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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 pr...

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Detalles Bibliográficos
Autores principales: Palopoli, Nicolas, Lythgow, Kieren T., Edwards, Richard J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495300/
https://www.ncbi.nlm.nih.gov/pubmed/25792551
http://dx.doi.org/10.1093/bioinformatics/btv155
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author Palopoli, Nicolas
Lythgow, Kieren T.
Edwards, Richard J.
author_facet Palopoli, Nicolas
Lythgow, Kieren T.
Edwards, Richard J.
author_sort Palopoli, Nicolas
collection PubMed
description 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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spelling pubmed-44953002015-07-09 QSLiMFinder: improved short linear motif prediction using specific query protein data Palopoli, Nicolas Lythgow, Kieren T. Edwards, Richard J. Bioinformatics Original Papers 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. Oxford University Press 2015-07-15 2015-03-19 /pmc/articles/PMC4495300/ /pubmed/25792551 http://dx.doi.org/10.1093/bioinformatics/btv155 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Palopoli, Nicolas
Lythgow, Kieren T.
Edwards, Richard J.
QSLiMFinder: improved short linear motif prediction using specific query protein data
title QSLiMFinder: improved short linear motif prediction using specific query protein data
title_full QSLiMFinder: improved short linear motif prediction using specific query protein data
title_fullStr QSLiMFinder: improved short linear motif prediction using specific query protein data
title_full_unstemmed QSLiMFinder: improved short linear motif prediction using specific query protein data
title_short QSLiMFinder: improved short linear motif prediction using specific query protein data
title_sort qslimfinder: improved short linear motif prediction using specific query protein data
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495300/
https://www.ncbi.nlm.nih.gov/pubmed/25792551
http://dx.doi.org/10.1093/bioinformatics/btv155
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