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Bioinformatics Advance Access published online on November 22, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl590
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 8, 2006
Revised November 15, 2006
Accepted November 16, 2006

Article

QOMA: Quasi-Optimal Multiple Alignment of protein sequences

Xu Zhang 1 * and Tamer Kahveci 1

1 Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611

* To whom correspondence should be addressed.
Xu Zhang, E-mail: xuzhang{at}cise.ufl.edu


  Abstract

Motivation: We consider the problem of multiple alignment of protein sequences with the goal of achieving a large SP (Sum-of-Pairs) score.

Results: We introduce a new graph-based method. We name our method QOMA (Quasi-Optimal Multiple Alignment). QOMA starts with an initial alignment. It represents this alignment using a K-partite graph. It then improves the SP score of the initial alignment through local optimizations within a window that moves greedily on the alignment. QOMA uses two parameters to permit flexibility in time/accuracy trade off: (1) The size of the window for local optimization. (2) The sparsity of the K-partite graph. Unlike traditional progressive methods, QOMA is independent of the order of sequences. The experimental results on BAliBASE benchmarks show that QOMA produces higher SP score than the existing tools including ClustalW, Probcons, Muscle, T-Coffee and DCA. The difference is more significant for distant proteins.

Availability: The software is available from the authors upon request.

Associate Editor: John Quackenbush
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