Kai Zhang

The paper evaluates computational methods for predicting cell type-specific enhancers in the mouse cortex using in vivo validation data and finds that open chromatin and sequence models improve prediction accuracy.

The paper examines how sensory input, sex, and function influence the developmental trajectories of hypothalamic cell types during early life transitions.

SnapATAC2 is a fast, scalable dimensionality reduction tool for single-cell omics data that improves accuracy and efficiency in capturing cellular heterogeneity across multiple modalities.

The paper creates a chromatin accessibility atlas across 25 adult human tissues, identifying over 750,000 cis-regulatory elements and linking them to gene regulatory programs and disease-associated variants.

The paper identifies 491,818 candidate cis regulatory elements in adult mouse brain sub-types and links them to target genes and transcriptional regulators involved in various pathways.

The paper defines cardiac cell type-specific cis-regulatory elements and links disease risk variants to these elements in human hearts.

SnapATAC is a software package for analyzing scATAC-seq data, revealing regulatory elements and transcriptional regulators in mouse brain cells.

The paper introduces a method to convert position weight matrices into compact consensus sequences with minimal information loss, showing improved performance in identifying transcription factor binding sites.

The paper uses single nucleus ATAC-seq and RNA-seq to show age-related changes in chromatin accessibility and gene expression linked to SARS-CoV-2 entry genes in human lung cells.

The paper identifies critical transcription factors and their regulatory networks during mouse embryonic development using epigenomic and transcriptomic data.

The paper expands DNA methylation data from TCGA using EAGLING, identifies triple-evidenced genes across 13 cancers, and reveals enriched pathways with potential therapeutic value.