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

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Abstract

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.

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Availability of data and materials

The Hi-C data sets of human cell types (hESC and IMR90) and mouse cell types (mCortex and mESC) were downloaded from the GEO database under the GEO accession number GSE35156. They can also be downloaded from http://chromosome.sdsc.edu/mouse/hi-c/download.html.

Code availability

The source code is available here: https://github.com/chunlin-long/SBTD.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 21775107, 21675114); the National Science and Technology Major Project of the Ministry of Science and Technology under Grant 2018ZX10201002-002-004, 2018/1-2020/12.

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610064, China

    Chunlin Long, Jianrong Yan & Min Zhu

  2. College of Chemistry, Sichuan University, Chengdu, 610064, China

    Yin**g Liao & Menglong Li

  3. School of Cyber Science and Engineering, Sichuan University, Chengdu, 610207, China

    Yizhou Li

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  1. Chunlin Long
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  2. Yin**g Liao
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  3. Yizhou Li
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  5. Min Zhu
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Correspondence to Min Zhu or Menglong Li.

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Long, C., Liao, Y., Li, Y. et al. SBTD: A Novel Method for Detecting Topological Associated Domains from Hi-C Data. Interdiscip Sci Comput Life Sci 13, 638–651 (2021). https://doi.org/10.1007/s12539-021-00453-4

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