Iterative Correction of Hi-C Data Reveals Hallmarks of Chromosome Organization

Abstract

Extracting biologically meaningful information from chromosomal interactions obtained with genome-wide chromosome conformation capture (3C) analyses requires elimination of systematic biases. We present a pipeline that integrates a strategy for mapping of sequencing reads and a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. We validate ICE (Iterative Correction and Eigenvector decomposition) on published Hi-C data, and demonstrate that eigenvector decomposition of the obtained maps provides insights into local chromatin states, global patterns of chromosomal interactions, and the conserved organization of human and mouse chromosomes.