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A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity
SUMMARY: Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotati...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6596880/ https://www.ncbi.nlm.nih.gov/pubmed/30475984 http://dx.doi.org/10.1093/bioinformatics/bty960 |
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author | Shamsani, Jannah Kazakoff, Stephen H Armean, Irina M McLaren, Will Parsons, Michael T Thompson, Bryony A O’Mara, Tracy A Hunt, Sarah E Waddell, Nicola Spurdle, Amanda B |
author_facet | Shamsani, Jannah Kazakoff, Stephen H Armean, Irina M McLaren, Will Parsons, Michael T Thompson, Bryony A O’Mara, Tracy A Hunt, Sarah E Waddell, Nicola Spurdle, Amanda B |
author_sort | Shamsani, Jannah |
collection | PubMed |
description | SUMMARY: Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes. AVAILABILITY AND IMPLEMENTATION: Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6596880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-65968802019-07-03 A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity Shamsani, Jannah Kazakoff, Stephen H Armean, Irina M McLaren, Will Parsons, Michael T Thompson, Bryony A O’Mara, Tracy A Hunt, Sarah E Waddell, Nicola Spurdle, Amanda B Bioinformatics Applications Notes SUMMARY: Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes. AVAILABILITY AND IMPLEMENTATION: Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-07-01 2018-11-23 /pmc/articles/PMC6596880/ /pubmed/30475984 http://dx.doi.org/10.1093/bioinformatics/bty960 Text en © The Author(s) 2018. 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 | Applications Notes Shamsani, Jannah Kazakoff, Stephen H Armean, Irina M McLaren, Will Parsons, Michael T Thompson, Bryony A O’Mara, Tracy A Hunt, Sarah E Waddell, Nicola Spurdle, Amanda B A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity |
title | A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity |
title_full | A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity |
title_fullStr | A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity |
title_full_unstemmed | A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity |
title_short | A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity |
title_sort | plugin for the ensembl variant effect predictor that uses maxentscan to predict variant spliceogenicity |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6596880/ https://www.ncbi.nlm.nih.gov/pubmed/30475984 http://dx.doi.org/10.1093/bioinformatics/bty960 |
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