Comparison of computational methods for the identification of topologically associating domains

TLDR

Chromatin folding creates topologically associating domains (TADs), which have been identified across species and tissues, but their reliable detection depends on accurate computational identification from Hi‑C data. The study aims to evaluate and compare 22 TAD‑calling algorithms across 20 experimental conditions. The authors applied each method to Hi‑C datasets of varying resolution and normalization, assessing performance across the conditions. Results show that TAD size and number differ markedly among callers and resolutions, yet a core set of algorithms consistently recover reproducible, biologically enriched domains, providing a reference and guidelines for method selection.

Abstract

Chromatin folding gives rise to structural elements among which are clusters of densely interacting DNA regions termed topologically associating domains (TADs). TADs have been characterized across multiple species, tissue types, and differentiation stages, sometimes in association with regulation of biological functions. The reliability and reproducibility of these findings are intrinsically related with the correct identification of these domains from high-throughput chromatin conformation capture (Hi-C) experiments. Here, we test and compare 22 computational methods to identify TADs across 20 different conditions. We find that TAD sizes and numbers vary significantly among callers and data resolutions, challenging the definition of an average TAD size, but strengthening the hypothesis that TADs are hierarchically organized domains, rather than disjoint structural elements. Performances of these methods differ based on data resolution and normalization strategy, but a core set of TAD callers consistently retrieve reproducible domains, even at low sequencing depths, that are enriched for TAD-associated biological features. This study provides a reference for the analysis of chromatin domains from Hi-C experiments and useful guidelines for choosing a suitable approach based on the experimental design, available data, and biological question of interest.