Alan F. Rubin

The paper measures variant effects across the MAPK pathway in human cells, revealing how cell context influences variant function and identifying 14,412 previously unknown dominant negative variants.

MaveMD provides a clinical interface for MAVE data to classify variants of uncertain significance and improve genetic medicine outcomes.

The paper introduces evoPLM-Tree, a tree-aware language model that predicts viral mutations by incorporating phylogenetic context, showing it captures mutational patterns and functional constraints in SARS-CoV-2 spike protein evolution.

Abundance scores for 8,891 TSC2 missense variants, plus mTOR pathway activity scores for 391, were calibrated to ACMG/AMP evidence. They putatively reclassify 212 of 276 clinical-cohort missense VUS.

The paper classifies G6PD variants using functional assays to identify clinically relevant variants and those unlikely to contribute to G6PD deficiency.

The paper introduces VIS-seq, an image-based method to assess variant effects on cellular phenotypes, revealing multidimensional variant impacts across different diseases.

CountESS provides a flexible, graphical pipeline for DMS analysis, supporting diverse workflows and input formats to generate variant scores.

VEFill is a gradient boosting model that imputes missing DMS scores across protein domains, achieving strong predictive performance and generalization to new proteins.

The paper develops a scalable workflow to reclassify variants of uncertain significance using experimental and predictive evidence, resolving 75% of VUS in 40 genes.

The paper presents a workshop-based approach to improve confidence and standardize the use of functional evidence in variant classification through community discussion and consultation.

This paper identifies shared and distinct sequence-function signatures for different modes of hTpoR activation using deep mutational scanning and AlphaFold 3 modeling.

The paper maps MAVE data to human reference sequences to improve variant functional assay utility for genomics applications.

This paper introduces ExCALIBR, a method to calibrate variant scores from functional assays, improving pathogenicity prediction and reducing variants of uncertain significance.

This study identifies barriers to using functional data in clinical variant interpretation and highlights the need for better access and standardized interpretation guidelines.

The paper surveys genetic professionals on functional evidence use in variant classification and identifies the need for expert recommendations and guidelines.

The paper analyzes variant classification disparities between European-like and non-European-like genetic ancestries and shows that MAVE data can reduce these disparities in VUS reclassification.

This study identifies 13 new proinsulin-derived HIPs recognized by human islet-infiltrating CD4+ T cells in T1D, highlighting proinsulin C-peptide's central role in HIP formation.

The paper generates a homozygous BRCA1-methylated OVCAR8 cell line using CRISPR editing to study meBRCA1 stability and PARPi resistance.

The paper introduces the Genomics 2 Proteins Portal, which links genetic variants to protein sequences and structures to aid in understanding disease mechanisms and therapeutic design.

The paper introduces MultiSTEP, a method to assess variant effects in secreted proteins, and applies it to coagulation factor IX, identifying functional variants and reclassifying uncertain ones.

The paper uses deep mutational scanning to analyze CYP2C19 variants, revealing a tradeoff between substrate specificity and protein abundance.

The paper improves variant impact prediction by integrating deep mutational scanning and low-throughput alanine scanning data across multiple proteins.

The paper introduces sccomp, a method for differential composition and variability analysis in cell omics data, showing improved performance over existing methods and identifying novel biological insights in breast cancer.

MaveDB v2 curates over three million variant effects from MAVE studies to facilitate analysis and dissemination of functional assay data.

MaveRegistry is a platform for tracking and sharing multiplexed variant effect assays to reduce redundancy and enable collaboration in variant interpretation.

The paper introduces MaveDB, a public repository for MAVE data, and MaveVis, a tool to visualize and contextualize variant effect maps.

Enrich2 provides a statistical framework for analyzing deep mutational scanning data, improving variant score accuracy and hypothesis testing.