PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Correcting BLAST e-Values for Low-Complexity Segments
Itai Sharon, Aaron Birkland, Kuan Chang, Ran El-Yaniv and Golan Yona
Journal of Computational Biology Volume 12, Number 7, pp. 978-1001, 2005.


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 and∼itaish/lowcomp/.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:1569
Deposited By:Ran El-Yaniv
Deposited On:28 November 2005