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Bioinformatics Advance Access published online on August 27, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth497
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received September 30, 2004
Revised July 17, 2004
Accepted August 19, 2004

Article

A New Algorithm for Detecting Low-complexity Regions in Protein Sequences

Sung W. Shin 1* Sam M. Kim 1

1 Department of Computer Engineering, Kyungpook National University, Daegu, Korea 702-701

* To whom correspondence should be addressed. E-mail: swshin{at}bioinfo.knu.ac.kr.


  Abstract

Motivation: Pair-wise alignment of protein sequences and local similarity searches produce many false positives because of compositionally biased regions, also called low-complexity regions (LCRs), of amino acid residues. Masking and filtering such regions significantly improves the reliability of homology searches and, consequently, functional predictions. Most of the available algorithms are based on a statistical approach. We want to investigate structural properties of LCRs of biological sequences and develop an algorithm for filtering them.

Results: We present an algorithm for detection and masking LCRs in protein sequences to improve the quality of database searches. We developed the algorithm based on the complexity analysis of subsequences delimited by a pair of identical repeating subsequences. Given a protein sequence, the algorithm first computes the suffix tree of the sequence. It then collects repeating subsequences from the tree. Finally, the algorithm iteratively tests whether each subsequence delimited by a pair of repeating subsequences meets a given criteria. Test results with 1,000 proteins from 20 families in Pfam show that the repeating subsequences are a good indicator for the low-complexity regions, and the algorithm based on such structural information strongly compete with others.

Availability: http://bioinfo.knu.ac.kr/research/CARD/.


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