A comparison of topologically associating domain callers over mammals at high resolution

Abstract

Background Topologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in. Result We have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties. Conclusion We have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.

The 3D chromatin landscape of rhabdomyosarcoma

“…We observed that TAD assignments are very sensitive to many parameters, among which include resolution of bins and size of the moving window, which has been seen by us and others, and remains a key area in bioinformatic research ( 59 , 60 ). For our analysis, we used 50 kb bins and a moving window of 10-fold on each side, resulting in median TAD size of ∼1 Mb (Figure 2A and B).…”

Mini-review: Gene regulatory network benefits from three-dimensional chromatin conformation and structural biology

“…The inferred results of different TAD identification methods are also different. The resolution size is another factor that affects the results [120] . Current criteria for evaluating TAD identification tools include robustness concerning bin size and normalization strategy, the minimum required data volume, reproducibility, biological relevance, and computational efficiency [121] .…”

Improving Hi-C contact matrices using genome graphs

“…They are introduced in Zufferey et al [2018], Liu et al [2022] and Sefer [2022]. Since the bin size in our experiments is 10kb, we adjust the definitions of these three metrics as the following:…”