Abstract
SummaryFully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes.Availability and implementationMaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Driven by the advancement of genomic sequencing technologies, and by rapid increases in the number of identified disease-related genes and variants (Brunham and Hayden, 2013), clinical genetic testing is gaining increasingly broad use
In ClinVar, a popular resource for submitting genetic variants seen in clinical settings, approximately 40% of all variants are missense variants (Landrum et al, 2016)
Functional evidence is considered important under the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines (Richards et al, 2015), and could help shift many variants of uncertain significance’ (VUS) variants to more clinically useful categories
Summary
Driven by the advancement of genomic sequencing technologies, and by rapid increases in the number of identified disease-related genes and variants (Brunham and Hayden, 2013), clinical genetic testing is gaining increasingly broad use. Experimental functional assays can detect far more disease-associated variants with high confidence than can computational approaches (Sun et al, 2016). Multiplexed assays of variant effect (MAVEs) provide a systematic, experimental approach to study most missense variants in selected gene targets (Starita et al, 2017). Some variant effect maps have been shown to outperform smaller-scale validated in vitro functional assays in quantitatively predicting disease phenotypes (Sun et al, 2020). To explore the clinical relevance of potential target genes and to identify scalable functional assays for these genes, information must be assembled from multiple database and literature resources. We developed MaveQuest, a webbased service simplifying access to diverse aggregated information about potential functional assays, disease phenotypes and clinical relevance of genes for systematic variant effect mapping.
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