AffiliationUniv Arizona, Dept Comp Sci
local mutation rates
multiple sequence alignment
MetadataShow full item record
PublisherMARY ANN LIEBERT, INC
CitationDan DeBlasio and John Kececioglu. Journal of Computational Biology.Jul 2018. http://doi.org/10.1089/cmb.2018.0045
JournalJOURNAL OF COMPUTATIONAL BIOLOGY
RightsCopyright © 2018, Mary Ann Liebert, Inc.
Collection InformationThis 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 firstname.lastname@example.org.
AbstractWhile mutation rates can vary markedly over the residues of a protein, multiple sequence alignment tools typically use the same values for their scoring-function parameters across a protein's entire length. We present a new approach, called adaptive local realignment, that in contrast automatically adapts to the diversity of mutation rates along protein sequences. This builds upon a recent technique known as parameter advising, which finds global parameter settings for an aligner, to now adaptively find local settings. Our approach in essence identifies local regions with low estimated accuracy, constructs a set of candidate realignments using a carefully-chosen collection of parameter settings, and replaces the region if a realignment has higher estimated accuracy. This new method of local parameter advising, when combined with prior methods for global advising, boosts alignment accuracy as much as 26% over the best default setting on hard-to-align protein benchmarks, and by 6.4% over global advising alone. Adaptive local realignment has been implemented within the Opal aligner using the Facet accuracy estimator.
Note12 month embargo; published online: 1 July 2018
VersionFinal accepted manuscript
SponsorsNSF [IIS-1217886]; Lane Fellows Program from the Computational Biology Department
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