Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation

Many fields of biology rely on the inference of accurate multiple sequence alignments (MSA) of biological sequences. Unfortunately, the problem of assembling an MSA is NP-complete thus limiting computation to approximate solutions using heuristics solutions. The progressive algorithm is one of the m...

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Authors: Garriga, Edgar, Di Tommaso, Paolo, Magis, Cedrik, Erb, Ionas, Mansouri, Leila, Baltzis, Athanasios, Floden, Evan, 1985-, Notredame, Cedric
Format: article
Status:Versión aceptada para publicación
Publication Date:2021
Country:España
Institution:Universitat Pompeu Fabra
Repository:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/46777
Online Access:http://hdl.handle.net/10230/46777
http://dx.doi.org/10.1007/978-1-0716-1036-7_6
Access Level:Open access
Keyword:Alineament de seqüència (Bioinformàtica)
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spelling Multiple sequence alignment computation using the T-Coffee regressive algorithm implementationGarriga, EdgarDi Tommaso, PaoloMagis, CedrikErb, IonasMansouri, LeilaBaltzis, AthanasiosFloden, Evan, 1985-Notredame, CedricAlineament de seqüència (Bioinformàtica)Many fields of biology rely on the inference of accurate multiple sequence alignments (MSA) of biological sequences. Unfortunately, the problem of assembling an MSA is NP-complete thus limiting computation to approximate solutions using heuristics solutions. The progressive algorithm is one of the most popular frameworks for the computation of MSAs. It involves pre-clustering the sequences and aligning them starting with the most similar ones. The scalability of this framework is limited, especially with respect to accuracy. We present here an alternative approach named regressive algorithm. In this framework, sequences are first clustered and then aligned starting with the most distantly related ones. This approach has been shown to greatly improve accuracy during scale-up, especially on datasets featuring 10,000 sequences or more. Another benefit is the possibility to integrate third-party clustering methods and third-party MSA aligners. The regressive algorithm has been tested on up to 1.5 million sequences, its implementation is available in the T-Coffee package.Humana Press (Springer Imprint)20212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/46777http://dx.doi.org/10.1007/978-1-0716-1036-7_6reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésMethods in Molecular Biology. 2021;2231:89-97© Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-1-0716-1036-7_6info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/467772026-06-12T07:21:37Z
dc.title.none.fl_str_mv Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
title Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
spellingShingle Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
Garriga, Edgar
Alineament de seqüència (Bioinformàtica)
title_short Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
title_full Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
title_fullStr Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
title_full_unstemmed Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
title_sort Multiple sequence alignment computation using the T-Coffee regressive algorithm implementation
dc.creator.none.fl_str_mv Garriga, Edgar
Di Tommaso, Paolo
Magis, Cedrik
Erb, Ionas
Mansouri, Leila
Baltzis, Athanasios
Floden, Evan, 1985-
Notredame, Cedric
author Garriga, Edgar
author_facet Garriga, Edgar
Di Tommaso, Paolo
Magis, Cedrik
Erb, Ionas
Mansouri, Leila
Baltzis, Athanasios
Floden, Evan, 1985-
Notredame, Cedric
author_role author
author2 Di Tommaso, Paolo
Magis, Cedrik
Erb, Ionas
Mansouri, Leila
Baltzis, Athanasios
Floden, Evan, 1985-
Notredame, Cedric
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Alineament de seqüència (Bioinformàtica)
topic Alineament de seqüència (Bioinformàtica)
description Many fields of biology rely on the inference of accurate multiple sequence alignments (MSA) of biological sequences. Unfortunately, the problem of assembling an MSA is NP-complete thus limiting computation to approximate solutions using heuristics solutions. The progressive algorithm is one of the most popular frameworks for the computation of MSAs. It involves pre-clustering the sequences and aligning them starting with the most similar ones. The scalability of this framework is limited, especially with respect to accuracy. We present here an alternative approach named regressive algorithm. In this framework, sequences are first clustered and then aligned starting with the most distantly related ones. This approach has been shown to greatly improve accuracy during scale-up, especially on datasets featuring 10,000 sequences or more. Another benefit is the possibility to integrate third-party clustering methods and third-party MSA aligners. The regressive algorithm has been tested on up to 1.5 million sequences, its implementation is available in the T-Coffee package.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/46777
http://dx.doi.org/10.1007/978-1-0716-1036-7_6
url http://hdl.handle.net/10230/46777
http://dx.doi.org/10.1007/978-1-0716-1036-7_6
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Methods in Molecular Biology. 2021;2231:89-97
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Humana Press (Springer Imprint)
publisher.none.fl_str_mv Humana Press (Springer Imprint)
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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