Samuel H. Kim

This study designs cell type-specific AAV promoters using deep learning, outperforming rational approaches in mouse retinal cells and showing promise for human applications.

The paper uses a single-cell multi-omic atlas and deep learning to identify cis-regulatory elements and genetic drivers of heart development and disease.

The paper identifies disease-linked regulatory DNA variants and their impact on epidermal homeostasis by analyzing SNVs, TF binding, and gene dysregulation in polygenic skin diseases.

The paper identifies HBO1 and MLL1 complexes as key tumor suppressors in lung cancer using in vivo functional screens of epigenetic regulators.

The paper presents a single cell multiomic atlas of human lung development, identifying regulatory elements and gene programs, and linking genetic variants to pulmonary function.

The paper develops RoboATAC, an automated ATAC-seq method, to study how TF dosage affects chromatin accessibility and gene expression, finding that DNA sequence predicts dosage sensitivity and nucleosome positioning patterns vary by TF.

The paper presents a single-cell multi-omics atlas of human development and uses deep learning to analyze regulatory DNA motifs and their impact on chromatin accessibility and gene expression.

The paper introduces KAS-ATAC, a method combining KAS-seq and ATAC-seq to simultaneously measure chromatin accessibility and single-stranded DNA, revealing insights into transcription and regulatory elements in the human genome.

The paper introduces a method to profile off-target specificity of CRISPR enzymes using ssDNA mapping, applicable to both catalytically dead and active Cas9 variants.