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Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework
The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735928/ https://www.ncbi.nlm.nih.gov/pubmed/29254494 http://dx.doi.org/10.1186/s13073-017-0509-y |
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author | Glusman, Gustavo Rose, Peter W. Prlić, Andreas Dougherty, Jennifer Duarte, José M. Hoffman, Andrew S. Barton, Geoffrey J. Bendixen, Emøke Bergquist, Timothy Bock, Christian Brunk, Elizabeth Buljan, Marija Burley, Stephen K. Cai, Binghuang Carter, Hannah Gao, JianJiong Godzik, Adam Heuer, Michael Hicks, Michael Hrabe, Thomas Karchin, Rachel Leman, Julia Koehler Lane, Lydie Masica, David L. Mooney, Sean D. Moult, John Omenn, Gilbert S. Pearl, Frances Pejaver, Vikas Reynolds, Sheila M. Rokem, Ariel Schwede, Torsten Song, Sicheng Tilgner, Hagen Valasatava, Yana Zhang, Yang Deutsch, Eric W. |
author_facet | Glusman, Gustavo Rose, Peter W. Prlić, Andreas Dougherty, Jennifer Duarte, José M. Hoffman, Andrew S. Barton, Geoffrey J. Bendixen, Emøke Bergquist, Timothy Bock, Christian Brunk, Elizabeth Buljan, Marija Burley, Stephen K. Cai, Binghuang Carter, Hannah Gao, JianJiong Godzik, Adam Heuer, Michael Hicks, Michael Hrabe, Thomas Karchin, Rachel Leman, Julia Koehler Lane, Lydie Masica, David L. Mooney, Sean D. Moult, John Omenn, Gilbert S. Pearl, Frances Pejaver, Vikas Reynolds, Sheila M. Rokem, Ariel Schwede, Torsten Song, Sicheng Tilgner, Hagen Valasatava, Yana Zhang, Yang Deutsch, Eric W. |
author_sort | Glusman, Gustavo |
collection | PubMed |
description | The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-017-0509-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5735928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57359282017-12-21 Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework Glusman, Gustavo Rose, Peter W. Prlić, Andreas Dougherty, Jennifer Duarte, José M. Hoffman, Andrew S. Barton, Geoffrey J. Bendixen, Emøke Bergquist, Timothy Bock, Christian Brunk, Elizabeth Buljan, Marija Burley, Stephen K. Cai, Binghuang Carter, Hannah Gao, JianJiong Godzik, Adam Heuer, Michael Hicks, Michael Hrabe, Thomas Karchin, Rachel Leman, Julia Koehler Lane, Lydie Masica, David L. Mooney, Sean D. Moult, John Omenn, Gilbert S. Pearl, Frances Pejaver, Vikas Reynolds, Sheila M. Rokem, Ariel Schwede, Torsten Song, Sicheng Tilgner, Hagen Valasatava, Yana Zhang, Yang Deutsch, Eric W. Genome Med Opinion The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-017-0509-y) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-18 /pmc/articles/PMC5735928/ /pubmed/29254494 http://dx.doi.org/10.1186/s13073-017-0509-y Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Opinion Glusman, Gustavo Rose, Peter W. Prlić, Andreas Dougherty, Jennifer Duarte, José M. Hoffman, Andrew S. Barton, Geoffrey J. Bendixen, Emøke Bergquist, Timothy Bock, Christian Brunk, Elizabeth Buljan, Marija Burley, Stephen K. Cai, Binghuang Carter, Hannah Gao, JianJiong Godzik, Adam Heuer, Michael Hicks, Michael Hrabe, Thomas Karchin, Rachel Leman, Julia Koehler Lane, Lydie Masica, David L. Mooney, Sean D. Moult, John Omenn, Gilbert S. Pearl, Frances Pejaver, Vikas Reynolds, Sheila M. Rokem, Ariel Schwede, Torsten Song, Sicheng Tilgner, Hagen Valasatava, Yana Zhang, Yang Deutsch, Eric W. Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework |
title | Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework |
title_full | Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework |
title_fullStr | Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework |
title_full_unstemmed | Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework |
title_short | Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework |
title_sort | mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework |
topic | Opinion |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735928/ https://www.ncbi.nlm.nih.gov/pubmed/29254494 http://dx.doi.org/10.1186/s13073-017-0509-y |
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