Abstract
Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify the biologically significant features, many questions still remain open. In this paper we describe analysis methods of Hi-C (specifically PCHi-C) interaction networks that are strictly focused on topological properties of these networks. The main questions we are trying to answer are: (1) can topological properties of interaction networks for different cell types alone be sufficient to distinguish between these types, and what the most important of such properties are; (2) what is a typical structure of interaction networks and can we assign a biological significance to network structural elements or features?
We have performed analysis on a dataset describing PCHi-C genome-wide interaction networks for 17 types of haematopoietic cells. From this analysis we propose a concrete set Base6 of network topological features (called metrics) that provide good discriminatory power between cell types. The identified features are clearly defined and simple topological properties – the presence and size of connected components and bi-connected components, cliques and cycles of length 2.
We have explored in more detail the component structure of the networks and show that such components tend to be well conserved within particular cell type subgroups and can be well associated with known biological processes. We also have assessed biological significance of network cliques using promoter level expression data and the obtained results indicate that for closely related cell types genes from the same clique tend to be co-expressed.
The research was supported by ERDF project 1.1.1.1/16/A/135.
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Lace, L. et al. (2020). Characteristic Topological Features of Promoter Capture Hi-C Interaction Networks. In: Roque, A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2019. Communications in Computer and Information Science, vol 1211. Springer, Cham. https://doi.org/10.1007/978-3-030-46970-2_10
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