Year: 2023
Type: preprint
Abstract: Multiplexed Assays of Variant Effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines has led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.
Source: arXiv (Cornell University)
Institutions: Broad Institute, Harvard University, Massachusetts General Hospital, Stanford Medicine, Stanford University, Nationwide Children's Hospital, Jackson Laboratory, Cambridge University Hospitals NHS Foundation Trust, Wellcome Sanger Institute, University of Washington, Baylor College of Medicine, Barcelona Institute of Science and Technology, Institute for Bioengineering of Catalonia, Vanderbilt University Medical Center, The University of Melbourne
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Boettcher Foundation, Johns Hopkins University, Ministerio de Ciencia, Innovación y Universidades, Australian Government, University of Toronto, Novo Nordisk, Novo Nordisk Fonden, National Institutes of Health, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases
Awards: G2903586903, Bridging the gap between type 2 diabetes GWAS and therapeutic targets, G4336914467, A pipeline for identifying disease-causing variants in transmembrane proteins, G4772171027, The Role of RBM20 Sequence and Expression in Dilated Cardiomyopathies, G5211151649, G7855289298, Baylor College of Medicine/Stanford University Clinical Genome Resource (CLINGEN), Technology to understand genetic variant effects in context, The Center for Actionable Variant Analysis; measuring variant function at scale, Development and validation of a computable knowledge framework for genomic medicine, Systematically mapping variant effects for cardiovascular genes
OpenAlex ID: W4382490960