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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........................................... -
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 -
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. -
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. -
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 Uniprot Knowledgebase BLAST
Introduction to bioinformatics The UniProt Knowledgebase BLAST UniProtKB Basic Local Alignment Search Tool A CRITICAL GUIDE 1 Version: 1 August 2018 A Critical Guide to BLAST BLAST Overview This Critical Guide provides an overview of the BLAST similarity search tool, Briefly examining the underlying algorithm and its rise to popularity. Several WeB-based and stand-alone implementations are reviewed, and key features of typical search results are discussed. Teaching Goals & Learning Outcomes This Guide introduces concepts and theories emBodied in the sequence database search tool, BLAST, and examines features of search outputs important for understanding and interpreting BLAST results. On reading this Guide, you will Be aBle to: • search a variety of Web-based sequence databases with different query sequences, and alter search parameters; • explain a range of typical search parameters, and the likely impacts on search outputs of changing them; • analyse the information conveyed in search outputs and infer the significance of reported matches; • examine and investigate the annotations of reported matches, and their provenance; and • compare the outputs of different BLAST implementations and evaluate the implications of any differences. finding short words – k-tuples – common to the sequences Being 1 Introduction compared, and using heuristics to join those closest to each other, including the short mis-matched regions Between them. BLAST4 was the second major example of this type of algorithm, From the advent of the first molecular sequence repositories in and rapidly exceeded the popularity of FastA, owing to its efficiency the 1980s, tools for searching dataBases Became essential. DataBase searching is essentially a ‘pairwise alignment’ proBlem, in which the and Built-in statistics. -
A Tool to Sanity Check and If Needed Reformat FASTA Files
ManuscriptbioRxiv preprint doi: https://doi.org/10.1101/024448; this version posted August 21, 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 Click here to download Manuscript: bmc_article.tex aCC-BY 4.0 International license. Click here to view linked References Shelton and Brown 1 2 3 4 5 RESEARCH 6 7 8 9 Fasta-O-Matic: a tool to sanity check and if 10 11 needed reformat FASTA files 12 13 Jennifer M Shelton1 and Susan J Brown1* 14 15 *Correspondence: [email protected] 16 1KSU/K-INBRE Bioinformatics Abstract 17 Center, Division of Biology, 18 Kansas State University, Background: As the sheer volume of bioinformatic sequence data increases, the 19 Manhattan, KS, USA only way to take advantage of this content is to more completely automate Full list of author information is 20 available at the end of the article robust analysis workflows. Analysis bottlenecks are often mundane and 21 overlooked processing steps. Idiosyncrasies in reading and/or writing 22 bioinformatics file formats can halt or impair analysis workflows by interfering 23 with the transfer of data from one informatics tools to another. 24 Results: Fasta-O-Matic automates handling of common but minor formatissues 25 that otherwise may halt pipelines. The need for automation must be balanced by 26 the need for manual confirmation that any formatting error is actually minor 27 28 rather than indicative of a corrupt data file. -
Multiple Alignments, Blast and Clustalw
Multiple alignments, blast and clustalW 1. Blast idea: a. Filter out low complexity regions (tandem repeats… that sort of thing) [optional] b. Compile list of high-scoring strings (words, in BLAST jargon) of fixed length in query (threshold T) c. Extend alignments (highs scoring pairs) d. Report High Scoring pairs: score at least S (or an E value lower than some threshold) 2. Multiple Sequence Alignments: a. Attempts to extend dynamic programming techniques to multiple sequences run into problems after only a few proteins (8 average proteins were a problem early in 2000s) b. Heuristic approach c. Idea (Progressive Approach): i. homologous sequences are evolutionarily related ii. Build multiple alignment by series of pairwise alignments based off some phylogenetic tree (the initial tree or the guide tree ) iii. Add in more distantly related sequences d. Progressive Sequence alignment example: 1. NYLS & NKYLS: N YLS N(K|-)YLS NKYLS 2. NFS & NFLS: N YLS NF S NF(L|-)S NKYLS NFLS 3. N(K|-)YLS & NF(L|-)S N YLS N(K|-)(Y|F)(L|-)S NKYLS N YLS N F S N FLS e. Assessment: i. Works great for fairly similar sequences ii. Not so well for highly divergent ones f. Two Problems: i. local minimum problem: Algorithm greedily adds sequences based off of tree— might miss global solution ii. Alignment parameters: Mistakes (misaligned regions) early in procedure can’t be corrected later. g. ClustalW does multiple alignments and attempts to solve alignment parameter problem i. gap costs are dynamically varied based on position and amino acid ii. weight matrices are changed as the level of divergence between sequence increases (say going from PAM30 -> PAM60) iii. -
The FASTA Program Package Introduction
fasta-36.3.8 December 4, 2020 The FASTA program package Introduction This documentation describes the version 36 of the FASTA program package (see W. R. Pearson and D. J. Lipman (1988), “Improved Tools for Biological Sequence Analysis”, PNAS 85:2444-2448, [17] W. R. Pearson (1996) “Effective protein sequence comparison” Meth. Enzymol. 266:227- 258 [15]; and Pearson et. al. (1997) Genomics 46:24-36 [18]. Version 3 of the FASTA packages contains many programs for searching DNA and protein databases and for evaluating statistical significance from randomly shuffled sequences. This document is divided into four sections: (1) A summary overview of the programs in the FASTA3 package; (2) A guide to using the FASTA programs; (3) A guide to installing the programs and databases. Section (4) provides answers to some Frequently Asked Questions (FAQs). In addition to this document, the changes v36.html, changes v35.html and changes v34.html files list functional changes to the programs. The readme.v30..v36 files provide a more complete revision history of the programs, including bug fixes. The programs are easy to use; if you are using them on a machine that is administered by someone else, you can focus on sections (1) and (2) to learn how to use the programs. If you are installing the programs on your own machine, you will need to read section (3) carefully. FASTA and BLAST – FASTA and BLAST have the same goal: to identify statistically signifi- cant sequence similarity that can be used to infer homology. The FASTA programs offer several advantages over BLAST: 1. -
Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher.