Dataset Information

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

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.

SUBMITTER: Cairns J

PROVIDER: S-EPMC6853696 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Cairns Jonathan J Orchard William R WR Malysheva Valeriya V Spivakov Mikhail M

Bioinformatics (Oxford, England) 20191101 22

<h4>Summary</h4>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 Chi ...[more]

PMID: 31197313

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Project description:BackgroundChromosomal compartmentalization plays a critical role in maintaining proper transcriptional programs in cell differentiation and oncogenesis. However, currently the prevalent method for comparative analysis of compartmentalization landscapes between different cell types is limited to the qualitative switched compartments.ResultsTo identify genomic regions with quantitatively differential compartmentalization changes from genome-wide chromatin conformation data like Hi-C, we developed a computational framework named DARIC. DARIC includes three modules: compartmentalization quantification, normalization, and differential analysis. Comparing DARIC with the conventional compartment switching analysis reveals substantial regions characterized by quantitatively significant compartmentalization changes without switching. These changes are accompanied by changes in gene expression, chromatin accessibility, H3K27ac intensity, as well as the interactions with nuclear lamina proteins and nuclear positioning, highlighting the functional importance of such quantitative changes in gene regulation. We applied DARIC to dissect the quantitative compartmentalization changes during human cardiomyocyte differentiation and identified two distinct mechanisms for gene activation based on the association with compartmentalization changes. Using the quantitative compartmentalization measurement module from DARIC, we further dissected the compartment variability landscape in the human genome by analyzing a compendium of 32 Hi-C datasets from 4DN. We discovered an interesting correlation between compartmentalization variability and sub-compartments.ConclusionsDARIC is a useful tool for analyzing quantitative compartmentalization changes and mining novel biological insights from increasing Hi-C data. Our results demonstrate the functional significance of quantitative compartmentalization changes in gene regulation, and provide new insights into the relationship between compartmentalization variability and sub-compartments in the human genome.

Dataset Information

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

Summary

Availability and implementation

Supplementary information

Publications

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

Similar Datasets

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