Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods
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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........................................... -
Multiple Sequence Alignment: a Major Challenge to Large-Scale Phylogenetics
Multiple sequence alignment: a major challenge to large-scale phylogenetics November 18, 2010 · Tree of Life Kevin Liu1, C. Randal Linder2, Tandy Warnow3 1 Postdoctoral Research Associate, Austin, TX, 2 Associate Professor, Austin, TX, 3 Professor of Computer Science, Department of Computer Science, University of Texas at Austin Liu K, Linder CR, Warnow T. Multiple sequence alignment: a major challenge to large-scale phylogenetics. PLOS Currents Tree of Life. 2010 Nov 18 [last modified: 2012 Apr 23]. Edition 1. doi: 10.1371/currents.RRN1198. Abstract Over the last decade, dramatic advances have been made in developing methods for large-scale phylogeny estimation, so that it is now feasible for investigators with moderate computational resources to obtain reasonable solutions to maximum likelihood and maximum parsimony, even for datasets with a few thousand sequences. There has also been progress on developing methods for multiple sequence alignment, so that greater alignment accuracy (and subsequent improvement in phylogenetic accuracy) is now possible through automated methods. However, these methods have not been tested under conditions that reflect properties of datasets confronted by large-scale phylogenetic estimation projects. In this paper we report on a study that compares several alignment methods on a benchmark collection of nucleotide sequence datasets of up to 78,132 sequences. We show that as the number of sequences increases, the number of alignment methods that can analyze the datasets decreases. Furthermore, the most accurate alignment methods are unable to analyze the very largest datasets we studied, so that only moderately accurate alignment methods can be used on the largest datasets. As a result, alignments computed for large datasets have relatively large error rates, and maximum likelihood phylogenies computed on these alignments also have high error rates. -
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). -
"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. -
Ple Sequence Alignment Methods: Evidence from Data
Mul$ple sequence alignment methods: evidence from data Tandy Warnow Alignment Error/Accuracy • SPFN: percentage of homologies in the true alignment that are not recovered (false negave homologies) • SPFP: percentage of homologies in the es$mated alignment that are false (false posi$ve homologies) • TC: total number of columns correctly recovered • SP-score: percentage of homologies in the true alignment that are recovered • Pairs score: 1-(avg of SP-FN and SP-FP) Benchmarks • Simulaons: can control everything, and true alignment is not disputed – Different simulators • Biological: can’t control anything, and reference alignment might not be true alignment – BAliBASE, HomFam, Prefab – CRW (Comparave Ribosomal Website) Alignment Methods (Sample) • Clustal-Omega • MAFFT • Muscle • Opal • Prank/Pagan • Probcons Co-es$maon of trees and alignments • Bali-Phy and Alifritz (stas$cal co-es$maon) • SATe-1, SATe-2, and PASTA (divide-and-conquer co- es$maon) • POY and Beetle (treelength op$mizaon) Other Criteria • Tree topology error • Tree branch length error • Gap length distribu$on • Inser$on/dele$on rao • Alignment length • Number of indels How does the guide tree impact accuracy? • Does improving the accuracy of the guide tree help? • Do all alignment methods respond iden$cally? (Is the same guide tree good for all methods?) • Do the default sengs for the guide tree work well? Alignment criteria • Does the relave performance of methods depend on the alignment criterion? • Which alignment criteria are predic$ve of tree accuracy? • How should we design MSA methods to produce best accuracy? Choice of best MSA method • Does it depend on type of data (DNA or amino acids?) • Does it depend on rate of evolu$on? • Does it depend on gap length distribu$on? • Does it depend on existence of fragments? Katoh and Standley . -
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 . -
Multiple Sequence Alignment Based on Profile Alignment of Intermediate
Multiple Sequence Alignment Based on Profile Alignment of Intermediate Sequences Yue Lu1 and Sing-Hoi Sze1,2 1 Department of Biochemistry & Biophysics 2 Department of Computer Science, Texas A&M University, College Station, TX 77843, USA Abstract. Despite considerable efforts, it remains difficult to obtain accurate multiple sequence alignments. By using additional hits from database search of the input sequences, a few strategies have been pro- posed to significantly improve alignment accuracy, including the con- struction of profiles from the hits while performing profile alignment, the inclusion of high scoring hits into the input sequences, the use of interme- diate sequence search to link distant homologs, and the use of secondary structure information. We develop an algorithm that integrates these strategies to further improve alignment accuracy by modifying the pair- HMM approach in ProbCons to incorporate profiles of intermediate se- quences from database search and utilize secondary structure predictions as in SPEM. We test our algorithm on a few sets of benchmark multi- ple alignments, including BAliBASE, HOMSTRAD, PREFAB and SAB- mark, and show that it significantly outperforms MAFFT and ProbCons, which are among the best multiple alignment algorithms that do not uti- lize additional information, and SPEM, which is among the best multiple alignment algorithms that utilize additional hits from database search. The improvement in accuracy over SPEM can be as much as 5 to 10% when aligning divergent sequences. A software program that implements this approach (ISPAlign) is at http://faculty.cs.tamu.edu/shsze/ispalign. 1 Introduction Although many algorithms have been proposed for multiple sequence alignment (Thompson et al. -
Multiple Sequence Alignments Iain M Wallace, Gordon Blackshields and Desmond G Higgins
Multiple sequence alignments Iain M Wallace, Gordon Blackshields and Desmond G Higgins Multiple sequence alignments are very widely used in all areas ‘progressive alignment’. This was described in various of DNA and protein sequence analysis. The main methods ways by several different groups and resulted in a series of that are still in use are based on ‘progressive alignment’ and programs in the mid to late 1980s that are still in use today date from the mid to late 1980s. Recently, some dramatic [8–12]. Progressive alignment allows large alignments of improvements have been made to the methodology with distantly related sequences to be constructed quickly and respect either to speed and capacity to deal with large numbers simply. It is based on building the full alignment up of sequences or to accuracy. There have also been some progressively, using the branching order of a quick practical advances concerning how to combine three- approximate tree (called the guide tree) to guide the dimensional structural information with primary sequences alignments. It is implemented in the most widely used to give more accurate alignments, when structures are programs (ClustalW [13] and ClustalX [14]), but is also available. used as the optimiser for other programs, such as T-Coffee [15]. The latter is unusual in that it uses the Addresses maximum weight trace [16] or ‘Coffee’ [17] objective The Conway Institute of Biomolecular and Biomedical Research, function, rather than a more conventional dynamic pro- University College Dublin, Ireland gramming sequence distance score. T-Coffee is of great Corresponding author: Higgins, Desmond G ([email protected]) interest not only because of the way it allows heteroge- neous data to be merged in alignments (see 3D-Coffee below) but also as a precursor to the probabilistic-based Current Opinion in Structural Biology 2005, 15:261–266 program PROBCONS [18], which is the most accurate This review comes from a themed issue on method available. -
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. -
Computational Protocol for Assembly and Analysis of SARS-Ncov-2 Genomes
PROTOCOL Computational Protocol for Assembly and Analysis of SARS-nCoV-2 Genomes Mukta Poojary1,2, Anantharaman Shantaraman1, Bani Jolly1,2 and Vinod Scaria1,2 1CSIR Institute of Genomics and Integrative Biology, Mathura Road, Delhi 2Academy of Scientific and Innovative Research (AcSIR) *Corresponding email: MP [email protected]; AS [email protected]; BJ [email protected]; VS [email protected] ABSTRACT SARS-CoV-2, the pathogen responsible for the ongoing Coronavirus Disease 2019 pandemic is a novel human-infecting strain of Betacoronavirus. The outbreak that initially emerged in Wuhan, China, rapidly spread to several countries at an alarming rate leading to severe global socio-economic disruption and thus overloading the healthcare systems. Owing to the high rate of infection of the virus, as well as the absence of vaccines or antivirals, there is a lack of robust mechanisms to control the outbreak and contain its transmission. Rapid advancement and plummeting costs of high throughput sequencing technologies has enabled sequencing of the virus in several affected individuals globally. Deciphering the viral genome has the potential to help understand the epidemiology of the disease as well as aid in the development of robust diagnostics, novel treatments and prevention strategies. Towards this effort, we have compiled a comprehensive protocol for analysis and interpretation of the sequencing data of SARS-CoV-2 using easy-to-use open source utilities. In this protocol, we have incorporated strategies to assemble the genome of SARS-CoV-2 using two approaches: reference-guided and de novo. Strategies to understand the diversity of the local strain as compared to other global strains have also been described in this protocol. -
Embnet.News Volume 4 Nr
embnet.news Volume 4 Nr. 3 Page 1 embnet.news Volume 4 Nr 3 (ISSN1023-4144) upon our core expertise in sequence analysis. Editorial Such debates can only be construed as healthy. Stasis can all too easily become stagnation. After some cliff-hanging As well as being the Christmas Bumper issue, this is also recounts and reballots at the AGM there have been changes the after EMBnet AGM Issue. The 11th Annual Business in all of EMBnet's committees. It is to be hoped that new Meeting was organised this year by the Italian Node (CNR- committee members will help galvanise us all into a more Bari) and took place up in the hills at Selva di Fasano at the active phase after a relatively quiet 1997. The fact that the end of September. For us northerners, the concept of "O for financial status of EMBnet is presently very healthy, will a beaker full of the warm south" so affected one of the certainly not impede this drive. Everyone agrees that delegates that he jumped (or was he pushed ?) fully clothed bioinformatics is one of science's growth areas and there is into the hotel swimming pool. Despite the balmy weather nobody better equipped than EMBnet to make solid and the excellent food and wine, we did manage to get a contributions to the field. solid day and a half of work done. The embnet.news editorial board: EMBnet is having to make some difficult choices about what its future direction and purpose should be. Our major source Alan Bleasby of funds is from the EU, but pretty much all countries which Rob Harper are eligible for EU funding have already joined the Robert Herzog organisation. -
Supplementary Figures
SUPPLEMENTARY FIGURES Figure S1. Read-through transcription by RNAPII in Seb1-deficient cells. (A) Average distribution of RNAPII density in wild-type (WT) and Seb1-depleted cells (Pnmt1-seb1), as determined by analysis of ChIP-seq results for all (n = 7005), mRNA (n = 5123), snoRNA (n = 53) and snRNA (n = 7) genes. Light grey rectangles represent open reading frames (mRNA genes) or noncoding sequences (snoRNA and snRNA genes) and black arrows 500 base pairs (bp) of upstream and downstream flanking DNA sequences. The absolute levels of RNAPII binding (y-axis) depend on the number of genes taken into account. Lower values for snoRNA and snRNA reflect the low number of genes present in these categories as compared to mRNA. (B) As in (A) but for (i) tandem genes that are more than 200 bp apart (n = 1425), (ii) tandem genes that are more than 400 bp apart (n = 855), (iii) convergent genes that are more than 200 bp apart (n = 452) as well as (iv) convergent genes that are more than 400 bp apart (n = 208). Figure S2. Seb1 is not the functional ortholog of Nrd1p. (A) Ten-fold serial dilutions of S. cerevisiae wild-type (WT) cells transformed with an empty vector (EV) as well as PGAL1-3XHA-NRD1 cells that were transformed with an EV or constructs that express 3XFLAG-tagged versions of S. pombe Seb1 or S. cerevisiae Nrd1. Cells were spotted on galactose- and glucose-containing media to allow and repress transcription from the PGAL1 promoter, respectively. (B) TOP: Schematic representation of the SNR13-TRS31 genomic environment.