Alistair S. Dunham

The paper presents a genome-scale CRISPRi perturbation map in human pluripotent stem cells using single-cell RNA sequencing to study trans gene regulation across diverse genetic backgrounds.

This paper introduces Sequence UNET, a deep learning model for variant effect prediction that is highly scalable and computationally efficient, achieving comparable performance to existing methods on ClinVar data.

The paper uses AlphaFold2 to predict structures for 65,484 human protein interactions, identifying high confidence models and disease-related mutations.

The paper systematically analyzes proteomic and genomic data of SARS-CoV-2 variants to identify how mutations affect viral gene expression, host interactions, and immune evasion.

The paper defines a mutational landscape of amino acid functions using deep mutational scanning data and identifies 100 functional amino acid subtypes.

The paper generates and provides computational predictions of the effects of all possible SARS-CoV-2 missense variants to support variant analysis and experimental validation.

The paper analyzes structural variation in 911 human samples from 54 populations, identifying novel variants and introgressed elements, and highlights the need for diverse high-quality genomes.

The paper assesses how genetic background affects gene deletion phenotypes in yeast, finding significant variation across strains and conditions.