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William Bakhache (IBMC Institute of Molecular and Cell Biology | Mechanisms of Positive-Strand RNA Virus Laboratory (M+RVL)) “Genotype-by-inhibitor interactions to dissect enterovirus replication” - presentation to the Atlas of Variant Effects (AVE) Virology Interest Group August 07 2026

🎉 New preprint: “Blending physics-based and inverse folding models to disentangle variant effects on stability and function” ➡️ Read it on https://lnkd.in/dCDw-3nb Together with Xavier Soler Sanchis...

The development of deep mutational scanning (DMS) approaches has led to a fundamental shift in our ability to connect viral genotype and phenotype (1) and accelerated our understanding of viral evolut...

Unmasking the Genome is a four-day advanced workshop designed for researchers working at the leading edge of genomic science. This programme brings together expertise from Google DeepMind, leaders in ...

Background With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evidence which can be used in clinical variant classification. Increasingly, multiple published MAVEs are available for the same gene, sometimes measuring different aspects of variant impact. When multiple functional roles of a gene need to be considered, combining data from multiple MAVEs may provide a more comprehensive measure of the consequence of a genetic variant, which could impact variant classifications. Methods We curated published datasets from five MAVEs for the gene TP53, two MAVEs for LDLR and two MAVEs for PTEN. Statistical methods (principal component analysis), unsupervised learning (k-means clustering), and supervised learning (Naïve Bayes and random forest classifiers) were used to integrate multiple MAVE datasets. The utility of MAVE integration methods were assessed using standard metrics (sensitivity, specificity, etc) as well as evidence strength in a putative variant classification framework. Results Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We also present a web applet that allows users to test various methods for combining score sets from multiple assays, calculate integrated functional scores for all variants, and assess whether combining data enables the application of stronger evidence for pathogenicity or benignity. In general, supervised learning methods such as random forest led to improved variant classification as compared to any individual MAVE dataset. Conclusions By following the steps outlined herein with appropriate guardrails, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.

Background With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant e...

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