Hi-C

This paper introduces GraphReg, a deep learning model that uses graph attention networks to predict gene expression from epigenomic data and genomic sequences, outperforming existing methods in modeling gene regulation and identifying functional enhancers.

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

This paper reveals the 3D chromatin structure of KSHV during latency and reactivation, showing similar TAD organization and reorganization of regulatory units.

This study investigates how MYC overexpression affects chromatin interactions and c-Myc binding in cancer cells, revealing its role in stabilizing enhancer-promoter interactions and driving oncogene expression.

This study investigates how 3D chromatin architecture influences and is influenced by somatic structural variants in cancer, showing an interplay between chromatin structure and SV formation.

A gapless 3.055 billion bp human genome from HiFi and ultra-long nanopore reads resolves all centromeric arrays and acrocentric p-arms, adding 182 Mbp of new sequence.

NOMe-HiC jointly profiles genetic variants, DNA methylation, chromatin accessibility, and 3D genome structure from the same DNA molecule, showing high concordance with mono-omic assays and revealing coordinated chromatin accessibility in spatially proximal regions.

This study uses bulk and single-cell assays to analyze gene expression, histone modifications, and chromatin features in ESCs transitioning to mesoderm lineages, identifying cis-regulatory elements and transcription factors involved in lineage specification.

The paper investigates how H1 depletion affects gene expression, H1 variant distribution, and genome 3D conformation in breast cancer cells.

Stripenn detects and quantifies architectural stripes from chromatin conformation data using computer vision, outperforming existing methods in biological applications.

This paper introduces a computational method to predict 3D chromatin structures from pairwise distances and applies it to analyze chromosome organization and structural variants.

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

The paper uses machine learning and polymer physics to infer 3D chromatin architecture from Hi-C data and shows how 1D epigenetic patterns encode this structure through combinatorial binding site arrangements.

This study maps open chromatin, transcription, and 3D organization of the nucleomorph genome in Bigelowiella natans, revealing its highly accessible and transcribed state with strong nucleosome positioning and telomeric chromosome interactions.

This study shows NSD2 E1099K mutation disrupts 3D chromatin organization in B-ALL, leading to drug resistance and clonal evolution through compartmentalization changes.

This study models nuclear chromatin organization using active Zimm chains to show how euchromatin activity drives heterochromatin segregation and compaction through hydrodynamic interactions and crosslinking.

monaLisa identifies transcription factors from experimental data by integrating with Bioconductor packages for motif analysis.

This paper introduces CTCC, a method using cryomilling to map chromatin interactions, showing it can generate global contact maps similar to Hi-C but with enhanced signal for stable inter-chromosomal contacts.

This study quantifies how transposable elements contribute to 3D genome structure and gene regulation across species and cell types, showing their role in shaping chromatin organization and gene expression.

HiC-LDNet is a deep learning framework for chromatin loop detection in Hi-C data, achieving high accuracy in both bulk and sparse single-cell Hi-C maps.

The paper develops a dynamical model of Drosophila nuclei to study how LAD-NE interactions and TAD dynamics shape chromatin structure.

This study shows that multivalent binding proteins can cause chromatin to collapse and reswell in confined spaces, with reswelling dependent on protein concentration and confinement strength.

This study shows that multiple factors influence the 3D chromatin structure of single nuclei during Drosophila embryonic development.

The paper introduces LOOPbit, a method to detect chromatin loops from Hi-C data by classifying CTCF-CTCF interactions, achieving higher enhancer-promoter loop detection than existing tools.

The paper identifies progestin control regions (PgCRs) where PR and PAX2 bind to regulate gene expression in endometrial cancer cells.

This study shows that chromosomal transit through micronuclei causes epigenetic dysregulation in cancer cells, leading to long-term chromatin accessibility abnormalities.

Bookend improves transcript assembly by using 5' and 3' end information from RNA-seq data, achieving accurate transcriptome reconstruction in single cells and model organisms.

HiCzin normalizes metagenomic Hi-C data and detects spurious contacts using zero-inflated negative binomial regression, improving bias correction and contig clustering.

This paper presents a chromosome-level genome assembly for Telopea speciosissima using long-reads, linked-reads, and Hi-C data, along with new tools for genome curation and size estimation.