Journal Article

Effective normalization for copy number variation in Hi-C data

Heard, Edith, Servant, Nicolas, Varoquaux, Nelle, Barillot, Emmanuel, Vert, Jean-Philippe

2018

Overview

Background Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other. Results In order to explore the effect of copy-number variations on Hi-C data normalization, we first propose a simulation model that predict the effects of large copy-number changes on a diploid Hi-C contact map. We then show that the standard approaches relying on equal visibility fail to correct for unwanted effects in the presence of copy-number variations. We thus propose a simple extension to matrix balancing methods that model these effects. Our approach can either retain the copy-number variation effects (LOIC) or remove them (CAIC). We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genomes. Conclusions Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Bioinformatics / Biomedical and Life Sciences / Cancer / Chromosome Aberrations / Chromosome Mapping / Chromosomes / Computational Biology - standards / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Computer simulation / Conformation / Copy number / Copy number variations / Deoxyribonucleic acid / DNA / DNA Copy Number Variations / DNA methylation / DNA sequencing / Epigenetics / Gene expression / Genetic research / Genome, Human / Genomes / Genomics / Genomics - methods / Hi-C / Humans / Life Sciences / Mammals / Methodology / Methodology Article / Microarrays / Mutation / Neoplasms - genetics / Normalization / Quantitative Methods / Sequence analysis (methods) / Three dimensional analysis / Variation