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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........................................... -
Comparative Analysis of Multiple Sequence Alignment Tools
I.J. Information Technology and Computer Science, 2018, 8, 24-30 Published Online August 2018 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2018.08.04 Comparative Analysis of Multiple Sequence Alignment Tools Eman M. Mohamed Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected]. Hamdy M. Mousa, Arabi E. keshk Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected], [email protected]. Received: 24 April 2018; Accepted: 07 July 2018; Published: 08 August 2018 Abstract—The perfect alignment between three or more global alignment algorithm built-in dynamic sequences of Protein, RNA or DNA is a very difficult programming technique [1]. This algorithm maximizes task in bioinformatics. There are many techniques for the number of amino acid matches and minimizes the alignment multiple sequences. Many techniques number of required gaps to finds globally optimal maximize speed and do not concern with the accuracy of alignment. Local alignments are more useful for aligning the resulting alignment. Likewise, many techniques sub-regions of the sequences, whereas local alignment maximize accuracy and do not concern with the speed. maximizes sub-regions similarity alignment. One of the Reducing memory and execution time requirements and most known of Local alignment is Smith-Waterman increasing the accuracy of multiple sequence alignment algorithm [2]. on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the Table 1. Pairwise vs. multiple sequence alignment most well-known programs (CLUSTAL-OMEGA, PSA MSA MAFFT, BROBCONS, KALIGN, RETALIGN, and Compare two biological Compare more than two MUSCLE). -
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 -
DNA Sequencing and Sorting: Identifying Genetic Variations
BioMath DNA Sequencing and Sorting: Identifying Genetic Variations Student Edition Funded by the National Science Foundation, Proposal No. ESI-06-28091 This material was prepared with the support of the National Science Foundation. However, any opinions, findings, conclusions, and/or recommendations herein are those of the authors and do not necessarily reflect the views of the NSF. At the time of publishing, all included URLs were checked and active. We make every effort to make sure all links stay active, but we cannot make any guaranties that they will remain so. If you find a URL that is inactive, please inform us at [email protected]. DIMACS Published by COMAP, Inc. in conjunction with DIMACS, Rutgers University. ©2015 COMAP, Inc. Printed in the U.S.A. COMAP, Inc. 175 Middlesex Turnpike, Suite 3B Bedford, MA 01730 www.comap.com ISBN: 1 933223 71 5 Front Cover Photograph: EPA GULF BREEZE LABORATORY, PATHO-BIOLOGY LAB. LINDA SHARP ASSISTANT This work is in the public domain in the United States because it is a work prepared by an officer or employee of the United States Government as part of that person’s official duties. DNA Sequencing and Sorting: Identifying Genetic Variations Overview Each of the cells in your body contains a copy of your genetic inheritance, your DNA which has been passed down to you, one half from your biological mother and one half from your biological father. This DNA determines physical features, like eye color and hair color, and can determine susceptibility to medical conditions like hypertension, heart disease, diabetes, and cancer. -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
"Phylogenetic Analysis of Protein Sequence Data Using The
Phylogenetic Analysis of Protein Sequence UNIT 19.11 Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program Antonis Rokas1 1Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee ABSTRACT Phylogenetic analysis is the study of evolutionary relationships among molecules, phenotypes, and organisms. In the context of protein sequence data, phylogenetic analysis is one of the cornerstones of comparative sequence analysis and has many applications in the study of protein evolution and function. This unit provides a brief review of the principles of phylogenetic analysis and describes several different standard phylogenetic analyses of protein sequence data using the RAXML (Randomized Axelerated Maximum Likelihood) Program. Curr. Protoc. Mol. Biol. 96:19.11.1-19.11.14. C 2011 by John Wiley & Sons, Inc. Keywords: molecular evolution r bootstrap r multiple sequence alignment r amino acid substitution matrix r evolutionary relationship r systematics INTRODUCTION the baboon-colobus monkey lineage almost Phylogenetic analysis is a standard and es- 25 million years ago, whereas baboons and sential tool in any molecular biologist’s bioin- colobus monkeys diverged less than 15 mil- formatics toolkit that, in the context of pro- lion years ago (Sterner et al., 2006). Clearly, tein sequence analysis, enables us to study degree of sequence similarity does not equate the evolutionary history and change of pro- with degree of evolutionary relationship. teins and their function. Such analysis is es- A typical phylogenetic analysis of protein sential to understanding major evolutionary sequence data involves five distinct steps: (a) questions, such as the origins and history of data collection, (b) inference of homology, (c) macromolecules, developmental mechanisms, sequence alignment, (d) alignment trimming, phenotypes, and life itself. -
Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods
International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019 Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods Arunima Mishra, B. K. Tripathi, S. S. Soam MSA is a well-known method of alignment of three or more Abstract: Protein Multiple sequence alignment (MSA) is a biological sequences. Multiple sequence alignment is a very process, that helps in alignment of more than two protein intricate problem, therefore, computation of exact MSA is sequences to establish an evolutionary relationship between the only feasible for the very small number of sequences which is sequences. As part of Protein MSA, the biological sequences are not practical in real situations. Dynamic programming as used aligned in a way to identify maximum similarities. Over time the sequencing technologies are becoming more sophisticated and in pairwise sequence method is impractical for a large number hence the volume of biological data generated is increasing at an of sequences while performing MSA and therefore the enormous rate. This increase in volume of data poses a challenge heuristic algorithms with approximate approaches [7] have to the existing methods used to perform effective MSA as with the been proved more successful. Generally, various biological increase in data volume the computational complexities also sequences are organized into a two-dimensional array such increases and the speed to process decreases. The accuracy of that the residues in each column are homologous or having the MSA is another factor critically important as many bioinformatics same functionality. Many MSA methods were developed over inferences are dependent on the output of MSA. -
HMMER User's Guide
HMMER User's Guide Biological sequence analysis using pro®le hidden Markov models http://hmmer.wustl.edu/ Version 2.1.1; December 1998 Sean Eddy Dept. of Genetics, Washington University School of Medicine 4566 Scott Ave., St. Louis, MO 63110, USA [email protected] With contributions by Ewan Birney ([email protected]) Copyright (C) 1992-1998, Washington University in St. Louis. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are retained on all copies. The HMMER software package is a copyrighted work that may be freely distributed and modi®ed under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Some versions of HMMER may have been obtained under specialized commercial licenses from Washington University; for details, see the ®les COPYING and LICENSE that came with your copy of the HMMER software. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Appendix for a copy of the full text of the GNU General Public License. 1 Contents 1 Tutorial 5 1.1 The programs in HMMER . 5 1.2 Files used in the tutorial . 6 1.3 Searching a sequence database with a single pro®le HMM . 6 HMM construction with hmmbuild . 7 HMM calibration with hmmcalibrate . 7 Sequence database search with hmmsearch . 8 Searching major databases like NR or SWISSPROT . -
Homology & Alignment
Protein Bioinformatics Johns Hopkins Bloomberg School of Public Health 260.655 Thursday, April 1, 2010 Jonathan Pevsner Outline for today 1. Homology and pairwise alignment 2. BLAST 3. Multiple sequence alignment 4. Phylogeny and evolution Learning objectives: homology & alignment 1. You should know the definitions of homologs, orthologs, and paralogs 2. You should know how to determine whether two genes (or proteins) are homologous 3. You should know what a scoring matrix is 4. You should know how alignments are performed 5. You should know how to align two sequences using the BLAST tool at NCBI 1 Pairwise sequence alignment is the most fundamental operation of bioinformatics • It is used to decide if two proteins (or genes) are related structurally or functionally • It is used to identify domains or motifs that are shared between proteins • It is the basis of BLAST searching (next topic) • It is used in the analysis of genomes myoglobin Beta globin (NP_005359) (NP_000509) 2MM1 2HHB Page 49 Pairwise alignment: protein sequences can be more informative than DNA • protein is more informative (20 vs 4 characters); many amino acids share related biophysical properties • codons are degenerate: changes in the third position often do not alter the amino acid that is specified • protein sequences offer a longer “look-back” time • DNA sequences can be translated into protein, and then used in pairwise alignments 2 Find BLAST from the home page of NCBI and select protein BLAST… Page 52 Choose align two or more sequences… Page 52 Enter the two sequences (as accession numbers or in the fasta format) and click BLAST. -
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 -
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. -
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.