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A Heuristic Algorithm to Find All Normalized Local Alignments Above Threshold  

Kim, Sangtae (Department of Computer Science, Korea Military Academy)
Sim, Jeong Seop (Electronics and Telecommunications Research Institute)
Park, Heejin (School of Computer Science and Engineering, Seoul National University)
Park, Kunsoo (Electronics and Telecommunications Research Institute, School of Computer Science and Engineering, Seoul National University)
Park, Hyunseok (Institute of Bioinformatics, Macrogen, Inc., Department of Computer Science, Ewha Womans University)
Seo, Jeong-Sun (Institute of Bioinformatics, Macrogen, Inc., Ilcheon Molecular Medicine Institute, Seoul National University)
Publication Information
Genomics & Informatics / v.1, no.1, 2003 , pp. 25-31 More about this Journal
Abstract
Local alignment is an important task in molecular biology to see if two sequences contain regions that are similar. The most popular approach to local alignment is the use of dynamic programming due to Smith and Waterman, but the alignment reported by the Smith-Waterman algorithm has some undesirable properties. The recent approach to fix these problems is to use the notion of normalized scores for local alignments by Arslan, Egecioglu and Pevzner. In this paper we consider the problem of finding all local alignments whose normalized scores are above a given threshold, and present a fast heuristic algorithm. Our algorithm is 180-330 times faster than Arslan et al.'s for sequences of length about 120 kbp and about 40-50 times faster for sequences of length about 30 kbp.
Keywords
local alignment; dynamic programming; normalized score; fractional programming;
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