Fixing Mosaic Effect on Sequence Local Alignment with Dynamic Penalty Strategy
用动态扣分策略消除序列局部联配中的马赛克问题

The local alignment problem for two sequences requires determining similar regions, one from each sequence, and aligning those regions. The Smith-Waterman algorithm for local sequence alignment is one of the most well-known algorithm in computational molecular biology. This ingenious dynamic programming approach is designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the local alignment sometimes produces a mosaic of well conserved fragments artificially connected by poorly conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction. In this paper we propose a new strategy of dynamic penalty strategy to fix this problem. In the process of computing similarity matrix, if similarity value is larger than the pre-specified threshold X then starting our strategy, when related character mismatches, then penalizing more than others until similarity value is 0 or the process ends. Test results show that this algorithm has better performance by comparison to the standard Smith-Waterman while dose not increase signally the computational complexity both in time and space.