Boosting alignment accuracy by adaptive local realignment

Dan DeBlasio, John D Kececioglu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings
PublisherSpringer Verlag
Pages1-17
Number of pages17
Volume10229 LNCS
ISBN (Print)9783319569697
DOIs
StatePublished - 2017
Event21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017 - Hong Kong, China
Duration: May 3 2017May 7 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10229 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017
CountryChina
CityHong Kong
Period5/3/175/7/17

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Keywords

  • Alignment accuracy
  • Iterative refinement
  • Local mutation rates
  • Multiple sequence alignment
  • Parameter advising

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

DeBlasio, D., & Kececioglu, J. D. (2017). Boosting alignment accuracy by adaptive local realignment. In Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings (Vol. 10229 LNCS, pp. 1-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10229 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-56970-3_1