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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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.
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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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