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dc.contributor.authorDeBlasio, Dan
dc.contributor.authorKececioglu, John
dc.date.accessioned2017-06-06T00:30:01Z
dc.date.available2017-06-06T00:30:01Z
dc.date.issued2017-04-19
dc.identifier.citationCore column prediction for protein multiple sequence alignments 2017, 12 (1) Algorithms for Molecular Biologyen
dc.identifier.issn1748-7188
dc.identifier.pmid28435440
dc.identifier.doi10.1186/s13015-017-0102-3
dc.identifier.urihttp://hdl.handle.net/10150/623957
dc.description.abstractBackground: In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted. Results: We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment's accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner's scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.
dc.description.sponsorshipUniversity of Arizona by US National Science Foundation [IIS-1217886]; Carnegie Mellon University by NSF [CCF-1256087]; NSF [CCF-131999]; NIH [R01HG007104]; Gordon and Betty Moore Foundation [GBMF4554]; University of Arizona Open Access Publishing Funden
dc.language.isoenen
dc.publisherBIOMED CENTRAL LTDen
dc.relation.urlhttp://almob.biomedcentral.com/articles/10.1186/s13015-017-0102-3en
dc.rights© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMultiple sequence alignmenten
dc.subjectCore blocksen
dc.subjectAlignment accuracyen
dc.subjectAccuracy estimationen
dc.subjectParameter advisingen
dc.subjectMachine learningen
dc.subjectRegressionen
dc.titleCore column prediction for protein multiple sequence alignmentsen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Comp Scien
dc.identifier.journalAlgorithms for Molecular Biologyen
dc.description.noteOpen Access Journal.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-11T19:55:56Z
html.description.abstractBackground: In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted. Results: We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment's accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner's scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.


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© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.
Except where otherwise noted, this item's license is described as © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.