Significance assessment of local sequence alignment with gaps
Department of Physics, The Ohio State University
(January 19, 2006 10:30 AM - 11:30 AM)
Sequence alignment is the most prevalent computational method for functionally annotating newly found genes. It remains a crucial problem in the application of sequence alignment to distinguish between biologically significant and spurious similarities between the query sequence and a database sequence. Current numerical methods for assessing the statistical significance of local alignments with gaps are time consuming. Analytical solutions thus far have been limited to specific cases. Here, we present a new line of attack to the problem of statistical significance assessment. We combine this new approach with known properties of the dynamics of the global alignment algorithm and high performance numerical techniques and present a novel method for assessing significance of gaps within practical time scales. The results and performance of these new methods test very well against tried methods with drastically less effort.