Richard I. Sherwood

The paper identifies two proteasomal mechanisms that target proteins with exposed cysteines, revealing their role in quality control and linking them to human diseases.

The paper uses CRISPR-based functional genomics to identify noncoding regulatory elements and variants affecting schizophrenia-associated gene expression.

This paper uses prime editing to classify LDLR variants by measuring their impact on LDL-C uptake, reclassifying many previously unclassified variants.

The paper presents a joint genomic analysis of UDN patients using whole genome sequencing and new statistical methods to prioritize disease genes, revealing new disease associations and releasing a software tool for cohort analysis.

The paper introduces ACCESS-ATAC, a method that improves TF binding site prediction resolution and reveals allele-specific co-occupancy patterns in HepG2 and K562 cells.

The paper develops FUSE, a framework that jointly estimates and imputes variant functional effects using deep mutational scanning data, improving variant impact predictions and clinical discrimination.

This paper improves variant effect quantification in base editing screens by integrating genotypic and phenotypic data using a Bayesian network called BEAN.

This study identifies 21 genes whose rare variants affect LDL-C levels by altering cholesterol uptake, using CRISPR screening and co-essentiality analysis.

This study introduces STARR-scan, a computational method that identifies transcription factor motifs in synthetic DNA constructs linked to enhancer activity, revealing new regulators of the APOBEC-3B gene.

The paper introduces MIAA, a method to measure DNA accessibility changes caused by synthetic oligonucleotide motifs, and identifies key transcription factor motifs influencing chromatin accessibility in stem cells and endoderm.

The paper develops a high-throughput assay to study Tcf7l2 binding and finds that nearby transcription factor motifs influence its binding in addition to DNA sequence and chromatin accessibility.

This study identifies genetic pathways involved in remdesivir cytotoxicity using CRISPR-Cas9 screening and transcriptomics, finding that SLC29A3 and AK2 are key factors in toxicity and efficacy.