Inverse sequence alignment from partial examples

Eagu Kim, John Kececioglu

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

2 Scopus citations

Abstract

When aligning biological sequences, the choice of parameter values for the alignment scoring function is critical. Small changes in gap penalties, for example, can yield radically different alignments. A rigorous way to compute parameter values that are appropriate for biological sequences is inverse parametric sequence alignment. Given a collection of examples of biologically correct alignments, this is the problem of finding parameter values that make the example alignments score close to optimal. We extend prior work on inverse alignment to partial examples and to an improved model based on minimizing the average error of the examples. Experiments on benchmark biological alignments show we can find parameters that generalize across protein families and that boost the recovery rate for multiple sequence alignment by up to 25%.

Original languageEnglish (US)
Title of host publicationAlgorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings
Pages359-370
Number of pages12
StatePublished - Dec 24 2007
Event7th International Workshop on Algorithms in Bioinformatics, WABI 2007 - PhiIadelphia, PA, United States
Duration: Sep 8 2007Sep 9 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4645 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Algorithms in Bioinformatics, WABI 2007
CountryUnited States
CityPhiIadelphia, PA
Period9/8/079/9/07

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, E., & Kececioglu, J. (2007). Inverse sequence alignment from partial examples. In Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings (pp. 359-370). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4645 LNBI).