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

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 dis...

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Bibliographic Details
Published in:Genome Biology Vol. 20; no. 1; p. 223
Main Authors: Esposito, Daniel, Weile, Jochen, Shendure, Jay, Starita, Lea M., Papenfuss, Anthony T., Roth, Frederick P., Fowler, Douglas M., Rubin, Alan F.
Format: Journal Article
Language:English
Published: London BioMed Central 04.11.2019
Springer Nature B.V
BMC
Subjects:
Animal Genetics and Genomics
Application programming interface
Bioinformatics
Biomedical and Life Sciences
biomedical research
data collection
Databases, Genetic
Datasets
Deep mutational scanning
Design
Enzymes
Evolutionary Biology
Experiments
Genes
Genetic Variation
genome
Genome interpretation
Genomics
Human Genetics
Large-scale mutagenesis
Life Sciences
Massively parallel reporter assays
MAVE
Metadata
Microbial Genetics and Genomics
Multiplexed assay of variant effect
Plant Genetics and Genomics
Proteins
Python
Relational data bases
Software
MAVE
Deep mutational scanning
Large-scale mutagenesis
Genome interpretation
Multiplexed assay of variant effect
Personalized medicine
Massively parallel reporter assays
ISSN:1474-760X, 1474-7596, 1474-760X
Online Access:Get full text
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Description
Summary: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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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1845-6