Classifying variants of uncertain significance (VUS) in genetic testing is a significant challenge, hindering accurate diagnoses and effective treatment decisions. The volume of genetic data and the complexity of variant interpretation require extensive resources and expertise. Existing methods often lack comprehensive functional evidence, leading to delays and inefficiencies in clinical genetics and drug development.

MAVEvidence is a functional evidence resource designed for the clinical genetics community, providing curated information on genetic variants derived from high-throughput studies and rigorous analysis. The platform aggregates and analyzes data to classify VUS, enhancing the efficiency of genetic testing and drug development. By leveraging integrated computational modeling, massively multiplexed functional genomics experiments, and human-centric biobank data, MAVEvidence offers insights into the consequences of genetic variation. The platform's applications include RareScan for VUS classification and drug response prediction, Genable for drug target discovery, and StratiVar for optimizing clinical trial enrollment.

Comprehensive curation of functional evidence from high-throughput studies of genetic variants.

Integrated computational modeling to elucidate the consequences of genetic variation.

Massively multiplexed functional genomics experiments for variant analysis.

Human-centric biobank data integration for enhanced insights.

RareScan platform for classifying VUS and predicting drug responses.

Genable platform for prioritizing high-confidence drug targets.

StratiVar platform for assessing susceptibility to therapeutics and optimizing clinical trial enrollment.

The primary users are clinical geneticists, researchers, and drug developers involved in genetic testing, variant interpretation, and precision medicine.