title: SubSeqer: a graph-based approach for the detection and identification of repetitive elements in low-complexity sequences.
abstract: 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.
authors.affiliation: Program in Molecular Structure and Function, Hospital for Sick Children, University of Toronto, Toronto, Canada.
authors.forename: David
authors.initials: D
authors.lastname: He
authors.forename: John
authors.initials: J
authors.lastname: Parkinson
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry*; D020449:Repetitive Sequences, Amino Acid; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins