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Optimizing multiple seeds for protein homology search

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1 Author(s)
Brown, D.G. ; Sch. of Comput. Sci., Waterloo Univ., Ont., Canada

We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds or ungapped alignment seeds to reduce noise hits. We model picking a set of seed models as an integer programming problem and give algorithms to choose such a set of seeds. While the problem is NP-hard, and Quasi-NP-hard to approximate to within a logarithmic factor, it can be solved easily in practice. A good set of seeds we have chosen allows four to five times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP.

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Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:2 ,  Issue: 1 )