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Bioinformatics Advance Access originally published online on August 25, 2005
Bioinformatics 2005 21(19):3704-3710; doi:10.1093/bioinformatics/bti616
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

Pairwise alignment incorporating dipeptide covariation

Gavin E. Crooks 1,*,{dagger}, Richard E. Green 1,2,{dagger} and Steven E. Brenner 1,2

1Department of Plant and Microbial Biology 111 Koshland Hall #3102 University of California, Berkeley, CA, USA
2Department of Molecular and Cell Biology, University of California Berkeley, CA 94720-3102, USA

*To whom correspondence should be addressed at Physical Biosciences Division, Lawrence Berkeley Natl Lab., Berkeley, CA 94720, USA

Motivation: Standard algorithms for pairwise protein sequence alignment make the simplifying assumption that amino acid substitutions at neighboring sites are uncorrelated. This assumption allows implementation of fast algorithms for pairwise sequence alignment, but it ignores information that could conceivably increase the power of remote homolog detection. We examine the validity of this assumption by constructing extended substitution matrices that encapsulate the observed correlations between neighboring sites, by developing an efficient and rigorous algorithm for pairwise protein sequence alignment that incorporates these local substitution correlations and by assessing the ability of this algorithm to detect remote homologies.

Results: Our analysis indicates that local correlations between substitutions are not strong on the average. Furthermore, incorporating local substitution correlations into pairwise alignment did not lead to a statistically significant improvement in remote homology detection. Therefore, the standard assumption that individual residues within protein sequences evolve independently of neighboring positions appears to be an efficient and appropriate approximation.

Availability: Sequence data, software and matrices are freely available from http://compbio.berkeley.edu/

Contact: gec{at}compbio.berkeley.edu

Supplementary information: Supplementary data for this paper is available at Bioinformatics online.

Received on March 16, 2005; revised on July 28, 2005; accepted on August 4, 2005

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[Abstract] [Full Text] [PDF]


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