William H. Majoros

PRIME is a Bayesian model that improves estimation of sub-genic intolerance by sharing information across regions, identifying disease-critical protein regions and enhancing variant interpretation.

This paper introduces GRN-BMuSeR, a Bayesian multi-species method for gene regulatory network inference, showing improved performance in bacteria and novel predictions in archaea.

The paper introduces a structured pooling method for population-scale STARR-seq experiments, improving detection of rare regulatory variants by increasing signal-to-noise ratio and providing Bayesian effect size estimates.

The paper develops a Bayesian method to infer inheritance patterns of allelic imbalance across trios, improving ASE estimation and linking it to chromatin state and causal variants.

The paper introduces BlueSTARR, a predictive modeling framework trained on whole-genome STARR-seq data to assess variant effects on regulatory functions, revealing purifying selection against loss-of-function and gain-of-function variants.

The paper functionally measures allele-specific regulatory element activity across five human genomes to fine map causal variants in eQTLs and chromatin QTLs.

The paper identifies a non-coding variant causing GSD IX γ2, validates its splicing effects, and demonstrates reversal using antisense oligonucleotides.

BEASTIE improves allele-specific expression estimation by addressing phasing uncertainty and genotyping errors, enhancing accuracy in detecting rare variants.

The paper develops CLEANSER, a model to filter ambient gRNAs in single-cell CRISPR screens, improving gRNA-cell assignment accuracy and differential gene expression analysis.

The paper identifies technical biases in STARR-seq data, develops a statistical model to correct them, and improves detection of regulatory elements with better precision and recall.