Send to

Choose Destination
J Bioinform Comput Biol. 2017 Dec;15(6):1740008. doi: 10.1142/S021972001740008X. Epub 2017 Oct 19.

Utilizing networks for differential analysis of chromatin interactions.

Author information

* College of Information Technology and Engineering, Marshall University, One John Marshall Drive, Huntington, WV 25755, USA.
† Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA.


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.


Differential analysis; Hi-C; chromatin interactions; networks

[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Atypon Icon for PubMed Central
Loading ...
Support Center