Cell Genomics (Nov 2021)
The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification
- Alex H. Wagner,
- Lawrence Babb,
- Gil Alterovitz,
- Michael Baudis,
- Matthew Brush,
- Daniel L. Cameron,
- Melissa Cline,
- Malachi Griffith,
- Obi L. Griffith,
- Sarah E. Hunt,
- David Kreda,
- Jennifer M. Lee,
- Stephanie Li,
- Javier Lopez,
- Eric Moyer,
- Tristan Nelson,
- Ronak Y. Patel,
- Kevin Riehle,
- Peter N. Robinson,
- Shawn Rynearson,
- Helen Schuilenburg,
- Kirill Tsukanov,
- Brian Walsh,
- Melissa Konopko,
- Heidi L. Rehm,
- Andrew D. Yates,
- Robert R. Freimuth,
- Reece K. Hart
Affiliations
- Alex H. Wagner
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA; The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43215, USA; Corresponding author
- Lawrence Babb
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Corresponding author
- Gil Alterovitz
- Harvard Medical School, Boston, MA 02115, USA; Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Michael Baudis
- University of Zurich and Swiss Institute of Bioinformatics, Zurich, Switzerland
- Matthew Brush
- Oregon Health & Science University, Portland, OR 97239, USA
- Daniel L. Cameron
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia
- Melissa Cline
- UC Santa Cruz Genomics Institute, Santa Cruz, CA 95060, USA
- Malachi Griffith
- Washington University School of Medicine, St. Louis, MO 63108, USA
- Obi L. Griffith
- Washington University School of Medicine, St. Louis, MO 63108, USA
- Sarah E. Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- David Kreda
- Department of Biomedical Informatics, Harvard Medical School, Boston MA 02115, USA
- Jennifer M. Lee
- Essex Management LLC and National Cancer Institute, Rockville, MD 20850, USA
- Stephanie Li
- The Global Alliance for Genomics and Health, Toronto, ON, Canada
- Javier Lopez
- Genomics England, London EC1M 6BQ, UK
- Eric Moyer
- National Center for Biotechnology Information, National Library of Medicine National Institutes of Health, Bethesda, MD 20894, USA
- Tristan Nelson
- Geisinger Health, Danville, PA 17822, USA
- Ronak Y. Patel
- Baylor College of Medicine, Houston, TX 77030, USA
- Kevin Riehle
- Baylor College of Medicine, Houston, TX 77030, USA
- Peter N. Robinson
- Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
- Shawn Rynearson
- Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA
- Helen Schuilenburg
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Kirill Tsukanov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Brian Walsh
- Oregon Health & Science University, Portland, OR 97239, USA
- Melissa Konopko
- The Global Alliance for Genomics and Health, Toronto, ON, Canada
- Heidi L. Rehm
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Genomic Medicine, Massachusetts General Hospital, Cambridge, MA 02142, USA
- Andrew D. Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- Robert R. Freimuth
- Center for Individualized Medicine, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA
- Reece K. Hart
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; MyOme, Inc., Menlo Park, CA 94070, USA; Corresponding author
- Journal volume & issue
-
Vol. 1,
no. 2
p. 100027
Abstract
Summary: Maximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced “verse”), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH).
