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Bioinformatics Advance Access originally published online on February 26, 2008
Bioinformatics 2008 24(7):1016-1017; doi:10.1093/bioinformatics/btn073
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

SubSeqer: a graph-based approach for the detection and identification of repetitive elements in low-complexity sequences

David He 1,2 and John Parkinson 1,2,3,*

1Program in Molecular Structure and Function, Hospital for Sick Children, 2Department of Molecular Genetics and 3Department of Biochemistry, University of Toronto, Toronto, Canada

*To whom correspondence should be addressed.


  Abstract

Summary: Low-complexity, repetitive protein sequences with a limited amino acid palette are abundant in nature, and many of them play an important role in the structure and function of certain types of proteins. However, such repetitive sequences often do not have rigidly defined motifs. Consequently, the identification of these low-complexity repetitive elements has proven challenging for existing pattern-matching algorithms. Here we introduce a new web-tool SubSeqer (http://compsysbio.org/subseqer/) which uses graphical visualization methods borrowed from protein interaction studies to identify and characterize repetitive elements in low-complexity sequences. Given their abundance, we suggest that SubSeqer represents a valuable resource for the study of typically neglected low-complexity sequences.

Contact: jparkin{at}sickkids.ca

Associate Editor: Limsoon Wong

Received on October 22, 2007; revised on January 25, 2008; accepted on February 24, 2008

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