Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: A proposed framework

Gustavo Glusman (Lead / Corresponding author), Peter W. Rose, Andreas Prlić, Jennifer Dougherty, José M. Duarte, Andrew S. Hoffman, Geoffrey J. Barton, Emøke Bendixen, Timothy Bergquist, Christian Bock, Elizabeth Brunk, Marija Buljan, Stephen K. Burley, Binghuang Cai, Hannah Carter, Jian Jiong Gao, Adam Godzik, Michael Heuer, Michael Hicks, Thomas HrabeRachel Karchin, Julia Koehler Leman, Lydie Lane, David L. Masica, Sean D. Mooney, John Moult, Gilbert S. Omenn, Frances Pearl, Vikas Pejaver, Sheila M. Reynolds, Ariel Rokem, Torsten Schwede, Sicheng Song, Hagen Tilgner, Yana Valasatava, Yang Zhang, Eric W. Deutsch

Research output: Contribution to journalArticle

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Abstract

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.

Original languageEnglish
Article number113
Pages (from-to)1-10
Number of pages10
JournalGenome Medicine
Volume9
Issue number1
DOIs
Publication statusPublished - 18 Dec 2017

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Proteins
Education
Protein Databases
Precision Medicine
Mutation
Inborn Genetic Diseases
Information Storage and Retrieval
Genomics
Registries
Catalytic Domain
Research Personnel
Databases
Enzymes
Pharmaceutical Preparations
Genes

Cite this

Glusman, G., Rose, P. W., Prlić, A., Dougherty, J., Duarte, J. M., Hoffman, A. S., ... Deutsch, E. W. (2017). Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: A proposed framework. Genome Medicine, 9(1), 1-10. [113]. https://doi.org/10.1186/s13073-017-0509-y
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, Jian Jiong ; 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. In: Genome Medicine. 2017 ; Vol. 9, No. 1. pp. 1-10.
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abstract = "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.",
author = "Gustavo Glusman and Rose, {Peter W.} and Andreas Prlić and Jennifer Dougherty and Duarte, {Jos{\'e} M.} and Hoffman, {Andrew S.} and Barton, {Geoffrey J.} and Em{\o}ke Bendixen and Timothy Bergquist and Christian Bock and Elizabeth Brunk and Marija Buljan and Burley, {Stephen K.} and Binghuang Cai and Hannah Carter and Gao, {Jian Jiong} and Adam Godzik and Michael Heuer and Michael Hicks and Thomas Hrabe and Rachel Karchin and Leman, {Julia Koehler} and Lydie Lane and Masica, {David L.} and Mooney, {Sean D.} and John Moult and Omenn, {Gilbert S.} and Frances Pearl and Vikas Pejaver and Reynolds, {Sheila M.} and Ariel Rokem and Torsten Schwede and Sicheng Song and Hagen Tilgner and Yana Valasatava and Yang Zhang and Deutsch, {Eric W.}",
note = "This work has been funded in part by the National Science Foundation under grant numbers IIS-1636903 and IIS-1636804. JG was supported by a National Cancer Institute Cancer Center Core Grant (P30-CA008748). GG was supported by the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number OT3TR002026. The content is solely the responsibility of the authors and does not nec",
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Glusman, G, Rose, PW, Prlić, A, Dougherty, J, Duarte, JM, Hoffman, AS, Barton, GJ, Bendixen, E, Bergquist, T, Bock, C, Brunk, E, Buljan, M, Burley, SK, Cai, B, Carter, H, Gao, JJ, Godzik, A, Heuer, M, Hicks, M, Hrabe, T, Karchin, R, Leman, JK, Lane, L, Masica, DL, Mooney, SD, Moult, J, Omenn, GS, Pearl, F, Pejaver, V, Reynolds, SM, Rokem, A, Schwede, T, Song, S, Tilgner, H, Valasatava, Y, Zhang, Y & Deutsch, EW 2017, 'Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: A proposed framework', Genome Medicine, vol. 9, no. 1, 113, pp. 1-10. https://doi.org/10.1186/s13073-017-0509-y

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation : A proposed framework. / Glusman, Gustavo (Lead / Corresponding author); 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, Jian Jiong; 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.

In: Genome Medicine, Vol. 9, No. 1, 113, 18.12.2017, p. 1-10.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation

T2 - A proposed framework

AU - Glusman, Gustavo

AU - Rose, Peter W.

AU - Prlić, Andreas

AU - Dougherty, Jennifer

AU - Duarte, José M.

AU - Hoffman, Andrew S.

AU - Barton, Geoffrey J.

AU - Bendixen, Emøke

AU - Bergquist, Timothy

AU - Bock, Christian

AU - Brunk, Elizabeth

AU - Buljan, Marija

AU - Burley, Stephen K.

AU - Cai, Binghuang

AU - Carter, Hannah

AU - Gao, Jian Jiong

AU - Godzik, Adam

AU - Heuer, Michael

AU - Hicks, Michael

AU - Hrabe, Thomas

AU - Karchin, Rachel

AU - Leman, Julia Koehler

AU - Lane, Lydie

AU - Masica, David L.

AU - Mooney, Sean D.

AU - Moult, John

AU - Omenn, Gilbert S.

AU - Pearl, Frances

AU - Pejaver, Vikas

AU - Reynolds, Sheila M.

AU - Rokem, Ariel

AU - Schwede, Torsten

AU - Song, Sicheng

AU - Tilgner, Hagen

AU - Valasatava, Yana

AU - Zhang, Yang

AU - Deutsch, Eric W.

N1 - This work has been funded in part by the National Science Foundation under grant numbers IIS-1636903 and IIS-1636804. JG was supported by a National Cancer Institute Cancer Center Core Grant (P30-CA008748). GG was supported by the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number OT3TR002026. The content is solely the responsibility of the authors and does not nec

PY - 2017/12/18

Y1 - 2017/12/18

N2 - 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.

AB - 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.

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DO - 10.1186/s13073-017-0509-y

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JF - Genome Medicine

SN - 1756-994X

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