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

David He | Zendy; John Parkinson | Zendy

Open Access

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

Author(s) -

David He,

John Parkinson

Publication year - 2008

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btn073

Subject(s) - computer science , visualization , identification (biology) , palette (painting) , graph , computational biology , matching (statistics) , computational complexity theory , interspersed repeat , theoretical computer science , biology , algorithm , data mining , genome , mathematics , genetics , statistics , botany , gene , human genome , operating system

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.

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