Hi-C

The paper develops a physics-based 4D mesoscale liquid model of the nucleus to resolve chromatin's radial organization and its dynamics.

The paper uses CRISPR and single-cell transcriptomics to screen ASD risk genes in brain organoids, identifying developmental defects and regulatory modules linked to autism.

This study shows that stochastic folding of fluidlike chromatin with DNA-packing density heterogeneity can form TADs without cohesin-mediated extrusion, and predicts TAD dynamics during T cell differentiation.

This study shows PfMORC regulates chromatin structure and gene repression in Plasmodium falciparum, essential for parasite survival and a potential target for antimalarial drugs.

SnapATAC2 is a fast, scalable dimensionality reduction tool for single-cell omics data that improves accuracy and efficiency in capturing cellular heterogeneity across multiple modalities.

The paper integrates genetic fine-mapping with multi-omics data to identify candidate effector genes and new drug targets for hypertension.

Sashimi.py provides a flexible and scalable tool for visualizing genomic data across multiple assays and formats, enabling combinatorial analysis of regulatory mechanisms.

The paper uses multi-omics analysis to compare malignant and normal uveal melanocytes, identifying genomic instability and a BAP1 promoter deletion as key features of uveal melanoma.

This paper introduces Dip3D, a method to reconstruct high-resolution diploid 3D human genomes from Pore-C data, achieving better performance than existing methods.

AutoHiC improves chromosome-level genome assembly accuracy using deep learning to detect and correct errors in Hi-C data.

This paper introduces TGIF, a multi-task NMF method for analyzing 3D genome dynamics across Hi-C datasets, identifying structural changes linked to regulatory signals and gene expression.

This study develops a chromosome dynamic structural model to analyze the structural ensemble characteristics of normal and lung cancer cells, revealing links between chromosome structure, epigenetics, and gene expression.

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.

Chrombus-XMBD predicts 3D-genome structures from chromatin features using a graph generative model, outperforming existing methods and showing cross-species generalizability.

The paper identifies differential chromatin loops during megakaryocyte differentiation and shows their association with gene expression changes and histone modifications.

The paper identifies ultra-long-range chromatin interactions between active regulatory elements in vertebrate and invertebrate genomes, showing they are driven by three distinct mechanisms independent of RNA polymerase II and some cofactors.

This study quantifies chromosome structural dynamics during the mitosis-to-G1 phase transition using a data-driven model and nonequilibrium landscape-switching model, revealing asynchronous structural changes and TAD dynamics.

The paper shows that RNA polymerase II pausing maintains chromatin organization in chicken erythrocytes by repositioning euchromatin and preserving paused promoters.

This study characterizes the organization of Borrelia burgdorferi's segmented genome and interactions between replicons using Hi-C, revealing chromosome structure and plasmid interactions.

The paper identifies enhancer plasticity and 3D-genome alterations in endometrial cancer that boost ESR1 expression through non-coding mutations and risk variants.

The paper maps 3D genome structures in cancer tissues to identify structural changes and potential biomarkers across cancer progression stages.

The paper develops a computational method to predict cell-type-specific nucleosome-binding abilities of transcription factors using ChIP-seq, MNase-seq, and DNase-seq data, identifying new potential pioneer transcription factors.

This paper introduces GAGE-seq, a single-cell method that simultaneously profiles 3D genome structure and gene expression, revealing their relationship in defining cell identity and lineage commitment.

The paper improves gene expression prediction models by incorporating protein-mediated spatial chromatin interactions, resulting in significant performance gains across multiple cell lines.

BL-Hi-C improves Hi-C for plant 3D genomics by simplifying protocols, reducing noise, and increasing sensitivity for detecting gene loops in Brassica species.

The paper uses Micro-C data from mouse embryonic stem cells to show that transcriptional activity influences 3D gene folding through Pol II-mediated micro-compartmentalization.

The paper constructs a super-pangenome of 19 Populus species to explore genomic factors driving adaptation and diversification in forest trees.

The paper investigates how ecDNA clusters form through phase separation, creating regulatory contacts that boost MYC expression, and shows how JQ1 disrupts this process.

The paper maps 3D chromatin structures in neuronal subtypes and shows how schizophrenia risk loci reorganize during differentiation, linking regulatory loops to gene function.

EpiGePT is a transformer-based model that predicts epigenomic signals and chromatin contacts by incorporating transcription factor activities and 3D genome interactions, outperforming existing methods in cross-cell-type prediction.