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Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2

Nature Protocols volume 15, pages 991–1012 (2020)Cite this article

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Abstract

Fit-Hi-C is a programming application to compute statistical confidence estimates for Hi-C contact maps to identify significant chromatin contacts. By fitting a monotonically non-increasing spline, Fit-Hi-C captures the relationship between genomic distance and contact probability without any parametric assumption. The spline fit together with the correction of contact probabilities with respect to bin- or locus-specific biases accounts for previously characterized covariates impacting Hi-C contact counts. Fit-Hi-C is best applied for the study of mid-range (e.g., 20 kb–2 Mb for human genome) intra-chromosomal contacts; however, with the latest reimplementation, named FitHiC2, it is possible to perform genome-wide analysis for high-resolution Hi-C data, including all intra-chromosomal distances and inter-chromosomal contacts. FitHiC2 also offers a merging filter module, which eliminates indirect/bystander interactions, leading to significant reduction in the number of reported contacts without sacrificing recovery of key loops such as those between convergent CTCF binding sites. Here, we describe how to apply the FitHiC2 protocol to three use cases: (i) 5-kb resolution Hi-C data of chromosome 5 from GM12878 (a human lymphoblastoid cell line), (ii) 40-kb resolution whole-genome Hi-C data from IMR90 (human lung fibroblast), and (iii) budding yeast whole-genome Hi-C data at a single restriction cut site (EcoRI) resolution. The procedure takes ~12 h with preprocessing when all use cases are run sequentially (~4 h when run parallel). With the recent improvements in its implementation, FitHiC2 (8 processors and 16 GB memory) is also scalable to genome-wide analysis of the highest resolution (1 kb) Hi-C data available to date (~48 h with 32 GB peak memory). FitHiC2 is available through Bioconda, GitHub and the Python Package Index.

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Fig. 1: FitHiC2 flowchart.
Fig. 2: Reproducibility and validation of FitHiC2 calls.
Fig. 3

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Data availability

FitHiC2 calls for different Hi-C datasets as well as processed files from published data that are used as references are provided in the Zenodo repository: https://doi.org/10.5281/zenodo.338058935.

Code availability

The source code and the documentation of FitHiC2 are publicly available through GitHub: https://github.com/ay-lab/fithic. An executable version is also provided on Code Ocean at https://codeocean.com/capsule/4528858/36. The source code is distributed under the MIT license at https://opensource.org/licenses/MIT.

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Acknowledgements

We would like to thank William S. Noble and Timothy L. Bailey for their contributions to earlier versions of Fit-Hi-C. We are also thankful to Abhijit Chakraborty for his feedback on the Fit-Hi-C package. Finally, we would like to thank all users of Fit-Hi-C/FitHiC2 who have reached out to us with their questions and valuable suggestions leading to significant improvements in the implementation and documentation. This work was funded by NIH grant R35-GM128938 to F.A.

Author information

Author notes

  1. Arya Kaul

    Present address: Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

  2. These authors contributed equally: Arya Kaul, Sourya Bhattacharyya.

Authors and Affiliations

  1. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Arya Kaul

  2. Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, USA

    Sourya Bhattacharyya & Ferhat Ay

  3. School of Medicine, University of California San Diego, La Jolla, CA, USA

    Ferhat Ay

Authors
  1. Arya Kaul
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  2. Sourya Bhattacharyya
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  3. Ferhat Ay
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Contributions

A.K. implemented the current version of FitHiC2. S.B. developed the merging filter module. A.K. and S.B. performed data analysis and wrote the manuscript under the supervision of F.A., who developed the original Fit-Hi-C code. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ferhat Ay.

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The authors declare no competing interests.

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Related links

Key references using this protocol

Ay, F. et al. Genome Res. 24, 999–1011 (2014): https://doi.org/10.1101/gr.160374.113

Sima, J. et al. Cell. 176, 816–830.e18 (2019): https://doi.org/10.1016/j.cell.2018.11.036

Bunnik, E. et al. Proc. Natl Acad. Sci. USA. 116, 3183–3192 (2019): https://doi.org/10.1073/pnas.1810815116

Zheng, Y., et al. Elife. 8, e38070 (2019): https://doi.org/10.7554/eLife.38070.001

Key data used in this protocol

Rao, S. et al. Cell. 159, 1665–1680 (2014): https://doi.org/10.1016/j.cell.2014.11.021

Quinodoz, S. et al. Cell. 174, 744–757.e24 (2018): https://doi.org/10.1016/j.cell.2018.05.024

Dixon, J. et al. Nat. Genet. 50, 1388–1398 (2018): https://doi.org/10.1038/s41588-018-0195-8

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Kaul, A., Bhattacharyya, S. & Ay, F. Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2. Nat Protoc 15, 991–1012 (2020). https://doi.org/10.1038/s41596-019-0273-0

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