Identifying high-confidence capture Hi-C interactions using CHiCANE
Open accessAbstract
The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .
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Many computational algorithms for Hi-C analysis explicitly correct for distance decay, identifying significant interactions based on how unusual their contact frequency is at a given distance.
“many computational algorithms for the analysis of data from Hi-C and related techniques explicitly correct for distance decay, identifying “significant interactions” based on how unusual their contact frequency is at a given distance [277, 468–471].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [470]
Cites
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“We have now extended CHiCANE to support a range of statistical distributions and genomic features to fi t a wide range of CHi-C protocols 8 – 10 , This protocol is designed to handle the most frequent tasks involved in CHi-C analysis from pre-processing, through intermediate data manipulation, on to…”
MethodHow far from the SNP may the causative genes be?
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Transcriptional enhancers and their communication with gene promoters
Ray-Jones, H., & Spivakov, M. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03903-w · Cited by 81 · see citing works
- Authors
- Holgersen, E. M., Gillespie, A., Leavy, O. C., Baxter, J. S., Zvereva, A., Muirhead, G., Johnson, N., Sipos, O., Dryden, N. H., Broome, L. R., Chen, Y., Kozin, I., Dudbridge, F., Fletcher, O., & Haider, S.
- Affiliations
- Institute of Cancer Research (GB)
- University of Leicester (GB)
- Corresponding author
- Fletcher, Olivia
- Haider, Syed
- ORCID
- Venue
- Nature Protocols
- Published
- 2021-04
- Volume
- 16
- Issue
- 4
- Pages
- 2257-2285
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W3140639381
- Cited by
- 22
- References
- 8
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 5 · 2021: 2
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Cancer Research UK (S_3754)
- National Institute for Health and Care Research
- Medical Research Council (MR/L007150/1)
- Biotechnology and Biological Sciences Research Council (BB/J004480/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Capture Hi-C, chicane, promoter capture Hi-C, 3D genome organization, interaction confidence, genetic susceptibility loci, gene expression regulation, R package
- MeSH terms
- Epigenome, Enhancer Elements, Genetic, Models, Genetic, Mutation, Promoter Regions, Genetic, Regulatory Sequences, Nucleic Acid, RNA, Messenger, Statistics as Topic, Genome, Polymorphism, Single Nucleotide, Genomics, Quantitative Trait Loci, Histone Code, Molecular Sequence Annotation
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