ArticleNature protocols2021
Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools.
Article in Nature protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetic variation in cis-regulatory domains suggests cell type-specific regulatory mechanisms in immunity.Communications biology · 2023Pooled it
- High-resolution promoter interaction analysis implicates genes involved in activation of type 3 innate lymphoid cells in immune disease risk.Nature genetics · 2026Article
- BCR::ABL1-Induced Enhancer Reprogramming Uncovers Hypersensitivity of Ph+B-ALL Cells to Enhancer-Targeting Drugs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- High-resolution promoter interaction analysis implicates genes involved in the activation of Type 3 Innate Lymphoid Cells in autoimmune disease risk.bioRxiv : the preprint server for biology · 2026Article
- The China Brain Multi-omics Atlas Project (CBMAP).Molecular psychiatry · 2026Review
- The gene regulatory landscape driving mouse gonadal supporting cell differentiation.Science advances · 2025Article
- Article
- Genetic coupling of enhancer activity and connectivity in gene expression control.Nature communications · 2025Article
- Integrated promoter-capture Hi-C and Hi-C analysis reveals fine-tuned regulation of the 3D chromatin architecture in colorectal cancer.Frontiers in genetics · 2025Article
- Using paired-end read orientations to assess technical biases in capture Hi-C.NAR genomics and bioinformatics · 2024Article
- Resolving the three-dimensional interactome of Human Accelerated Regions during human and chimpanzee neurodevelopment.bioRxiv : the preprint server for biology · 2024Article
- Increased enhancer-promoter interactions during developmental enhancer activation in mammals.Nature genetics · 2024Article
- Epigenomic mapping reveals distinct B cell acute lymphoblastic leukemia chromatin architectures and regulators.Cell genomics · 2023Article
- MacroH2A restricts inflammatory gene expression in melanoma cancer-associated fibroblasts by coordinating chromatin looping.Nature cell biology · 2023Article
- A multiple super-enhancer region establishes inter-TAD interactions and controls Hoxa function in cranial neural crest.Nature communications · 2023Article
- 3D genome mapping identifies subgroup-specific chromosome conformations and tumor-dependency genes in ependymoma.Nature communications · 2023Article
- Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution.Nature communications · 2023Article
- Hooked Up from a Distance: Charting Genome-Wide Long-Range Interaction Maps in Neural Cells Chromatin to Identify Novel Candidate Genes for Neurodevelopmental Disorders.International journal of molecular sciences · 2023Review
- Capture-C: a modular and flexible approach for high-resolution chromosome conformation capture.Nature protocols · 2022Review
- 3D genome organization links non-coding disease-associated variants to genes.Frontiers in cell and developmental biology · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Capture Hi-C is widely used to obtain high-resolution profiles of chromosomal interactions involving, at least on one end, regions of interest such as gene promoters. Signal detection in Capture Hi-C data is challenging and cannot be adequately accomplished with tools developed for other chromosome conformation capture methods, including standard Hi-C. Capture Hi-C Analysis of Genomic Organization (CHiCAGO) is a computational pipeline developed specifically for Capture Hi-C analysis. It implements a statistical model accounting for biological and technical background components, as well as bespoke normalization and multiple testing procedures for this data type. Here we provide a step-by-step guide to the CHiCAGO workflow that is aimed at users with basic experience of the command line and R. We also describe more advanced strategies for tuning the key parameters for custom experiments and provide guidance on data preprocessing and downstream analysis using companion tools. In a typical experiment, CHiCAGO takes ~2-3 h to run, although pre- and postprocessing steps may take much longer.
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Registered trials
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 Socratic graph.