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      T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension

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          Abstract

          This article introduces a new interface for T-Coffee, a consistency-based multiple sequence alignment program. This interface provides an easy and intuitive access to the most popular functionality of the package. These include the default T-Coffee mode for protein and nucleic acid sequences, the M-Coffee mode that allows combining the output of any other aligners, and template-based modes of T-Coffee that deliver high accuracy alignments while using structural or homology derived templates. These three available template modes are Expresso for the alignment of protein with a known 3D-Structure, R-Coffee to align RNA sequences with conserved secondary structures and PSI-Coffee to accurately align distantly related sequences using homology extension. The new server benefits from recent improvements of the T-Coffee algorithm and can align up to 150 sequences as long as 10 000 residues and is available from both http://www.tcoffee.org and its main mirror http://tcoffee.crg.cat.

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          Most cited references19

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          Expresso: automatic incorporation of structural information in multiple sequence alignments using 3D-Coffee

          Expresso is a multiple sequence alignment server that aligns sequences using structural information. The user only needs to provide sequences. The server runs BLAST to identify close homologues of the sequences within the PDB database. These PDB structures are used as templates to guide the alignment of the original sequences using structure-based sequence alignment methods like SAP or Fugue. The final result is a multiple sequence alignment of the original sequences based on the structural information of the templates. An advanced mode makes it possible to either upload private structures or specify which PDB templates should be used to model each sequence. Providing the suitable structural information is available, Expresso delivers sequence alignments with accuracy comparable with structure-based alignments. The server is available on .
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            Alignment uncertainty and genomic analysis.

            The statistical methods applied to the analysis of genomic data do not account for uncertainty in the sequence alignment. Indeed, the alignment is treated as an observation, and all of the subsequent inferences depend on the alignment being correct. This may not have been too problematic for many phylogenetic studies, in which the gene is carefully chosen for, among other things, ease of alignment. However, in a comparative genomics study, the same statistical methods are applied repeatedly on thousands of genes, many of which will be difficult to align. Using genomic data from seven yeast species, we show that uncertainty in the alignment can lead to several problems, including different alignment methods resulting in different conclusions.
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              PROMALS: towards accurate multiple sequence alignments of distantly related proteins.

              Accurate multiple sequence alignments are essential in protein structure modeling, functional prediction and efficient planning of experiments. Although the alignment problem has attracted considerable attention, preparation of high-quality alignments for distantly related sequences remains a difficult task. We developed PROMALS, a multiple alignment method that shows promising results for protein homologs with sequence identity below 10%, aligning close to half of the amino acid residues correctly on average. This is about three times more accurate than traditional pairwise sequence alignment methods. PROMALS algorithm derives its strength from several sources: (i) sequence database searches to retrieve additional homologs; (ii) accurate secondary structure prediction; (iii) a hidden Markov model that uses a novel combined scoring of amino acids and secondary structures; (iv) probabilistic consistency-based scoring applied to progressive alignment of profiles. Compared to the best alignment methods that do not use secondary structure prediction and database searches (e.g. MUMMALS, ProbCons and MAFFT), PROMALS is up to 30% more accurate, with improvement being most prominent for highly divergent homologs. Compared to SPEM and HHalign, which also employ database searches and secondary structure prediction, PROMALS shows an accuracy improvement of several percent. The PROMALS web server is available at: http://prodata.swmed.edu/promals/. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                1 July 2011
                1 July 2011
                9 May 2011
                9 May 2011
                : 39
                : Web Server issue , Web Server issue
                : W13-W17
                Affiliations
                1Centre For Genomic Regulation (Pompeu Fabra University), Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain, 2Vital-IT, Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland, 3Department of Ecology and Evolution, Biophore, Lausanne University, CH-1015 Lausanne, Switzerland and 4Department of Computer Science and Industrial Engineering, University of Lleida, Campus de Cappont, C. de Jaume II 69, E-25001 Lleida, Spain
                Author notes
                *To whom correspondence should be addressed. Tel: +34 93 3160271; Fax: +34 93 3160099; Email: cedric.notredame@ 123456crg.eu
                Article
                gkr245
                10.1093/nar/gkr245
                3125728
                21558174
                66b98560-fc2b-418f-b281-ea47b78099df
                © The Author(s) 2011. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 February 2011
                : 23 March 2011
                : 5 April 2011
                Page count
                Pages: 5
                Categories
                Articles

                Genetics
                Genetics

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