Statistics of local multiple alignments.
BLAST statistics have been shown to be extremely useful for searching for significant similarity hits, for amino acid and nucleotide sequences. Although these statistics are well understood for pairwise comparisons, there has been little success developing statistical scores for multiple alignments. In particular, there is no score for multiple alignment that is well founded and treated as a standard. We extend the BLAST theory to multiple alignments. Following some simple assumptions, we present and justify a significance score for multiple segments of a local multiple alignment. We demonstrate its usefulness in distinguishing high and moderate quality multiple alignments from low quality ones, with supporting experiments on orthologous vertebrate promoter sequences.
Prakash
Amol
A
Department of Computer Science and Engineering Box 352350 University of Washington Seattle, WA 98195-2350, USA. amol@cs.washington.edu
Tompa
Martin
M
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D015233:Models, Statistical; D008969:Molecular Sequence Data; D010802:Phylogeny; D011401:Promoter Regions, Genetic; D015203:Reproducibility of Results; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software