A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Rapid significance estimation in local sequence alignment with gaps
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Rapid Assessment of Extremal Statistics for Gapped Local Alignment
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
ICIT '08 Proceedings of the 2008 International Conference on Information Technology
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Derived distribution points heuristic for fast pairwise statistical significance estimation
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
FPGA architecture for pairwise statistical significance estimation
International Journal of High Performance Systems Architecture
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Pairwise sequence alignment is a central problem in bioinformatics, which forms the basis of various other applications. Two related sequences are expected to have a high alignment score, but relatedness is usually judged by statistical significance rather than by alignment score. Recently, it was shown that pairwise statistical significance gives promising results as an alternative to database statistical significance for getting individual significance estimates of pairwise alignment scores. The improvement was mainly attributed to making the statistical significance estimation process more sequence-specific and database-independent. In this paper, we use sequence-specific and position-specific substitution matrices to derive the estimates of pairwise statistical significance, which is expected to use more sequence-specific information in estimating pairwise statistical significance. Experiments on a benchmark database with sequence-specific substitution matrices at different levels of sequence-specific contribution were conducted, and results confirm that using sequence-specific substitution matrices for estimating pairwise statistical significance is significantly better than using a standard matrix like BLOSUM62, and than database statistical significance estimates reported by popular database search programs like BLAST, PSI-BLAST (without pretrained PSSMs), and SSEARCH on a benchmark database, but with pretrained PSSMs, PSI-BLAST results are significantly better. Further, using position-specific substitution matrices for estimating pairwise statistical significance gives significantly better results even than PSI-BLAST using pretrained PSSMs.