Correcting BLAST e-values for low-complexity segments.
The statistical estimates of BLAST and PSI-BLAST are of extreme importance to determine the biological relevance of sequence matches. While being very effective in evaluating most matches, these estimates usually overestimate the significance of matches in the presence of low complexity segments. In this paper, we present a model, based on divergence measures and statistics of the alignment structure, that corrects BLAST e-values for low complexity sequences without filtering or excluding them and generates scores that are more effective in distinguishing true similarities from chance similarities. We evaluate our method and compare it to other known methods using the Gene Ontology (GO) knowledge resource as a benchmark. Various performance measures, including ROC analysis, indicate that the new model improves upon the state of the art. The program is available at biozon.org/ftp/ and www.cs.technion.ac.il/ approximately itaish/lowcomp/.
Sharon
Itai
I
Department of Computer Science, Technion, Haifa, Israel.
Birkland
Aaron
A
Chang
Kuan
K
El-Yaniv
Ran
R
Yona
Golan
G
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D030562:Databases, Protein; D008969:Molecular Sequence Data; D016415:Sequence Alignment / Q000379:methods* / Q000706:statistics & numerical data; D020539:Sequence Analysis, Protein / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software