A new algorithm for detecting low-complexity regions in protein sequences

Bioinformatics
Sung W Shin, Sam M Kim

Abstract

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 wished to investigate the structural properties of LCRs in biological sequences and develop an algorithm for filtering them. We present an algorithm for detecting 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 1000 proteins from 20 families in Pfam show that the repeating subsequences are a good indicator for the low-complexity regions, and the algorithm ba...Continue Reading

References

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Citations

Oct 5, 2006·Bioinformatics·Xuehui Li, Tamer Kahveci
Jun 8, 2007·Nucleic Acids Research·Chung-Tsai SuChen-Ming Hsu
Apr 20, 2013·Nucleic Acids Research·Jaina MistryMarco Punta
Feb 19, 2013·Briefings in Bioinformatics·Hong Luo, Harm Nijveen
Feb 28, 2008·Journal of Structural and Functional Genomics·Ryan M BannenGeorge N Phillips
Sep 25, 2018·PLoS Computational Biology·Sean M Cascarina, Eric D Ross
Jan 31, 2019·Briefings in Bioinformatics·Pablo MierMiguel A Andrade-Navarro
Jan 28, 2020·PLoS Computational Biology·Sean M CascarinaEric D Ross

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