Chicdiff: a computational pipeline for detecting differential chromosomal interactions in Capture Hi-C data

Author:

Cairns Jonathan12,Orchard William R1345,Malysheva Valeriya134,Spivakov Mikhail134ORCID

Affiliation:

1. Regulatory Genomics Group, Nuclear Dynamics Programme, Babraham Institute, Cambridge CB22 3AT, UK

2. Data Sciences and Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB4 0WG, UK

3. Functional Gene Control Group, Epigenetics Section, MRC London Institute of Medical Sciences, London W12 0NN, UK

4. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London W12 0NN, UK

5. Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK

Abstract

Abstract Summary Capture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. Chicdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalization and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression. Availability and implementation Chicdiff is implemented as an R package that is publicly available at https://github.com/RegulatoryGenomicsGroup/chicdiff. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

UK Research and Innovation

Babraham Institute Translational Advisory Group Award

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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