ArticleNature communications2020
A supervised learning framework for chromatin loop detection in genome-wide contact maps.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers.
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116 citing papers in PubMed.
- Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Spatial chromatin architecture and accessibility co-profiling of mammalian tissues.Nature methods · 2026Article
- Alterations in 3D-DNA architecture drive unique leukemic profiles in IDH1- and DNMT3A-mutant acute myeloid leukemia.Leukemia · 2026Article
- Article
- Dose-dependent sensitivity of human three-dimensional chromatin to a heart disease-linked transcription factor.Science (New York, N.Y.) · 2026Article
- TM-Loop: Transformer multi-omics hierarchical detection of chromatin loop.Scientific reports · 2026Article
- CLASH (Chromatin Loop Across-sample Score Harmonizer) quantifies the relative contributions of genetic variation, methylation, and CTCF occupancy on chromatin loop strength across individuals.bioRxiv : the preprint server for biology · 2026Article
- A Targetable Developmental Program Coregulates Angiogenesis and Immune Evasion in Melanoma.Cancer discovery · 2026Article
- Persistent homology-based multiscale Hi-C analysis uncovers TADs-mediated gene regulation.Scientific reports · 2026Article
- BandHiC: a memory-efficient and user-friendly Python package for organizing and analyzing Hi-C matrices down to sub-kilobase resolution.BMC genomics · 2026Article
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- A conserved hormonal signalling-H2A.Z axis rapidly reorganizes 3D chromatin interactions in adipocyte thermogenesis.Nature metabolism · 2026Article
- CellLoop: Identifying single-cell 3D genome chromatin loops.Nature communications · 2026Article
- NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- 3D genetic architecture of schizophrenia risk across three neuronal subtypes.Molecular psychiatry · 2026Article
- Chromatin architecture and physical constriction cooperate in phenotype switching and cancer cell dissemination.bioRxiv : the preprint server for biology · 2026Article
- Phase separation of β-catenin assembles active loop hubs in colorectal cancer.Genome biology · 2026Article
- scCAPReSE: detection of large-scale genomic rearrangements from single-cell Hi-C based on few-shot learning.Genomics & informatics · 2026Article
- Robin: an advanced tool for comparative loop caller result analysis leveraging large language models.NAR genomics and bioinformatics · 2026Article
- Trimodal single-cell profiling of transcriptome, epigenome and 3D genome in complex tissues with scHiCAR.Nature biotechnology · 2026Article
56 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here, we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.