Binless normalization of Hi-C data provides significant interaction and difference detection independently of resolution

Abstract

3C-like experiments, such as 4C or Hi-C, have been fundamental in understanding genome organization. Thanks to these technologies, it is now known, for example, that Topologically Associating Domains (TADs) and chromatin loops are implicated in the dynamic interplay of gene activation and repression, and their disruption can have dramatic effects on embryonic development. To make their detection easier, scientists have endeavored into deeper sequencing to mechanically increase the chances to detect weaker signals such as chromatin loops. Part of this mindset can be attributed to the limitations of existing software: the analysis of Hi-C experiments is both statistically and computationally demanding. Here, we devise a new way to represent Hi-C data, which leads to a more detailed classification of paired-end reads and, ultimately, to a new normalization and interaction detection method. Unlike any other, Binless is resolution-agnostic, and adapts to the quality and quantity of available data. We demonstrate its capacities to call interactions and differences and make the software freely available.