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dc.contributor.authorLladós Segura, Jordi
dc.contributor.authorGuirado Fernández, Fernando
dc.contributor.authorCores Prado, Fernando
dc.contributor.authorLérida Monsó, Josep Lluís
dc.contributor.authorNotredame, Cedric
dc.date.accessioned2015-09-23T14:02:33Z
dc.date.available2015-09-23T14:02:33Z
dc.date.issued2015-05-01
dc.identifier.issn0920-8542
dc.identifier.urihttp://hdl.handle.net/10459.1/48751
dc.description.abstractMultiple sequence alignment (MSA) is crucial for high-throughput next generation sequencing applications. Large-scale alignments with thousands of sequences are necessary for these applications. However, the quality of the alignment of current MSA tools decreases sharply when the number of sequences grows to several thousand. This accuracy degradation can be mitigated using global consistency information as in the T-Coffee MSA-Tool, which implements a consistency library. However, consistency-based methods do not scale well because of the computational resources required to calculate and store the consistency information, which grows quadratically. In this paper, we propose an alternative method for building the consistency-library. To allow unlimited scalability, consistency information must be discarded to avoid exceeding the environment memory. Our first approach deals with the memory limitation by identifying the most important entries, which provide better consistency. This method is able to achieve scalability, although there is a negative impact on accuracy. The second proposal, aims to reduce this degradation of accuracy, with three different methods presented to attain a better alignment.
dc.description.sponsorshipThis work has been supported by the Government of Spain TIN2011-28689-C02-02. Cedric Notredame is funded by the Plan Nacional BFU2011-28575 and The Quantomics project (KBBE- 2A-222664).
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relationMICINN/PN2008-2011/TIN2011-28689-C02-02
dc.relationMICINN/PN2008-2011/BFU2011-28575
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1007/s11227-014-1362-z
dc.relation.ispartofJournal of Supercomputing, 2015, vol. 71, núm. 5, p. 1833-1845
dc.rightscc-by (c) Lladós Segura, Jordi et al., 2015
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es
dc.subjectLarge-Scale Alignments
dc.subjectScalability
dc.subjectConsistency
dc.subjectT-Coffee
dc.subjectMultiple Sequence Alignment
dc.subject.classificationLlenguatges de programació
dc.subject.classificationInformàtica
dc.subject.classificationArquitectures de xarxes d'ordinadors
dc.subject.otherProgramming languages (Electronic computers)
dc.subject.otherComputer science
dc.subject.otherComputer network architectures
dc.titleRecovering accuracy methods for scalable consistency library
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2015-09-23T14:02:33Z
dc.identifier.idgrec023039
dc.type.versionpublishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1007/s11227-014-1362-z


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cc-by (c) Lladós Segura, Jordi et al., 2015
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