Douglas M. Fowler

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.

This paper measures ERK activation state using a sequencing-based assay to identify BRAF variants affecting signaling and drug sensitivity.

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

The paper evaluates how different truthset strategies affect evidence points (EPs) in clinical validation of functional assays across 2,120 gene-truthset-assay combinations.

The paper establishes consensus guidelines for constructing truthsets to validate functional assays in cancer variant interpretation, focusing on missense variants and clinical validation frameworks.

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 uses saturation genome editing to assess the functional impact of RAD51D and XRCC2 variants, showing high accuracy in distinguishing pathogenic from benign variants.

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

The paper develops a high-throughput Visual Cell Sorting workflow to link nuclear compartment morphology to single-nucleus transcriptomes, identifying a conserved transcriptional signature of nucleolar size linked to proteostasis.

The paper recalibrates BAP1 MAVE data for different disease phenotypes to enable accurate variant classification across related disorders.

This study explores patient experiences with genetic variant reclassifications, finding that re-contact practices significantly impact trust and perceived value of genetic testing.

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.

STARCall processes in situ sequencing data by integrating image stitching, alignment, and read calling to improve genotype-to-phenotype mapping with reduced error and increased genotyped cells.

This paper improves variant effect predictors by calibrating them gene- and domain-aware, increasing evidence assignment and accuracy for variant classification.

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 develops iPSC-SGE to assess variant effects in diverse genetic and cell contexts, identifying pathogenic variants and essential disease genes.

The paper uses saturation genome editing to evaluate BARD1 variants, resolving most variants of uncertain significance and confirming their role in tumor suppression.

The paper develops a method to discover new effector handles for proteome editing by combining de novo protein design with multiplex screening, identifying handles for degradation and stabilization of target proteins.

The paper systematically identifies degrons in human cytosolic proteins and links them to disease mechanisms through machine learning and structural analysis.

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 paper introduces LABEL-seq, a platform for multimodal profiling of protein variants, revealing how cancer-related mutations affect activity, interactions, and druggability.

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.

Pacybara improves the accuracy of long-read sequencing for barcoded mutagenized libraries by addressing sequencing errors and non-unique barcodes.

The paper creates a functional map of all possible missense variants in CHK2 to assess their impact on cancer risk.

The paper introduces Sceptic, a supervised pseudotime analysis model that improves prediction accuracy over existing methods for scRNA-seq and single-nucleus image data.

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