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Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools

Nature Protocols volume 16, pages 4144–4176 (2021)Cite this article

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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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Fig. 1: An overview of CHi-C and Capture-C.
Fig. 2: Standard CHi-C data analysis steps.
Fig. 3: Visualizing the suitability of the background model estimation.
Fig. 4: Tuning the CHiCAGO score cutoff by balancing recall and enrichment of regulatory chromatin features at interacting fragments.
Fig. 5: QC plots generated by CHiCAGO.

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Data availability

All of the figures for this paper were produced using publicly available data from Ray-Jones et al.36, Montefiori et al.33, and Choy et al.34. We provide downsampled FASTQ files and all intermediate file types (.bam, .chinput, .Rds) from Ray-Jones et al. on the OSF repository (https://osf.io/kt67f) to allow readers to test either the full pipeline or specific analysis steps.

Code availability

The Chicago and PCHiCdata R packages are available from Bioconductor and from the Bitbucket repository: https://bitbucket.org/chicagoTeam/chicago. The chicagoTools suite of auxiliary scripts is available from the same Bitbucket repository. Full documentation and installation instructions for HiCUP are available from https://www.bioinformatics.babraham.ac.uk/projects/hicup/. The Peaky R package is available from the GitHub repository: http://github.com/cqgd/pky. The Chicdiff R package is available from the GitHub repository: https://github.com/RegulatoryGenomicsGroup/chicdiff. The code presented in the Procedure and the versions of the software used in this protocol are deposited on OSF: https://osf.io/kt67f/ (DOI 10.17605/OSF.IO/KT67F).

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Acknowledgements

We thank all users of CHiCAGO and associated packages for providing test data and reporting issues. Research in M.S. lab is supported by core funding from the UK’s Medical Research Council (MRC) (MC-A652-5QA20). S.W.W. acknowledges core support from the UK’s Biotechnology and Biological Sciences Research Council (BBSRC). C.W. is supported by the MRC (MC_UU_00002/4) and the Wellcome Trust (WT107881, 215097/Z/18/Z).

Author information

Author notes

  1. Steven W. Wingett

    Present address: Cell Biology Division, MRC Laboratory of Molecular Biology, Cambridge, UK

  2. These authors contributed equally: Paula Freire-Pritchett, Helen Ray-Jones.

Authors and Affiliations

  1. Cell Biology Division, MRC Laboratory of Molecular Biology, Cambridge, UK

    Paula Freire-Pritchett

  2. Functional Gene Control Group, Epigenetics Section, MRC London Institute of Medical Sciences, London, UK

    Helen Ray-Jones, Monica Della Rosa, Mikhail Spivakov & Valeriya Malysheva

  3. Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK

    Helen Ray-Jones, Monica Della Rosa, Mikhail Spivakov & Valeriya Malysheva

  4. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK

    Chris Q. Eijsbouts

  5. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Chris Q. Eijsbouts

  6. Cancer Research UK Cambridge Research Institute, Cambridge, UK

    William R. Orchard

  7. Bioinformatics, The Babraham Institute, Cambridge, UK

    Steven W. Wingett

  8. Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK

    Chris Wallace

  9. MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge, UK

    Chris Wallace

  10. The Babraham Institute, Cambridge, UK

    Jonathan Cairns

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Contributions

J.C., P.F.P. and M.S developed the CHiCAGO pipeline. J.C., W.R.O., V.M. and M.S. developed Chicdiff. C.E. and C.W. developed Peaky and its integration with CHiCAGO. S.W.W. developed the HiCUP pipeline. V.M., H.R.J. and M.D.R. optimized CHiCAGO parameters and contributed auxiliary scripts, with input from J.C., W.R.O. and M.S. P.F.P. and H.R.J. wrote and tested the code in the Procedure. P.F.P., H.R.J., M.D.R, C.E., C.W., M.S. and V.M. wrote the manuscript. All authors read and approved the final manuscript. M.S. and V.M. supervised the work.

Corresponding authors

Correspondence to Mikhail Spivakov or Valeriya Malysheva.

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Competing interests

P.F.P. is currently an employee of Inivata Limited. J.C. is currently an employee of AstraZeneca and may or may not own stock options. M.S. is a cofounder of Enhanc3D Genomics Ltd. The rest of the authors declare no competing interests.

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Peer review information Nature Protocols thanks Andrea M. Chiariello, Fulai Jin and Yun Li for their contribution to the peer review of this work.

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Related links

Original methodological articles

Cairns, J. et al. Genome Biol. 17, 127 (2016): https://doi.org/10.1186/s13059-016-0992-2

Cairns, J. et al. Bioinformatics 35, 4764–4766 (2019): https://doi.org/10.1093/bioinformatics/btz450

Eijsbouts, C. et al. BMC Genomics 20, 77 (2019): https://doi.org/10.1186/s12864-018-5314-5

Wingett, S. et al. F1000Res. 4, 1310 (2015): https://doi.org/10.12688/f1000research.7334.1

Key data used in this protocol

Ray-Jones, H. et al. BMC Biol. 18, 47 (2020): https://doi.org/10.1186/s12915-020-00779-3

Choy, M. et al. Nat. Commun. 9, 2526 (2018): https://doi.org/10.1038/s41467-018-07399-0

Montefiori, L. et al. eLife 7, (2018): https://doi.org/10.7554/eLife.35788.001

Key articles using this protocol

Javierre, B. M. et al. Cell 167, 1369–1384.e19 (2016): https://doi.org/10.1016/j.cell.2016.09.037

Siersbæk, R. et al. Mol. Cell 66, 420–435.e5 (2017): https://doi.org/10.1016/j.molcel.2017.04.010

Rubin, A. et al. Nat. Genet. 49, 1522–1528 (2017): https://doi.org/10.1038/ng.3935

Orlando, G. et al. Nat. Genet. 50, 1375–1380 (2018): https://doi.org/10.1038/s41588-018-0211-z

Extended data

Extended Data Fig. 1 Comparative analysis of PCHi-C data generated with a four- and a six-cutter restriction enzyme.

Three MboI PCHi-C replicates obtained from iPSC-derived cardiomyocytes (iPSC CMs33) were processed by CHiCAGO either at the restriction fragment level, using standard 4 bp cutter settings or in 5 kb bins, as described in the Procedure. Three HindIII PCHi-C replicates obtained from hESC-derived cardiomyocytes (hESC CMs34) were processed using standard 6 bp cutter settings. Only genes baited in both iPSC CMs and hESC CMs were included in the comparative analysis. An interaction was considered shared when the middle of the significantly interacting fragments in the MboI data fell within the respective interacting fragments in the HindIII dataset (CHiCAGO score >5). When several interactions in MboI data overlapped with the same HindIII interaction, it was counted as a single shared interaction to avoid double-counting. a,b, Comparison between MboI and HindIII PCHi-C datasets in nonbinned mode (a) and binned mode (b). The violin plots show the distance distribution of significant interactions belonging to shared, MboI- and HindIII-specific groups. The number of significant interactions in each group is indicated in gray. The barplots show enrichment for regulatory histone marks (as a ratio between observed and expected) in each group of interactions.

Extended Data Fig. 2 QC plots generated by HiCUP for downsampled CHi-C data.

MyLa CHi-C36 replicate 1 was downsampled to 20 million raw read pairs and processed using HiCUP19, as described in the Procedure. a, Truncation, alignment to GRCh37 and pairing results for read 1 (dark blue) and read 2 (light blue). The ~15 million paired reads are taken forwards for filtering. b, Detection of valid Hi-C di-tags (dark blue) and removal of Hi-C artifacts such as religation products (turquoise) and di-tags falling outside the specified size range (orange). c, Size distribution of di-tags with limits shown as red lines. d, Interacting fragments are grouped into cis < 10 kb (dark blue), cis > 10 kb (light blue) and trans (green) for di-tags before removal of PCR duplicates (left) and after (right).

Extended Data Fig. 3 QC plots generated by CHiCAGO for downsampled CHi-C data.

Downsampled CHi-C datasets36 were processed by CHiCAGO using both replicates per cell line as described in the Procedure. a, Barplot showing the scaling factors (si’s) computed for each pool of other ends for MyLa. b, Boxplots showing distribution of technical noise estimates for each pool of baits/viewpoints (top) and for each pool of other ends (bottom) for MyLa. c, Distance dependency of background counts and computed fit (red curve), plotted on a log–log scale for MyLa. d, Interaction profiles for the bait 670997, assigned to rs4141001, in MyLa (top) and HaCaT (bottom). High-scoring interactions detected by CHiCAGO (score ≥5) are shown in red, and subthreshold interactions (3 ≤ score < 5) are shown in blue. e, Number of overlaps between chromatin features of interacting fragments detected using CHiCAGO (yellow bars) versus number of overlaps from 100 random distance-matched subsets of HindIII fragments (blue bars) in MyLa (top) and HaCaT (bottom). Error bars represent 95% confidence intervals.

Extended Data Fig. 4 Identifying differential interactions between conditions using Chicdiff.

a, Dendrogram for downsampled HaCaT and MyLa samples36 obtained from running getPeakMatrix as outlined in the Procedure. b, Chicdiff45 bait profiles were generated for four loci as described in the Procedure. The plots show the raw read counts versus linear distance from the bait fragment as mirror images for HaCaT and MyLa. Other-end interacting fragments are pooled and color-coded by their adjusted weighted P-value.

Extended Data Fig. 5 Example of fine-mapping chromatin contacts with Peaky.

The full MyLa CHi-C36 data were processed by CHiCAGO using both replicates and then analyzed using Peaky44. The top panel shows the distribution of raw read counts for other end fragments for the bait 642001, with high-scoring interactions (CHiCAGO score ≥ 5) highlighted in blue. The second panel shows the CHiCAGO adjusted read counts with high-scoring interactions (CHiCAGO score ≥ 5) highlighted in blue and with the Peaky model fitted as a green line. The third panel shows CHiCAGO scores for those interactions with the blue dashed line showing the score cutoff of 5. In the bottom panel, the probability of each other-end fragment being a causal contact is quantified as the marginal posterior probability of contact (MPPC). Based on this metric, a number of fragments with CHiCAGO score ≥ 5 (points highlighted in blue) have MPPC very close to zero. After discounting these, a smaller subset of fine-mapped interactions may be identified.

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Freire-Pritchett, P., Ray-Jones, H., Della Rosa, M. et al. Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools. Nat Protoc 16, 4144–4176 (2021). https://doi.org/10.1038/s41596-021-00567-5

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