HiC1Dmetrics: framework to extract various one-dimensional features from chromosome structure data

Eukaryotic genomes are organized in a three-dimensional spatial structure. In this regard, the development of chromosome conformation capture methods has enabled studies of chromosome organization on a genomic scale. Hi-C, the high-throughput chromosome conformation capture method, can reveal a population-averaged, hierarchical chromatin structure. The typical Hi-C analysis uses a two-dimensional (2D) contact matrix that indicates contact frequencies between all possible genomic position pairs. Oftentimes, however, such a 2D matrix is not amenable to handling quantitative comparisons, visualizations and integrations across multiple datasets. Although several one-dimensional (1D) metrics have been proposed to depict structural information in Hi-C data, their effectiveness is still underappreciated. Here, we first review the currently available 1D metrics for individual Hi-C samples or two-sample comparisons and then discuss their validity and suitable analysis scenarios. We also propose several new 1D metrics to identify additional unique features of chromosome structures. We highlight that the 1D metrics are reproducible and robust for comparing and visualizing multiple Hi-C samples. Moreover, we show that 1D metrics can be easily combined with epigenome tracks to annotate chromatin states in greater details. We develop a new framework, called HiC1Dmetrics, to summarize all 1D metrics discussed in this study. HiC1Dmetrics is open-source (github.com/wangjk321/HiC1Dmetrics) and can be accessed from both command-line and web-based interfaces. Our tool constitutes a useful resource for the community of chromosome-organization researchers.