steps on FASTA files received from disparate sources. -
The UCSC Genome Browser Database: Update 2011 Pauline A
D876–D882 Nucleic Acids Research, 2011, Vol. 39, Database issue Published online 18 October 2010 doi:10.1093/nar/gkq963 The UCSC Genome Browser database: update 2011 Pauline A. Fujita1,*, Brooke Rhead1, Ann S. Zweig1, Angie S. Hinrichs1, Donna Karolchik1, Melissa S. Cline1, Mary Goldman1, Galt P. Barber1, Hiram Clawson1, Antonio Coelho1, Mark Diekhans1, Timothy R. Dreszer1, Belinda M. Giardine2, Rachel A. Harte1, Jennifer Hillman-Jackson1, Fan Hsu1, Vanessa Kirkup1, Robert M. Kuhn1, Katrina Learned1, Chin H. Li1, Laurence R. Meyer1, Andy Pohl1,3, Brian J. Raney1, Kate R. Rosenbloom1, Kayla E. Smith1, David Haussler1,4 and W. James Kent1 1Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Downloaded from (UCSC), Santa Cruz, CA 95064, 2Center for Comparative Genomics and Bioinformatics, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA, 3Centre for Genomic Regulation (CRG), Barcelona, Spain and 4Howard Hughes Medical Institute, UCSC, Santa Cruz, CA 95064, USA Received September 15, 2010; Accepted September 30, 2010 nar.oxfordjournals.org ABSTRACT differs among species, with recent assemblies of the human The University of California, Santa Cruz Genome genome being the most richly annotated. The Genome Browser (http://genome.ucsc.edu) offers online Browser contains mapping and sequencing annotation tracks describing assembly, gap and GC percent details access to a database of genomic sequence and at Health Sciences Center Library on February 4, 2011 annotation data for a wide variety of organisms. for all assemblies. Most organisms also have tracks con- taining alignments of RefSeq genes (3,4), mRNAs and The Browser also has many tools for visualizing, ESTs from GenBank (5) as well as gene and gene predic- comparing and analyzing both publicly available tion tracks such as Ensembl Genes (6).