A wavelet-based approach generates quantitative, scale-free and hierarchical descriptions of 3D genome structures and new biological insights
Eukaryotes fold their genomes within nuclei in three-dimensional space, with coordinated multiscale structures including loops, topologically associating domains (TADs), and higher-order chromosome territories. This 3D organization plays essential roles in gene regulation and development, responses to physiological stress, and disease. However, current methodologies to infer these 3D structures from genomic data have limitations. These include varying outcomes depending on the resolution of the analysis and sequencing depth, qualitative results that hinder statistical comparisons, lack of insight into the frequency of the structures in samples with many genomes, and no direct inference of hierarchical structures. These shortcomings can make it difficult for the rigorous comparison of 3D properties across genomes, between experimental conditions, or species. To address these challenges, we developed a wavelet transform-based method (WaveTAD) that describes the 3D nuclear organization in a resolution-free, probabilistic, and hierarchical manner. WaveTAD generates probabilities that capture the variable frequency within samples and shows increased accuracy and sensitivity compared to current approaches. We applied WaveTAD to multiple datasets from Drosophila, mouse, and humans to illustrate new biological insights that our more sensitive and quantitative approach provides, such as the widespread presence of embryonic 3D organization before zygotic genome activation, the effect of multiple CTCF units on the stability of loops and TADs, and the association between gene expression and TAD structures in COVID-19 patients or sex-specific transcription in Drosophila.