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Correcting BLAST e-Values for Low-Complexity Segments
Author(s) -
Itai Sharon,
Aaron Birkland,
Kuan Y. Chang,
Ran ElYaniv,
Golan Yona
Publication year - 2005
Publication title -
journal of computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 95
eISSN - 1557-8666
pISSN - 1066-5277
DOI - 10.1089/cmb.2005.12.980
Subject(s) - benchmark (surveying) , computer science , divergence (linguistics) , sequence (biology) , relevance (law) , data mining , statistics , artificial intelligence , machine learning , mathematics , biology , geography , cartography , philosophy , linguistics , political science , law , genetics
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/.

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