SBTD: A Novel Method for Detecting Topological Associated Domains from Hi-C Data

The development of Hi-C technology has generated terabytes of chromatin interaction data, which bring possibilities for insight study of chromatin structure. Several studies revealed that mammalian chromosomes are folded into topological associated domains (TADs), which are conserved across cell types. Accurate detection of topological associated domains is now a vital process for revealing the relationship between the structure and function of genome organization. Unfortunately, the current TAD detection methods require massive computing resources, careful parameter adjustment and/or encounter inconsistent results. In this paper, we propose a novel method, Spectral-Based TAD Detector (SBTD), and evaluate its performance with a set of widely accepted statistical methods. We treat the chromatin interaction matrix as a graph and first introduce cosine similarity as a measure of the interaction patterns between bins. The results show that SBTD identifies higher quality TADs than the popular methods (DomainCaller, TopDom and SpectralTAD) and the internal bins of TADs identified by SBTD have higher correlation. Besides, The TADs identified by SBTD show a highly similar histone modification signal enrichment pattern at the boundary as reported in the previous literature. Finally, the motif enrichment analysis shows that compared with the background region, the DNA motifs of known insulator proteins are significantly enriched in the TAD boundary region identified by our method, which proves the high performance of our proposed method. Overall, SBTD is much more effective than existing methods with only one easy-to-adjust parameter, cluster number, for which we provide optimization guidelines.