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Human Splicing Finder: an online bioinformatics tool to predict splicing signals

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt...

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Autores principales: Desmet, François-Olivier, Hamroun, Dalil, Lalande, Marine, Collod-Béroud, Gwenaëlle, Claustres, Mireille, Béroud, Christophe
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2685110/
https://www.ncbi.nlm.nih.gov/pubmed/19339519
http://dx.doi.org/10.1093/nar/gkp215
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author Desmet, François-Olivier
Hamroun, Dalil
Lalande, Marine
Collod-Béroud, Gwenaëlle
Claustres, Mireille
Béroud, Christophe
author_facet Desmet, François-Olivier
Hamroun, Dalil
Lalande, Marine
Collod-Béroud, Gwenaëlle
Claustres, Mireille
Béroud, Christophe
author_sort Desmet, François-Olivier
collection PubMed
description Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.
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spelling pubmed-26851102009-05-21 Human Splicing Finder: an online bioinformatics tool to predict splicing signals Desmet, François-Olivier Hamroun, Dalil Lalande, Marine Collod-Béroud, Gwenaëlle Claustres, Mireille Béroud, Christophe Nucleic Acids Res Methods Online Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project. Oxford University Press 2009-05 2009-04-01 /pmc/articles/PMC2685110/ /pubmed/19339519 http://dx.doi.org/10.1093/nar/gkp215 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Desmet, François-Olivier
Hamroun, Dalil
Lalande, Marine
Collod-Béroud, Gwenaëlle
Claustres, Mireille
Béroud, Christophe
Human Splicing Finder: an online bioinformatics tool to predict splicing signals
title Human Splicing Finder: an online bioinformatics tool to predict splicing signals
title_full Human Splicing Finder: an online bioinformatics tool to predict splicing signals
title_fullStr Human Splicing Finder: an online bioinformatics tool to predict splicing signals
title_full_unstemmed Human Splicing Finder: an online bioinformatics tool to predict splicing signals
title_short Human Splicing Finder: an online bioinformatics tool to predict splicing signals
title_sort human splicing finder: an online bioinformatics tool to predict splicing signals
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2685110/
https://www.ncbi.nlm.nih.gov/pubmed/19339519
http://dx.doi.org/10.1093/nar/gkp215
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