Benjamin E. Parks

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.

BPCells enables scalable single-cell RNA-seq and ATAC-seq analysis on modest hardware by using disk-backed streaming algorithms and compressed formats.

This study links transcription factor binding to gene expression by analyzing chromatin configurations using single-molecule footprinting in human cells.

NEAT-seq profiles nuclear proteins, chromatin accessibility, and transcriptomes in single cells, revealing TF regulatory mechanisms and linking GWAS SNPs to chromatin accessibility and target genes.

This paper develops a single-cell multi-omic framework to identify malignant regulatory signatures in mixed phenotype acute leukemia by analyzing transcriptomic and epigenetic data.