Feng Yue

This study identifies POU2AF2 as a lineage-specific dependency in DLBCL, showing it coactivates POU2F2 to sustain malignant transcriptional networks and innate-like B-cell identity.

This study investigates the molecular mechanisms of AAV receptor AAVR and AAVR2, identifies functional motifs, and demonstrates enhanced AAV entry through engineered receptors.

This study investigates cohesin's role in transcriptional loops and DNA replication using human cells, finding that cohesin loss affects long-range chromatin interactions and up-regulates early initiation zone genes.

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.

This study identifies Ikaros and HDAC1's roles in regulating heterochromatin and tumor suppression in T-ALL by modulating EZH2 activity and chromatin structure.

The paper identifies PRC1 and CTCF's role in forming chromatin loops that maintain bivalent genes in a poised state for activation during differentiation.

This study identifies conserved and species-specific cis-regulatory elements in human and mouse blood cells using integrative epigenomic analysis and clustering.

The paper introduces COMSV, a method for identifying structural variations in cancer samples, showing high sensitivity and specificity and detecting hundreds of novel SVs per sample.

EagleC Explorer is a desktop tool for visualizing and detecting structural variations and enhancer hijacking in Hi-C data, enabling interactive analysis in cancer studies.

HiCLift converts chromatin interaction data between genome assemblies efficiently, outperforming re-mapping approaches in speed while maintaining accuracy.

This study shows that cohesin-mediated loops in G1 phase determine the genomic location of replication origins in S phase by anchoring replication initiation zones.

Chromap provides a fast and accurate method for aligning and preprocessing chromatin profiles, outperforming existing tools in speed while maintaining alignment accuracy.

This study identifies subtype-specific epigenomic and 3D genome structures in bladder cancer, linking NPAS2 to luminal subtype regulation and clinical outcomes.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.

This paper introduces Peakachu, a Random Forest framework for chromatin loop detection in Hi-C data, showing improved identification of short-range interactions across multiple platforms and species.

The paper integrates epigenomic and transcriptomic data from mouse hematopoiesis to identify regulatory elements and their dynamics across cell types.

The paper integrates GATA transcription factors and epigenomes in mouse hematopoietic cells using IDEAS to map regulatory landscapes and identify candidate cis-regulatory elements.

The paper evaluates reproducibility and quality measures for Hi-C data using real and simulated datasets, showing that specialized methods outperform simple correlation analysis.

The paper identifies numerous previously undetected structural variants in leukemia samples using integrated genome analysis and computational methods, revealing their impact on gene expression and patient survival.

The paper introduces a web-based browser for visualizing 3D genome organization and chromatin interactions, enabling integration with other omics data to study gene regulation.

This paper presents an integrative framework to detect structural variants in cancer genomes using multiple methods and finds that non-coding structural variation significantly impacts genome function in cancer cells.

HiCPlus enhances the resolution of Hi-C interaction heatmaps using deep learning, reconstructing high-resolution data from low-resolution inputs.

HiCRep assesses Hi-C data reproducibility using a stratum-adjusted correlation coefficient, improving accuracy over existing methods.

The paper develops a Bayesian framework to jointly annotate epigenomes and detect differential regulation across cell types, identifying epigenetic variations linked to gene expression and complex phenotypes.