Ming Hu

The paper uses fully phased telomere-to-telomere assemblies and long-read epigenomes to study centromere genetic and epigenetic dynamics across inheritance and cell-fate transitions, finding positional stability of CDRs and dynamic epigenetic changes.

The paper integrates 4D Nucleome data to analyze 3D genome structure and function in human cells, revealing chromatin domains, loops, and interactions, and showing how genetic variants affect genome folding.

The paper validates the existence of transitional cerebellar progenitors using multimodal approaches across multiple datasets and stages.

The paper maps chromatin interactions in mouse fetal tissues and links them to gene expression and disease risk variants in humans.

The paper identifies combinatorial TF-cRE interactions in mouse basal ganglia development, revealing modules that drive GABAergic neurogenesis and suppress other fates.

SnapHiC-G identifies long-range enhancer-promoter interactions from scHi-C data using a global background model, showing high sensitivity and cell-type-specific enrichment of neuropsychiatric disease SNPs.

SnapFISH-IMPUTE imputes missing values in multiplexed DNA FISH data, improving downstream analysis by preserving pairwise distances and enhancing clustering and enhancer-promoter loop detection.

The paper identifies functional CREs in iPSCs and neurons and links them to neuropsychiatric disease risk variants.

SnapFISH identifies chromatin loops from multiplexed DNA FISH data with high sensitivity and accuracy in mouse embryonic stem cells.

SnapHiC-D identifies differential chromatin contacts between two scHi-C datasets with high sensitivity and accuracy, showing correlation with cell-type-specific gene expression and epigenomic features.

THUNDER is a deconvolution method that infers cell type proportions from bulk Hi-C data, outperforming existing methods in accuracy.

TWO-SIGMA-G tests gene sets in scRNA-seq data by accounting for gene and cell correlations, showing improved power and error control in simulations and real datasets.

The paper identifies a transcription factor network and enhancers involved in cortical regional patterning using mouse embryonic data.

This paper introduces a network-based deep learning framework to translate GWAS findings into biological insights and drug targets for Alzheimer's disease, identifying 156 putative risk genes.

HPRep quantifies reproducibility in HiChIP and PLAC-seq data by using a stratified and weighted correlation metric, outperforming existing Hi-C-based measures.

CRISPRpath enables parallel characterization of cis-regulatory elements for multiple genes in a pathway, identifying functional enhancers and distinguishing their impact on gene expression.

MUNIn identifies long-range chromatin interactions across multiple samples by accounting for sequencing depth and heterogeneity, achieving lower false positives and higher power for shared peaks.

HiC-ACT improves chromatin interaction detection from Hi-C data by post-processing independent methods, enhancing sensitivity while controlling type-I error.

This study shows that chromatin organization remains largely intact in mouse embryonic stem cells after acute CTCF depletion, despite reduced cell growth and changes in chromatin loop interactions and cohesin occupancy.

FIREcaller detects frequently interacting regions from Hi-C data and shows their association with gene regulation and GWAS variants in human tissues.

SnapHiC identifies high-resolution chromatin loops from scHi-C data and demonstrates cell-type-specific chromatin architecture in mouse and human cells.

This study characterizes 3D epigenomic landscapes during human corticogenesis, revealing chromatin looping and super interactive promoters involved in lineage-specific gene regulation.

TWO-SIGMA is a single-cell RNA-seq differential expression method that handles drop-out, overdispersion, and within-sample correlation, outperforming existing methods in simulations and real data.

This study investigates how CTCF binding sites mediate transcriptional insulation in mouse embryonic stem cells, showing that insulation depends on CTCF site number, sequence context, and TAD boundary location.

This study shows that CTCF depletion in mESCs weakens TAD boundaries and INs but only affects a small subset of enhancer-promoter contacts, highlighting CTCF's role in promoting long-range gene regulation.

This study identifies DNA sequence variants that influence 3D chromatin conformation and links them to gene expression and disease risk.

The paper develops an improved Hi-C protocol and bias-correction pipeline to map chromatin loops in human neurogenesis and brain tissues, showing their role in neural differentiation and GWAS gene identification.

The paper integrates Hi-C and eQTL data from multiple human tissues and cell lines to show eQTLs and their target genes are often in the same topologically associating domains and have higher chromatin interaction frequency.

MAPS processes PLAC-seq and HiChIP data to identify long-range chromatin interactions by removing biases and analyzing contact frequencies.

BAMM-SC clusters droplet-based scRNA-seq data from multiple individuals by accounting for batch effects and heterogeneity, improving accuracy over existing methods.