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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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.