Journal Article

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

Vladimirov, Vladimir I., Stansfield, John C., Cresswell, Kellen G., Dozmorov, Mikhail G.

2018

Overview

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 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/ .

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accounting / Algorithms / Animals / Bias / Bioinformatics / Biomedical and Life Sciences / CCCTC-Binding Factor - metabolism / Cell Differentiation / Chromatin / Chromatin - metabolism / Chromosome conformation capture / Chromosomes / Comparative analysis / Comparative genomics / Comparison / Computational biology / Computational Biology - methods / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Databases, Genetic / Datasets / Deoxyribonucleic acid / Differential analysis / Differential thermal analysis / DNA / Gene expression / Gene sequencing / Genome / Genomes / Hi-C / HiCcompare / Humans / Life Sciences / Loess / Medical informatics / Medical research / Methods / Mice / Microarrays / Molecular biology / Neurons - cytology / Normalization / Normalizing / Nucleotide sequence / Open source software / Regression analysis / Repositories / Scientific software / Software