Mapping MAVE data for use in human genomics applications

Jeremy A. Arbesfeld; Estelle Y. Da; James Stevenson; Kori Kuzma; Anika Paul; Tierra R. Farris; Benjamin J. Capodanno; Sally B. Grindstaff; Kevin P. Riehle; Nuno Saraiva-Agostinho; Jordan F. Safer; Jonathan Casper; Maximilian Haeussler; Aleksandar Milosavljevic; Julia Foreman; Helen V. Firth; Sarah E Hunt; Sumaiya Iqbal; Melissa Cline; Alan F. Rubin; Alex Handler Wagner

doi:10.1186/s13059-025-03647-x

BioinformaticsGenome biologyJun 25, 2025

Peer-reviewed journal article

Mapping MAVE data for use in human genomics applications

Jeremy A. Arbesfeld iD, Estelle Y. Da iD, James Stevenson iD, Kori Kuzma iD, Anika Paul, Tierra R. Farris iD, Benjamin J. Capodanno iD, Sally B. Grindstaff iD, Kevin P. Riehle iD, Nuno Saraiva-Agostinho iD, Jordan F. Safer iD, Jonathan Casper, Maximilian Haeussler iD, Aleksandar Milosavljevic iD, Julia Foreman iD, Helen V. Firth iD, Sarah E Hunt iD, Sumaiya Iqbal iD, Melissa Cline iD, Alan F. Rubin iD, Alex Handler Wagner iD

Abstract

The large-scale experimental measures of variant functional assays submitted to MaveDB have the potential to provide key information for resolving variants of uncertain significance, but the reporting of results relative to assayed sequence hinders their downstream utility. The Atlas of Variant Effects Alliance mapped multiplexed assays of variant effect data to human reference sequences, creating a robust set of machine-readable homology mappings. This method processed approximately 2.5 million protein and genomic variants in MaveDB, successfully mapping 98.61% of examined variants and disseminating data to resources such as the UCSC Genome Browser and Ensembl Variant Effect Predictor.

PDF DOI