Mapping Mave Data for Use in Human Genomics Applications

Authors

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

Language

English

Publication Date

6-25-2025

Journal

Genome Biology

DOI

10.1186/s13059-025-03647-x

PMID

40563119

PMCID

PMC12188674

PubMedCentral® Posted Date

6-25-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Background: Experimental data from functional assays have a critical role in interpreting the impact of genetic variants. Assay data must be unambiguously mapped to a reference genome to make it accessible, but it is often reported relative to assay-specific sequences, complicating downstream use and integration of variant data across resources. To make multiplexed assays of variant effect (MAVE) data more broadly available to the research and clinical communities, the Atlas of Variant Effects Alliance mapped MAVE data from the MaveDB community database to human reference sequences, creating an extensive set of machine-readable homology mappings that are incorporated into widely used human genomics applications.

Results: Here, we map approximately 9.0 million individual protein and nucleotide variants in MaveDB to the human genome, describing the examined variants with respect to human reference sequences while preserving the data provenance of the original MAVE sequences. We then disseminate the results to major genomic resources including the Genomics 2 Proteins Portal, UCSC Genome Browser, Ensembl Variant Effect Predictor, and DECIPHER platform. Within these applications, MAVE variants can now be visualized and integrated with other relevant clinical and biological data, making additional knowledge available when performing variant interpretation and conducting other research activities.

Conclusions: Mapping MAVE variants to human reference sequences and sharing the mapped dataset with several key human genomics applications enables a new and diverse set of applications for MAVE data. This study provides increased access to functional data that can assist in clinical variant interpretation pipelines and enable biomedical research and discovery.

Keywords

Humans, Genomics, Genome, Human, Databases, Genetic, Genetic Variation, Chromosome Mapping, Functional assay, Genomics, Genomic medicine, Multiplexed assays of variant effect, Variation representation specification, Deep mutational scanning, Massively parallel reporter assays, Global Alliance for Genomics and Health

Published Open-Access

yes

Recommended Citation

Arbesfeld, Jeremy A; Da, Estelle Y; Stevenson, James S; et al., "Mapping Mave Data for Use in Human Genomics Applications" (2025). Faculty and Staff Publications. 5076.
https://digitalcommons.library.tmc.edu/baylor_docs/5076