Arwa S. Kathiria

Learning and removing Tn5/DNase-I sequence bias lets a small base-resolution CNN (ChromBPNet) recover TF motifs and footprints from accessibility data and rival Enformer at predicting chromatin QTL effects.

The paper identifies upstream regulators of immune genes in human T cells and maps their regulatory network, revealing its enrichment for immune disease variants.

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.

The paper identifies dynamic cis-regulatory elements in epidermal differentiation using multi-omic data and deep learning, revealing combinatorial motif rules linked to gene expression and polygenic skin disease.

The paper uses single-cell transcriptomics and epigenomics to analyze immune cell subsets in human tonsils, revealing that autoimmune risk variants have strong regulatory potential in germinal center cells.

The paper maps gene-regulatory dynamics in the developing human cerebral cortex using single-cell data, identifying key transcription factors and disrupted regulatory elements in ASD.

This study profiles single-nucleus transcriptomes of adult mouse spinal motor neurons to identify transcriptional subtypes and their functional roles.

The paper maps stimulation-responsive chromatin changes in immune cells and identifies variants linked to autoimmunity.

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.

Omni-ATAC improves ATAC-seq by reducing background noise and enabling analysis of frozen tissues, revealing disease-associated DNA element activities in human brain structures.