GLProbs: Aligning multiple sequences adaptively

Authors:
Yongtao Ye;David W. Cheung;Yadong Wang;Siu-Ming Yiu;Qing Zhan;Tak-Wah Lam;Hing-Fung Ting
Affiliations:
University of Hong Kong;University of Hong Kong;Harbin Institute of Technology;University of Hong Kong;Harbin Institute of Technology;University of Hong Kong;University of Hong Kong
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

Citing 6
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Abstract

This paper proposes a simple and effective approach to improve the accuracy of multiple sequence alignment. We use a natural measure to estimate the similarity of the input sequences, and based on this measure, we align the input sequences differently. For example, for inputs with high similarity, we consider the whole sequences and align them globally, while for those with moderately low similarity, we may ignore the flank regions and align locally. To test the effectiveness of this approach, we have implemented a multiple sequence alignment tool called GLProbs, and compared its performance with a dozen leading alignment tools on three benchmark alignment databases. Our results show that GLProbs has the best accuracy for almost all testings.