Utilizing networks for differential analysis of chromatin interactions

Lu Liu, Jianhua Ruan

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations


    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.

    Original languageEnglish (US)
    Article number1740008
    JournalJournal of bioinformatics and computational biology
    Issue number6
    StatePublished - Dec 1 2017


    • Differential analysis
    • Hi-C
    • chromatin interactions
    • networks

    ASJC Scopus subject areas

    • Biochemistry
    • Molecular Biology
    • Computer Science Applications


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