Need and Role of Scala Implementations in Bioinformatics
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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 -
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. -
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. -
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. -
Understanding the Origins, Dispersal, and Evolution of Bonamia Species (Phylum Haplosporidia) Based on Genetic Analyses of Ribosomal RNA Gene Regions
W&M ScholarWorks Dissertations, Theses, and Masters Projects Theses, Dissertations, & Master Projects 2011 Understanding the Origins, Dispersal, and Evolution of Bonamia Species (Phylum Haplosporidia) Based on Genetic Analyses of Ribosomal RNA Gene Regions Kristina M. Hill College of William and Mary - Virginia Institute of Marine Science Follow this and additional works at: https://scholarworks.wm.edu/etd Part of the Developmental Biology Commons, Evolution Commons, and the Molecular Biology Commons Recommended Citation Hill, Kristina M., "Understanding the Origins, Dispersal, and Evolution of Bonamia Species (Phylum Haplosporidia) Based on Genetic Analyses of Ribosomal RNA Gene Regions" (2011). Dissertations, Theses, and Masters Projects. Paper 1539617909. https://dx.doi.org/doi:10.25773/v5-a0te-9079 This Thesis is brought to you for free and open access by the Theses, Dissertations, & Master Projects at W&M ScholarWorks. It has been accepted for inclusion in Dissertations, Theses, and Masters Projects by an authorized administrator of W&M ScholarWorks. For more information, please contact [email protected]. Understanding the Origins, Dispersal, and Evolution of Bonamia Species (Phylum Haplosporidia) Based on Genetic Analyses of Ribosomal RNA Gene Regions A Thesis Presented to The Faculty of the School of Marine Science The College of William and Mary in Virginia In Partial Fulfillment of the Requirements for the Degree of Master of Science by Kristina M. Hill 2011 APPROVAL SHEET This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science CH-s 7n - "UuUL ' Kristina Marie Hill Approved, May 2011 w. n Eugene M. Burreson, Ph.D Advisor Kimberly S. Reece, Ph.D. -
MATLAB Software for Extracting Protein Name and Sequence Information from FASTA Formatted Proteome File
MATLAB software for extracting protein name and sequence information from FASTA formatted proteome file Wenfa Ng Unaffiliated researcher, Singapore, Email: [email protected] Abstract FASTA file format is a common file type for distributing proteome information, especially those obtained from Uniprot. While MATLAB could automatically read fasta files using the built-in function, fastaread, important information such as protein name and organism name remain enmeshed in a character array. Hence, difficulty exists in automatic extraction of protein names from fasta proteome file to help in building a database with fields comprising protein name and its amino acid sequence. The objective of this work was in developing a MATLAB software that could automatically extract protein name and amino acid sequence information from fasta proteome file and assign them to a new database that comprises fields such as protein name, amino acid sequence, number of amino acid residues, molecular weight of protein and nucleotide sequence of protein. Information on number of amino acid residues came from the use of the length built-in function in MATLAB analyzing the length of the amino acid sequence of a protein. The final two fields were provided by MATLAB built-in functions molweight and aa2nt, respectively. Molecular weight of proteins is useful for a variety of applications while nucleotide sequence is essential for gene synthesis applications in molecular cloning. Finally, the MATLAB software is also equipped with an error check function to help detect letters in the amino acid sequence that are not part of the family of 20 natural amino acids. Sequences with such letters would constitute as error inputs to molweight and aa2nt, and would not be processed. -
The Shared Genomic Architecture of Human Nucleolar Organizer Regions
The shared genomic architecture of human nucleolar organizer regions Ioanna Floutsakou1,4, Saumya Agrawal2,4, Thong T. Nguyen3,4, Cathal Seoighe3, Austen R.D. Ganley2, and Brian McStay1,5 1 Centre for Chromosome Biology, School of Natural Sciences, National University of Ireland, Galway, Galway, Ireland. 2 Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand. 3 School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Galway, Ireland. 4 These authors contributed equally to this work. 5Corresponding author: E-mail [email protected] SUPPLEMENTAL INFORMATION Supplemental Methods (page 3) Supplemental Tables (page 12) Table 1. Putative gene models from the DJ Table 2. Putative gene models from the PJ Table 3. Segmentally duplicated regions from the DJ contig Table 4. Segmentally duplicated regions from the PJ contig Table 5. Fold increase of DJ sequences present in BAC array clones Table 6. Sequencing statistics for the DJ and PJ cosmids Table 7. Assembly statistics for the DJ and PJ cosmid assemblies Table 8. DJ and PJ primers for mapping, ChIP, and RT-PCR Supplemental Figures Fig. 1. Positions of cosmids and BACs in the DJ Fig. 2. Positions of cosmids and BACs in the PJ Fig. 3. PJ sequences are conserved among the acrocentric chromosomes Fig. 4. Structure of DJ and PJ cosmids Fig. 5. Workflow for junction verification mapping pipeline Fig. 6. Alu and 147bp ACRO1 repeat inter-chromosomal PJ variation Fig. 7. Construction scheme for the DJ and PJ contigs Fig. 8. Repeat composition of the DJ and PJ regions. Fig. 9. 48bp CER repeats are found distal to the NOR on all five acrocentric chromosomes. -
Biology 3055
Laboratory Manual Biology 3055 Table of Contents Page(s) Laboratory Syllabus…………………………………………….……3 - 7 Lab Calendar……………………………………………………..7 Laboratory 1………………………………………………………... 8 - 18 GenBank Entry……………………………………………… …10 SwissProt Entry…………………………………………… 11, 12 Sequence Manipulation Suite…………………………………13 ClustalW Submission Form……………………………………14 Laboratory 2………………………………………………………..19 - 23 Laboratory 3………………………………………………..………24 - 28 Tool Bar……………………………………………………….…25 Ribbon Color Panel…………………………………………….26 Control Panel…………………………………………………...27 Laboratory 4………………………………………………………..29 -30 Laboratory 5…………………………………………………………….31 Glossary……………………………………………………………32 - 37 Created in 2003 by: April Bednarski Advised by: Professor Himadri Pakrasi Funded by a grant from: Howard Hughes Medical Institute to Washington University 2 Biology 3055 Laboratory Syllabus I. LAB INSTRUCTORS Instructors: Jessica Wagenseil, Kathy Hafer, April Bednarski II. COURSE DESCRIPTION Welcome to the laboratory for Biology 3050. This lab is designed around bioinformatics tools that are widely used in biomedical research today. Over the past few years, the number of freely available software programs and web-based research tools has increased dramatically. Knowing how to use these tools is very important to research and health professionals in order to access and interpret the constantly growing amounts of genomic and proteomic information. These bioinformatics tools as well as genomic information are freely available on the web to anyone who knows how to access them. Biology 3055 introduces these computer-based research tools in a context that reinforces concepts presented during the Biology 3050 lecture. III. PROJECTS You will be given your own gene to study during the 5 lab sessions. This gene will be the focus of all your guide sheets and final report. There will be one other person working on the same gene in each lab section. -
Tracheophyte Genomes Keep Track of the Deep Evolution of the 2 Caulimoviridae 3 4 Authors 5 Seydina Diop1, Andrew D.W
bioRxiv preprint doi: https://doi.org/10.1101/158972; this version posted July 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Tracheophyte genomes keep track of the deep evolution of the 2 Caulimoviridae 3 4 Authors 5 Seydina Diop1, Andrew D.W. Geering2, Françoise Alfama-Depauw1, Mikaël Loaec1, Pierre-Yves 6 Teycheney3 and Florian Maumus1* 7 8 Affiliations 9 1 URGI, INRA, Université Paris-Saclay, 78026 Versailles, France; 10 2 Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, GPO Box 11 267, Brisbane, Queensland 4001, Australia 12 3 UMR AGAP, CIRAD, INRA, SupAgro, 97130 Capesterre Belle-Eau, France 13 14 Corresponding author 15 Florian Maumus 16 URGI-INRA 17 RD10 route de Saint Cyr 18 78026, Versailles 19 France 20 +33 1 30 83 31 74 21 [email protected] 22 23 24 1 bioRxiv preprint doi: https://doi.org/10.1101/158972; this version posted July 21, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 25 Abstract 26 Endogenous viral elements (EVEs) are viral sequences that are integrated in the nuclear genomes of 27 their hosts and are signatures of viral infections that may have occurred millions of years ago. The 28 study of EVEs, coined paleovirology, provides important insights into virus evolution. The 29 Caulimoviridae is the most common group of EVEs in plants, although their presence has often been 30 overlooked in plant genome studies.