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Issue No.01 - January-March (2005 vol.2)
pp: 29-38
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.
Bioinformatics database applications, similarity measures, biology and genetics.
Daniel G. Brown, "Optimizing Multiple Seeds for Protein Homology Search", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.2, no. 1, pp. 29-38, January-March 2005, doi:10.1109/TCBB.2005.13
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