Cargando…
The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations
BACKGROUND: Understanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, cr...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665582/ https://www.ncbi.nlm.nih.gov/pubmed/23819919 http://dx.doi.org/10.1186/1471-2164-14-S3-S4 |
_version_ | 1782271276719538176 |
---|---|
author | Al-Numair, Nouf S Martin, Andrew CR |
author_facet | Al-Numair, Nouf S Martin, Andrew CR |
author_sort | Al-Numair, Nouf S |
collection | PubMed |
description | BACKGROUND: Understanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, creating the SAAPdb resource. RESULTS: We have updated SAAPdb to include 41% more SNPs and 36% more PDs. Introducing a hydrophobic residue on the surface, or a hydrophilic residue in the core, no longer shows significant differences between SNPs and PDs. We have improved some of the analyses significantly enhancing the analysis of clashes and of mutations to-proline and from-glycine. A new web interface has been developed allowing users to analyze their own mutations. Finally we have developed a machine learning method which gives a cross-validated accuracy of 0.846, considerably out-performing well known methods including SIFT and PolyPhen2 which give accuracies between 0.690 and 0.785. CONCLUSIONS: We have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface. Results of machine learning using the structural analysis results to predict pathogenicity considerably outperform other methods. |
format | Online Article Text |
id | pubmed-3665582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36655822013-06-05 The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations Al-Numair, Nouf S Martin, Andrew CR BMC Genomics Research BACKGROUND: Understanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, creating the SAAPdb resource. RESULTS: We have updated SAAPdb to include 41% more SNPs and 36% more PDs. Introducing a hydrophobic residue on the surface, or a hydrophilic residue in the core, no longer shows significant differences between SNPs and PDs. We have improved some of the analyses significantly enhancing the analysis of clashes and of mutations to-proline and from-glycine. A new web interface has been developed allowing users to analyze their own mutations. Finally we have developed a machine learning method which gives a cross-validated accuracy of 0.846, considerably out-performing well known methods including SIFT and PolyPhen2 which give accuracies between 0.690 and 0.785. CONCLUSIONS: We have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface. Results of machine learning using the structural analysis results to predict pathogenicity considerably outperform other methods. BioMed Central 2013-05-28 /pmc/articles/PMC3665582/ /pubmed/23819919 http://dx.doi.org/10.1186/1471-2164-14-S3-S4 Text en Copyright © 2013 Al-Numair and Martin; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Al-Numair, Nouf S Martin, Andrew CR The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations |
title | The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations |
title_full | The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations |
title_fullStr | The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations |
title_full_unstemmed | The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations |
title_short | The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations |
title_sort | saap pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3665582/ https://www.ncbi.nlm.nih.gov/pubmed/23819919 http://dx.doi.org/10.1186/1471-2164-14-S3-S4 |
work_keys_str_mv | AT alnumairnoufs thesaappipelineanddatabasetoolstoanalyzetheimpactandpredictthepathogenicityofmutations AT martinandrewcr thesaappipelineanddatabasetoolstoanalyzetheimpactandpredictthepathogenicityofmutations AT alnumairnoufs saappipelineanddatabasetoolstoanalyzetheimpactandpredictthepathogenicityofmutations AT martinandrewcr saappipelineanddatabasetoolstoanalyzetheimpactandpredictthepathogenicityofmutations |