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dc.contributor.authorDíaz, David
dc.contributor.authorEsteban, F.J.
dc.contributor.authorHernández, Pilar
dc.contributor.authorCaballero, J.A.
dc.contributor.authorGuevara, A
dc.contributor.authorDorado, G.
dc.contributor.authorGálvez, Sergio
dc.date.accessioned2017-12-11T10:34:59Z
dc.date.available2017-12-11T10:34:59Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10396/15682
dc.description.abstractWe have developed the MC64-ClustalWP2 as a new implementation of the Clustal W algorithm, integrating a novel parallelization strategy and significantly increasing the performance when aligning long sequences in architectures with many cores. It must be stressed that in such a process, the detailed analysis of both the software and hardware features and peculiarities is of paramount importance to reveal key points to exploit and optimize the full potential of parallelism in many-core CPU systems. The new parallelization approach has focused into the most time-consuming stages of this algorithm. In particular, the so-called progressive alignment has drastically improved the performance, due to a fine-grained approach where the forward and backward loops were unrolled and parallelized. Another key approach has been the implementation of the new algorithm in a hybrid-computing system, integrating both an Intel Xeon multi-core CPU and a Tilera Tile64 many-core card. A comparison with other Clustal W implementations reveals the high-performance of the new algorithm and strategy in many-core CPU architectures, in a scenario where the sequences to align are relatively long (more than 10 kb) and, hence, a many-core GPU hardware cannot be used. Thus, the MC64-ClustalWP2 runs multiple alignments more than 18x than the original Clustal W algorithm, and more than 7x than the best x86 parallel implementation to date, being publicly available through a web service. Besides, these developments have been deployed in cost-effective personal computers and should be useful for life-science researchers, including the identification of identities and differences for mutation/polymorphism analyses, biodiversity and evolutionary studies and for the development of molecular markers for paternity testing, germplasm management and protection, to assist breeding, illegal traffic control, fraud prevention and for the protection of the intellectual property (identification/traceability), including the protected designation of origin, among other applications.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherPLOSes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourcePLoS ONE 9(4): e94044 (2014)es_ES
dc.subjectSequence alignmentes_ES
dc.subjectMultiple alignment calculationes_ES
dc.subjectHeuristic alignment procedurees_ES
dc.subjectCLUSTAL analysises_ES
dc.subjectMicroprocessorses_ES
dc.subjectAlgorithmses_ES
dc.subjectPhylogenetic analysises_ES
dc.subjectJobses_ES
dc.titleMC64-ClustalWP2: A Highly-Parallel Hybrid Strategy to Align Multiple Sequences in Many-Core Architectureses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1371/journal.pone.0094044es_ES
dc.relation.projectIDGobierno de España. AGL2010-17316es_ES
dc.relation.projectIDGobierno de España. BIO2011-15237-Ees_ES
dc.relation.projectIDGobierno de España. RF2012-00002-C02-02es_ES
dc.relation.projectIDJunta de Andalucía. PAIDI AGR-248es_ES
dc.relation.projectIDJunta de Andalucía. PAIDI 041/C/2007es_ES
dc.relation.projectIDJunta de Andalucía. PAIDI 75/C/2009es_ES
dc.relation.projectIDJunta de Andalucía. PAIDI 56/C/2010es_ES
dc.relation.projectIDJunta de Andalucía. PAIDI AGR-7322es_ES
dc.relation.projectIDJunta de Andalucía. PAIDI AGR-482es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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