ParkinsonCanadaV1, Main, Exploration, bibRecord, 002235

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

Identifieur interne : 002235 ( Main/Exploration ); précédent : 002234; suivant : 002236

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

Auteurs : David He [Canada] ; John Parkinson [Canada]

Source :

Bioinformatics [ 1367-4803 ] ; 2008-04-01.

RBID : ISTEX:C84798FA608625FBB9AFBB580ED786705CBC8ABC

English descriptors

KwdEn :
- Algorithms, Amino Acid Sequence, Molecular Sequence Data, Proteins (chemistry), Repetitive Sequences, Amino Acid, Sequence Alignment (methods), Sequence Analysis, Protein (methods), Software.
MESH :
- chemical , chemistry : Proteins.
- methods : Sequence Alignment, Sequence Analysis, Protein.
- Algorithms, Amino Acid Sequence, Molecular Sequence Data, Repetitive Sequences, Amino Acid, Software.

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@sickkids.ca

Url:

https://api-v5.istex.fr/document/C84798FA608625FBB9AFBB580ED786705CBC8ABC/fulltext/pdf

DOI: 10.1093/bioinformatics/btn073

Affiliations:

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<front><div type="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@sickkids.ca</div>
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La maladie de Parkinson au Canada (serveur d'exploration)

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

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

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

	La maladie de Parkinson au Canada (serveur d'exploration)
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