QSLiMFinder: improved short linear motif prediction using specific query protein data
QSLiMFinder: improved short linear motif prediction using specific query protein data
MOTIVATION: The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions.
RESULTS: QSLiMFinder was extensively benchmarked using known SLiM-containing proteins and simulated protein interaction datasets of real human proteins. Exploiting prior knowledge of a query protein likely to be involved in a SLiM-mediated interaction increased the proportion of true positives correctly returned and reduced the proportion of datasets returning a false positive prediction. The biggest improvement was seen if a short region of the query protein flanking the interaction site was known.
AVAILABILITY AND IMPLEMENTATION: All the tools and data used in this study, including QSLiMFinder and the SLiMBench benchmarking software, are freely available under a GNU license as part of SLiMSuite, at: http://bioware.soton.ac.uk.
CONTACT: richard.edwards@unsw.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
2284-2293
Palopoli, Nicolas
227c4548-0b1a-4dc9-b628-74cf48c878d5
Lythgow, Kieren T.
3aeb00d0-0d35-4648-a215-3bafc85205af
Edwards, Richard J.
9d25e74f-dc0d-455a-832c-5f363d864c43
15 July 2015
Palopoli, Nicolas
227c4548-0b1a-4dc9-b628-74cf48c878d5
Lythgow, Kieren T.
3aeb00d0-0d35-4648-a215-3bafc85205af
Edwards, Richard J.
9d25e74f-dc0d-455a-832c-5f363d864c43
Palopoli, Nicolas, Lythgow, Kieren T. and Edwards, Richard J.
(2015)
QSLiMFinder: improved short linear motif prediction using specific query protein data.
Bioinformatics, 31 (14), .
(doi:10.1093/bioinformatics/btv155).
(PMID:25792551)
Abstract
MOTIVATION: The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions.
RESULTS: QSLiMFinder was extensively benchmarked using known SLiM-containing proteins and simulated protein interaction datasets of real human proteins. Exploiting prior knowledge of a query protein likely to be involved in a SLiM-mediated interaction increased the proportion of true positives correctly returned and reduced the proportion of datasets returning a false positive prediction. The biggest improvement was seen if a short region of the query protein flanking the interaction site was known.
AVAILABILITY AND IMPLEMENTATION: All the tools and data used in this study, including QSLiMFinder and the SLiMBench benchmarking software, are freely available under a GNU license as part of SLiMSuite, at: http://bioware.soton.ac.uk.
CONTACT: richard.edwards@unsw.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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Accepted/In Press date: 16 March 2015
e-pub ahead of print date: 19 March 2015
Published date: 15 July 2015
Organisations:
Molecular and Cellular
Identifiers
Local EPrints ID: 378592
URI: http://eprints.soton.ac.uk/id/eprint/378592
ISSN: 1367-4803
PURE UUID: 5f5efcc7-2ca2-4e5d-a912-78ad991dc8b4
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Date deposited: 10 Jul 2015 10:36
Last modified: 14 Mar 2024 20:25
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Contributors
Author:
Nicolas Palopoli
Author:
Kieren T. Lythgow
Author:
Richard J. Edwards
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