TY - JOUR T1 - HiCdiff: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection JF - bioRxiv DO - 10.1101/147850 SP - 147850 AU - John C. Stansfield AU - Mikhail G. Dozmorov Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/06/08/147850.abstract N2 - Changes in the 3D structure of the human genome are now emerging as a unifying mechanism orchestrating gene expression regulation. Evolution of chromatin conformation capture methods into Hi-C sequencing technology now allows an insight into the 3D structures of the human genome. However, Hi-C data used to obtain 3D structures contains many known and unknown biases. These biases prevent effective comparison of the 3D structures to identify differential chromatin interactions. Several methods have been developed for normalization of individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering their comparative analysis. We developed a simple and effective method HiCdiff for the joint normalization and differential analysis of multiple Hi-C datasets. The method avoids constraining Hi-C data within a rigid statistical model, allowing a data-driven normalization of biases using locally weighted linear regression (loess). HiCdiff outperforms methods for normalizing individual Hi-C datasets in detecting a priori known chromatin interaction differences in simulated and real-life settings. HiCdiff is freely available as an R package https://github.com/dozmorovlab/HiCdiff and on Bioconductor (submitted).Author Summary Advances in chromosome conformation capture sequencing technologies (Hi-C) have sparked interest in studying the 3-dimensional (3D) structure of the human genome. The 3D structure of the genome is now considered as a primary regulator of gene expression and other cellular processes. Changes to the 3D structure of the genome are now emerging as a hallmark of cancer and other complex diseases. With the growing availability of Hi-C data generated under different conditions (e.g. tumor-normal, cell-type-specific) methods are needed to compare them. However, biases in Hi-C data hinder their comparative analysis. Several normalization techniques have been developed for removing biases in individual Hi-C datasets, but very few were designed to account for the between-datasets biases. We developed a new method and R package for the joint normalization and differential chromatin interaction detection among multiple Hi-C datasets. Our results show the superiority of our joint normalization methods compared to methods normalizing individual datasets in detecting true chromatin interaction differences. Our method enables further research into discovering the dynamics of 3D genomic changes. ER -