MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

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Abstract

Multiplex assays of variant effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here, we present MaveDB ( https://www.mavedb.org), a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first such application, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

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Most cited references 150

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Deep mutational scanning: a new style of protein science.

Douglas Fowler, Stanley Fields (2014)

Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach-'deep mutational scanning'-yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.

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Clustal Omega, accurate alignment of very large numbers of sequences.

Fabian Sievers, Desmond Higgins (2014)

Clustal Omega is a completely rewritten and revised version of the widely used Clustal series of programs for multiple sequence alignment. It can deal with very large numbers (many tens of thousands) of DNA/RNA or protein sequences due to its use of the mBED algorithm for calculating guide trees. This algorithm allows very large alignment problems to be tackled very quickly, even on personal computers. The accuracy of the program has been considerably improved over earlier Clustal programs, through the use of the HHalign method for aligning profile hidden Markov models. The program currently is used from the command line or can be run on line.

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Accurate classification of BRCA1 variants with saturation genome editing

Gregory M. Findlay, Riza M Daza, Beth Martin … (2018)

Variants of uncertain significance (VUS) fundamentally limit the clinical utility of genetic information. The challenge they pose is epitomized by BRCA1, a tumor suppressor in which germline loss-of-function variants predispose women to breast and ovarian cancer. Although BRCA1 has been sequenced in millions of women, the risk associated with most newly observed variants cannot be definitively assigned. Here, we employ saturation genome editing to assay 96.5% of all possible single nucleotide variants (SNVs) in 13 exons encoding functionally critical domains of BRCA1. Functional effects for nearly 4,000 SNVs are bimodally distributed and almost perfectly concordant with established assessments of pathogenicity. Over 400 non-functional missense SNVs are identified, as well as ~300 SNVs that disrupt expression. We predict that these results will be immediately useful for clinical interpretation of BRCA1 variants, and that this paradigm can be extended to overcome the challenge of VUS in additional clinically actionable genes.

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Author and article information

Contributors

Frederick P. Roth: fritz.roth@utoronto.ca

Douglas M. Fowler: dfowler@uw.edu

Alan F. Rubin:

ORCID: http://orcid.org/0000-0003-1474-605X

alan.rubin@wehi.edu.au

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 4 November 2019

Publication date PMC-release: 4 November 2019

Publication date Collection: 2019

Volume: 20

Electronic Location Identifier: 223

Affiliations

[1 ]GRID grid.1042.7, Bioinformatics Division, , The Walter and Eliza Hall Institute of Medical Research, ; Parkville, VIC Australia

[2 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, The Donnelly Centre, , University of Toronto, ; Toronto, ON Canada

[3 ]GRID grid.492573.e, Lunenfeld-Tanenbaum Research Institute, , Sinai Health System, ; Toronto, ON Canada

[4 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Molecular Genetics, , University of Toronto, ; Toronto, ON Canada

[5 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Computer Science, , University of Toronto, ; Toronto, ON Canada

[6 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Genome Sciences, , University of Washington, ; Seattle, WA USA

[7 ]Brotman Baty Institute for Precision Medicine, Seattle, WA USA

[8 ]ISNI 0000000122986657, GRID grid.34477.33, Howard Hughes Medical Institute, , University of Washington, ; Seattle, WA USA

[9 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Medical Biology, , University of Melbourne, ; Melbourne, VIC Australia

[10 ]ISNI 0000000403978434, GRID grid.1055.1, Bioinformatics and Cancer Genomics Laboratory, , Peter MacCallum Cancer Centre, ; Melbourne, VIC Australia

[11 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Sir Peter MacCallum Department of Oncology, , University of Melbourne, ; Melbourne, VIC Australia

[12 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Mathematics and Statistics, , University of Melbourne, ; Melbourne, VIC Australia

[13 ]ISNI 0000 0004 0408 2525, GRID grid.440050.5, Canadian Institute for Advanced Research, ; Toronto, ON Canada

[14 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Bioengineering, , University of Washington, ; Seattle, WA USA

Author information

Alan F. Rubin http://orcid.org/0000-0003-1474-605X

Article

Publisher ID: 1845

DOI: 10.1186/s13059-019-1845-6

PMC ID: 6827219

PubMed ID: 31679514

SO-VID: 0998c03b-dbbb-42fe-9e16-fad18d810a08

License:

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.

History

Date received : 31 March 2019

Date accepted : 1 October 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: R01GM109110

Award ID: HG004233

Award ID: RM1HG010461

Award Recipient : Frederick P. Roth Douglas M. Fowler

Funded by: Australian National Health and Medical Research Council

Award ID: 1054618

Award ID: 1116955

Award Recipient : Anthony T. Papenfuss

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: deep mutational scanning,massively parallel reporter assays,large-scale mutagenesis,mave,multiplexed assay of variant effect,genome interpretation,personalized medicine

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ScienceOpen disciplines: Genetics

Keywords: deep mutational scanning, massively parallel reporter assays, large-scale mutagenesis, mave, multiplexed assay of variant effect, genome interpretation, personalized medicine

MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect

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Genome Integrity

Most cited references 150

Deep mutational scanning: a new style of protein science.

Clustal Omega, accurate alignment of very large numbers of sequences.

Accurate classification of BRCA1 variants with saturation genome editing

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