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Bioinformatics Advance Access originally published online on November 22, 2006
Bioinformatics 2007 23(2):162-168; 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

QOMA: quasi-optimal multiple alignment of protein sequences

Xu Zhang and Tamer Kahveci *

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

*To whom correspondence should be addressed.


  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.

Contact: tamer{at}cise.ufl.edu

Supplementary information: Supplementary material is available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on August 8, 2006; revised on November 15, 2006; accepted on November 16, 2006

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[Abstract] [Full Text] [PDF]


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