pairwise statistical significance of local sequence alignment using multiple parameter sets
Accurate estimation of statistical significance of a pairwise alignment is an important problem in sequence comparison. Recently, a comparative study of pairwise statistical significance with database statistical significance was conducted. In this paper, we extend the earlier work on pairwise statistical significance by incorporating with it the use of multiple parameter sets. Preliminary results for a knowledge discovery application such as homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives significantly better coverage than using a single parameter set, at least at some error levels. Also, the fact that the performance does not degrade when using multiple parameter sets is a strong evidence that the assumption that the score distribution follows an extreme value distribution is valid even when using multiple parameter sets. Results of pairwise statistical significance using multiple parameter sets are further shown to be significantly better than database statistical significance estimates reported by BLAST and PSI-BLAST, and comparable and at times significantly better than SSEARCH.
pairwise statistical significance sequence alignment parameter set database statistical significance homologs