Utilizing networks for differential analysis of chromatin interactions

Abstract

Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. In this paper, we introduce two novel approaches to detect differentially interacting genomic regions between two Hi-C experiments using a network model. To make input data from multiple experiments comparable, we propose a normalization strategy guided by network topological properties. We then devise two measurements, using local and global connectivity information from the chromatin interaction networks, respectively, to assess the interaction differences between two experiments. When multiple replicates are present in experiments, our approaches provide the flexibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global method performs better on real Hi-C data. The local and global methods, regardless of pooling, are always superior to two existing methods. Furthermore, our methods work well on both unweighted and weighted networks and our normalization strategy significantly improves the performance compared with raw networks without normalization. Therefore, we believe our methods will be useful for identifying differentially interacting genomic regions.

Network-based method for regions with statistically frequent interchromosomal interactions at single-cell resolution

“…Some methods [34][35][36][37][38][39] are intended to learn a set of chromatin structures representative of the observed chromatin interaction data. Besides the above downstream analysis tasks, there are some computational methods to carry out differential analysis on Hi-C data [40,41] and multiple two-dimensional visualization tools exist [42][43][44][45]. For a comprehensive list of computational tools on Hi-C data, please check out the Omictools website [46] on high-throughput chromosome conformation capture data analysis software tools.…”

Chicdiff: a computational pipeline for detecting differential chromosomal interactions in Capture Hi-C data

“…Such tools are theoretically suitable for the analysis of most sequencing data types, including chromatin immunoprecipitation and Hi-C, leading to the development of wrapper packages around DESeq and EdgeR that facilitate differential analyses for such data (Lareau and Aryee, 2018; Ross-Innes et al , 2012). However, both of these algorithms have been developed with standard RNA sequencing data in mind and therefore not account for or benefit from the specific properties of data resulting from other assays, prompting the development of assay-specific differential analysis tools (Chen et al , 2015; Liu and Ruan, 2017; Stansfield et al , 2018; Xu et al , 2008).…”