TY - GEN
T1 - Boosting alignment accuracy by adaptive local realignment
AU - DeBlasio, Dan
AU - Kececioglu, John
N1 - Funding Information:
Research of JK and DD at Arizona was funded by NSF Grant IIS-1217886 to JK. DD was partially supported at Carnegie Mellon by NSF Grant CCF-1256087, NSF Grant CCF-131999, NIH Grant R01HG007104, and Gordon and Betty Moore Foundation Grant GBMF4554, to Carl Kingsford.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Alignment accuracy
KW - Iterative refinement
KW - Local mutation rates
KW - Multiple sequence alignment
KW - Parameter advising
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U2 - 10.1007/978-3-319-56970-3_1
DO - 10.1007/978-3-319-56970-3_1
M3 - Conference contribution
AN - SCOPUS:85018377751
SN - 9783319569697
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 17
BT - Research in Computational Molecular Biology - 21st Annual International Conference, RECOMB 2017, Proceedings
A2 - Sahinalp, S.Cenk
PB - Springer-Verlag
T2 - 21st Annual International Conference on Research in Computational Molecular Biology, RECOMB 2017
Y2 - 3 May 2017 through 7 May 2017
ER -