Recovering accuracy methods for scalable consistency library
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- dc.contributor.author Lladós, Jordica
- dc.contributor.author Guirado, Fernandoca
- dc.contributor.author Cores, Fernandoca
- dc.contributor.author Lérida, Josep Lluísca
- dc.contributor.author Notredame, Cedricca
- dc.date.accessioned 2015-11-13T17:35:23Z
- dc.date.available 2015-11-13T17:35:23Z
- dc.date.issued 2015
- dc.description.abstract Multiple 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.ca
- dc.format.mimetype application/pdfca
- dc.identifier.citation Lladós J, Guirado F, Cores F, Lérida JL, Notredame C. Recovering accuracy methods for scalable consistency library. The Journal of Supercomputing. 2015; 71(5): 1833-1845. DOI 10.1007/s11227-014-1362-zca
- dc.identifier.doi http://dx.doi.org/10.1007/s11227-014-1362-z
- dc.identifier.issn 1573-0484
- dc.identifier.uri http://hdl.handle.net/10230/25086
- dc.language.iso engca
- dc.publisher Springerca
- dc.relation.ispartof The Journal of Supercomputing. 2015; 71(5): 1833-1845
- dc.rights © The Author(s) 2014. This article is published with open access at Springerlink.com. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.ca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.subject.keyword Large-scale alignments
- dc.subject.keyword Scalability
- dc.subject.keyword Consistency
- dc.subject.keyword Accuracy
- dc.subject.keyword T-Coffee
- dc.subject.keyword Multiple sequence alignment
- dc.subject.other Bioinformàticaca
- dc.subject.other Biologia computacionalca
- dc.title Recovering accuracy methods for scalable consistency libraryca
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/publishedVersionca