Xingzhao Wen

An integrated view of the structure and function of the human 4D nucleome

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.

Rewired m6A methylation of promoter antisense RNAs in Alzheimer disease regulates global gene transcription in the 3D nucleome

The paper identifies AD-associated changes in m6A methylation on promoter antisense RNAs and shows their role in regulating gene expression and neuronal survival in Alzheimer's disease.

Genome-wide identification of stable RNA-chromatin interactions

The paper identifies stable RNA-chromatin interactions in human embryonic stem cells using RNase treatment and reveals their genomic localization and association with transcription factors and histone modifications.

Genome-Wide Mapping of RNA-Protein Associations via Sequencing

PRIM-seq maps RNA-protein interactions by generating chimeric DNA sequences, revealing over 350,000 RNA-protein pairs in human cells.

Genome-wide analysis of the interplay between chromatin-associated RNA and 3D genome organization in human cells

This study investigates how chromatin-associated RNA interacts with 3D genome organization in human cells, revealing a feedback loop between RNA distribution and chromatin structure.

Single-cell multiplex chromatin and RNA interactions in aging human brain

The paper introduces MUSIC, a technique to profile chromatin interactions, gene expression, and RNA-chromatin associations in single cells, revealing age-related changes and disease associations in human brain samples.

Functional Organization of the Maternal and Paternal Human 4D Nucleome

The paper investigates allele-specific relationships between gene expression and genome structure in human cells using integrated haplotype-resolved data.

scSTATseq: Diminishing Technical Dropout Enables Core Transcriptome Recovery and Comprehensive Single-cell Trajectory Mapping

scSTATseq reduces technical dropout in scRNA-seq, enabling comprehensive transcriptome recovery and trajectory mapping, as demonstrated in osteoclast differentiation analysis.

D-GPM: a deep learning method for gene promoter methylation inference

D-GPM uses deep learning to predict promoter methylation levels from landmark gene methylation data, outperforming traditional methods in accuracy and correlation.

Genome-wide co-localization of RNA-DNA interactions and fusion RNA pairs

The paper identifies a correlation between RNA-DNA interactions in normal cells and fusion RNA pairs in cancer, suggesting a mechanism for fusion transcript formation.

Efficient inference of single cell expression profiles with overlapping pooling and compressed sensing

The paper introduces a computational framework that reduces scRNA-seq library costs while maintaining sensitivity by using overlapping pooling and compressed sensing.