HiCcompare: an R-package for joint normalization and comparison of HI-C datasets

• Published in: BMC bioinformatics 2018-07, Vol.19 (1), p.279-279, Article 279
• Main Authors: Stansfield, John C, Cresswell, Kellen G, Vladimirov, Vladimir I, Dozmorov, Mikhail G
• Format: Article
• Language: English
• Subjects: Accounting; Animals; Bias; Bioinformatics; CCCTC-Binding Factor - metabolism; Cell Differentiation; Chromatin; Chromatin - metabolism; Chromosome conformation capture; Chromosomes; Comparative analysis; Comparison; Computational Biology - methods; Databases, Genetic; Datasets; Deoxyribonucleic acid; Differential analysis; Differential thermal analysis; DNA; Gene expression; Gene sequencing; Genome; Genomes; Hi-C; HiCcompare; Humans; Loess; Methods; Mice; Neurons - cytology; Normalization; Normalizing; Nucleotide sequence; Open source software; Regression analysis; Repositories; Software; Technology
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2288-x

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. 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. HiCcompare is able to remove between-dataset bias present in Hi-C matrices. It also provides a user-friendly tool to allow the scientific community to perform direct comparisons between the growing number of pre-processed Hi-C datasets available at online repositories. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/ .