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Multiobjective characteristic-based framework for very-large multiple sequence alignment

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dc.contributor.authorRubio-Largo, Álvaro
dc.contributor.authorVanneschi, Leonardo
dc.contributor.authorCastelli, Mauro
dc.contributor.authorVega-Rodríguez, Miguel A.
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2023-03-30T22:09:16Z
dc.date.available2024-01-27T01:32:01Z
dc.date.embargoedUntil2023-03-31
dc.date.issued2018
dc.descriptionRubio-Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2018). Multiobjective characteristic-based framework for very-large multiple sequence alignment. Applied Soft Computing Journal, 69, 719-736. [Advanced online publication on 27 June 2017]DOI: 10.1016/j.asoc.2017.06.022
dc.description.abstractIn the literature, we can find several heuristics for solving the multiple sequence alignment problem. The vast majority of them makes use of flags in order to modify certain alignment parameters; however, if no flags are used, the aligner will run with the default parameter configuration, which, often, is not the optimal one. In this work, we propose a framework that, depending on the biological characteristics of the input dataset, runs the aligner with the best parameter configuration found for another dataset that has similar biological characteristics, improving the accuracy and conservation of the obtained alignment. To train the framework, we use three well-known multiobjective evolutionary algorithms: NSGA-II, IBEA, and MOEA/D. Then, we perform a comparative study between several aligners proposed in the literature and the characteristic-based version of Kalign, MAFFT, and MUSCLE, when solving widely-used benchmarks (PREFAB v4.0 and SABmark v1.65) and very-large benchmarks with thousands of unaligned sequences (HomFam).en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent2080282
dc.identifier.doi10.1016/j.asoc.2017.06.022
dc.identifier.issn1568-4946
dc.identifier.otherPURE: 3236565
dc.identifier.otherPURE UUID: 4a7f0bff-0944-49c4-8ffb-9ebb025fb14b
dc.identifier.otherScopus: 85023618707
dc.identifier.otherWOS: 000438775200046
dc.identifier.otherORCID: /0000-0002-8793-1451/work/131992544
dc.identifier.otherORCID: /0000-0003-4732-3328/work/151426686
dc.identifier.urihttp://hdl.handle.net/10362/151413
dc.identifier.urlhttps://www.scopus.com/pages/publications/85023618707
dc.language.isoeng
dc.peerreviewedyes
dc.subjectCharacteristic-based
dc.subjectEvolutionary algorithms
dc.subjectMultiobjective optimization
dc.subjectMultiple sequence alignment
dc.subjectSoftware
dc.subjectSDG 3 - Good Health and Well-being
dc.titleMultiobjective characteristic-based framework for very-large multiple sequence alignmenten
dc.typejournal article
degois.publication.firstPage719
degois.publication.lastPage736
degois.publication.titleApplied Soft Computing Journal
degois.publication.volume69
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)