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Issue No.01 - Jan.-Feb. (2015 vol.12)
pp: 67-78
Yongtao Ye , HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Department of Computer Science, University of Hong Kong, Hong Kong
David Wai-lok Cheung , HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Department of Computer Science, University of Hong Kong, Hong Kong
Yadong Wang , Department of Computer Science and Engineering, Harbin Institue of Technology, Harbin, Heilongjiang, China
Siu-Ming Yiu , HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Department of Computer Science, University of Hong Kong, Hong Kong
Qing Zhan , Department of Computer Science and Engineering, Harbin Institue of Technology, Harbin, Heilongjiang, China
Tak-Wah Lam , HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Department of Computer Science, University of Hong Kong, Hong Kong
Hing-Fung Ting , HKU-BGI Bioinformatics Algorithms & Core Technology Research Lab, Department of Computer Science, University of Hong Kong, Hong Kong
ABSTRACT
This paper introduces 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 them locally. To test the effectiveness of this approach, we have implemented a multiple sequence alignment tool called GLProbs and compared its performance with about one dozen leading alignment tools on three benchmark alignment databases, and GLProbs’s alignments have the best scores in almost all testings. We have also evaluated the practicability of the alignments of GLProbs by applying the tool to three biological applications, namely phylogenetic trees construction, protein secondary structure prediction and the detection of high risk members for cervical cancer in the HPV-E6 family, and the results are very encouraging.
INDEX TERMS
Databases, Hidden Markov models, Benchmark testing, Proteins, Bioinformatics, Accuracy,secondary structure prediction, Multiple sequence alignment, progressive alignment, hidden Markov model, phylogenetic analysis
CITATION
Yongtao Ye, David Wai-lok Cheung, Yadong Wang, Siu-Ming Yiu, Qing Zhan, Tak-Wah Lam, Hing-Fung Ting, "GLProbs: Aligning Multiple Sequences Adaptively", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.12, no. 1, pp. 67-78, Jan.-Feb. 2015, doi:10.1109/TCBB.2014.2316820
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