Journal
BMC BIOINFORMATICS
Volume 19, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12859-018-2288-x
Keywords
Hi-C; Chromosome conformation capture; Normalization; Comparison; Differential analysis; HiCcompare
Categories
Funding
- American Cancer Society [IRG-14-192-40]
- National Institute of Environmental Health Sciences of the National Institutes of Health [T32ES007334]
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Background: Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating the regulation of gene expression. Hi-C sequencing technology allows insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence-and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, e.g., disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. Results: We developed a simple and effective method, HiCcompare, for the joint normalization and differential analysis of multiple Hi-C datasets. The method introduces a distance-centric analysis and visualization of the differences between two Hi-C datasets on a single plot that allows for a data-driven normalization of biases using locally weighted linear regression (loess). HiCcompare outperforms methods for normalizing individual Hi-C datasets and methods for differential analysis (diffHiC, FIND) in detecting a priori known chromatin interaction differences while preserving the detection of genomic structures, such as A/B compartments. Conclusions: HiCcompare is able to remove between-dataset bias present in Hi-C matrices. It also provides a userfriendly tool to allow the scientific community to perform direct comparisons between the growing number of preprocessed Hi-C datasets available at online repositories. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/.
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