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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...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2009
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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. |
format | Text |
id | pubmed-2685110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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