Hi-C

This study investigates the mechanical deformation and structural properties of ligated DNA crystals using simulations and experiments, revealing complex behaviors not predictable from individual components.

GOntact uses chromatin contact data to predict CRE target genes and their Gene Ontology enrichments, showing more specific functional annotations than genomic proximity.

The paper identifies regulatory elements and chromatin structures in the RCA gene cluster, revealing long-range interactions and a TAD boundary within CR1 that influence gene expression and autoimmune disease susceptibility.

The paper uses simulations to link 3D gene structure with transcription noise in human chromosomes, showing structural heterogeneity drives gene expression variability.

ZipHiC identifies enriched interactions and experimental biases in Hi-C data using a Bayesian framework with hidden Markov random field and approximate Bayesian computation.

The paper identifies a new subset of cohesin binding sites, DICs, with distinct functions in transcriptional regulation across human cell types.

The paper introduces ECHO, a graph-based deep learning model that predicts chromatin features using 3D chromatin organization, showing improved performance over sequence-based models.

This study assembled the chromosome-level genome of Rorippa aquatica to investigate its allotetraploid origin and the molecular mechanisms of heterophylly under submergence.

scGAD provides a dimension reduction tool for scHi-C data to explore gene-level chromatin interactions and integrate with other single-cell modalities.

Hi-TrAC identifies high-resolution chromatin loops and reveals TFs' roles in organizing long- and short-range chromatin structures.

The paper investigates how nuclear position affects long-range chromatin interactions by analyzing single-cell data, finding position-dependent interactions that vary by cell type and chromatin type.

The paper investigates how Polycomb-mediated genome architecture facilitates long-range H3K27 methylation spreading in ESCs and iPSCs, revealing RNA-dependent DNA looping and developmental gene roles in chromatin organization.

This study investigates how cohesin and related factors regulate 3D genome structure and gene expression by analyzing depletion effects on chromatin, transcriptome, and epigenome data.

HiCUP-Plus improves Hi-C data processing by integrating HiSAT2 and Dragen aligners, enhancing speed and accuracy while maintaining compatibility with Bowtie2.

The paper uses Hi-C to study chromosome folding and prophage activation in gut bacteria, identifying functional prophages and their environmental dynamics.

The paper develops three predictors of multi-contact frequencies using physical models and applies them to experimental data, finding that loop-extrusion explains observed three-point contacts.

The paper investigates how drug exposure alters gene regulatory networks in the brain using multi-omics approaches, identifying enhancer repression and transcription factor involvement in addiction-related behaviors.

The paper uses Hi-C data to show AMR-carrying plasmids form more connected networks than non-AMR plasmids, enhancing AMR gene spread.

This study simulates the human genome using active forces to show their impact on heterochromatin localization and long-range spatial correlations.

The paper introduces the 4DN Data Portal, a resource for searching and visualizing curated nucleomics data, including Hi-C and other chromosome architecture datasets.

This study shows that the EGR2 promoter antisense RNA modulates chromatin accessibility and spatial genome organization by interacting with histone modification complexes.

The paper tracks chromosomal instability in iPSCs during culture, finding trisomy 12 dominance and novel structural variations acquired over time.

The paper benchmarks five Hi-C scaffolders on four reference genomes, finding HiRise and Lachesis perform best across various conditions.

The paper analyzes sub-kilobase chromatin topology to show that nano-scale regulatory interactions depend on CTCF but not cohesin, and that cohesin affects longer-range enhancer-promoter interactions.

The paper develops a deep learning model, CAESAR, to predict high-resolution 3D chromatin structures from epigenomic data and validates its accuracy across 91 human tissues and cell lines.

HiCHub identifies domains of differential chromatin interactions from 3D genome data by detecting clusters of genomic elements with concurrent interaction changes and state concordance.

3DPolyS-LE simulates chromatin loop extrusion and its impact on 3D chromosome structure, enabling comparison with Hi-C data.

The paper introduces MCDP, a tool that computes p-values for genome annotation overlaps using Markov chains, improving efficiency and accuracy over existing methods.

This study shows that individual transcription factors modulate both local chromatin mobility and long-range chromatin structure in living cells.

This study identifies clinically relevant super-enhancers in ovarian cancer using multi-omic approaches and shows their role in driving oncogenic gene expression programs.