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- W2607073721 abstract "While 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 that finds global parameter settings for aligners, to 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, implemented within the Opal aligner using the Facet accuracy estimator, is available at facet.cs.arizona.edu." @default.
- W2607073721 created "2017-04-28" @default.
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- W2607073721 date "2017-01-01" @default.
- W2607073721 modified "2023-09-26" @default.
- W2607073721 title "Boosting Alignment Accuracy by Adaptive Local Realignment" @default.
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- W2607073721 doi "https://doi.org/10.1007/978-3-319-56970-3_1" @default.
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