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dc.contributor.authorLladós Segura, Jordi
dc.contributor.authorGuirado Fernández, Fernando
dc.contributor.authorCores Prado, Fernando
dc.date.accessioned2019-07-17T07:39:02Z
dc.date.available2019-07-17T07:39:02Z
dc.date.issued2017
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10459.1/66626
dc.description.abstractLarge-scale data processing techniques, currently known as Big-Data, are used to manage the huge amount of data that are generated by sequencers. Although these techniques have significant advantages, few biological applications have adopted them. In the Bioinformatic scientific area, Multiple Sequence Alignment (MSA) tools are widely applied for evolution and phylogenetic analysis, homology and domain structure prediction. Highly-rated MSA tools, such as MAFFT, ProbCons and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage in order to improve the final accuracy. In this paper, a novel approach named PPCAS (Probabilistic Pairwise model for Consistency-based multiple alignment in Apache Spark) is presented. PPCAS is based on the MapReduce processing paradigm in order to enable large datasets to be processed with the aim of improving the performance and scalability of the original algorithm.ca_ES
dc.description.sponsorshipThis work was supported by the MEyC-Spain [contract TIN2014-53234-C2-2-R].ca_ES
dc.language.isoengca_ES
dc.publisherSpringerca_ES
dc.relationMINECO/PN2013-2016/TIN2014-53234-C2-2-Rca_ES
dc.relation.isformatofVersió postprint del document publicat a https://doi.org/10.1007/978-3-319-65482-9_45ca_ES
dc.relation.ispartofLecture Notes in Computer Science, 2017, vol. 10393, p. 601–610ca_ES
dc.rights(c) Springer International Publishing AG 2017ca_ES
dc.subjectMultiple Sequence Alignmentca_ES
dc.subjectConsistencyca_ES
dc.subjectSparkca_ES
dc.subjectMapReduceca_ES
dc.titlePPCAS: Implementation of a Probabilistic Pairwise Model for Consistency-Based Multiple Alignment in Apache Sparkca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec027256
dc.type.versioninfo:eu-repo/semantics/acceptedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1007/978-3-319-65482-9_45


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