TADBay: A Bayesian Construction of Topologically Associated Domains.

Advances in imaging and sequencing techniques provide increasingly informative but complex structural data for understanding the 3D genome. Segmenting 3D conformations of chromosomes into their topologically associated domains (TADs) – structurally homogeneous parts of a chromosome – is an essential tool in shape analysis. TAD segmentation allows one to divide complex chromosomal structures into smaller, simpler geometries and facilitates statistical analysis of their shapes. There are several TAD segmentation procedures in the current literature, but they often lack consistency and interpretability. We propose a novel algorithm for determining TADs, called TADBay, directly from contact matrices, which avoids the difficult step of estimating 3D conformations. While consistent with some current top-performing existing callers, it has an added ability to provide a hierarchical organization of TADs and subTADs. To validate TADBay, we utilize simulated contact matrices with ground truth TADs which can be used to benchmark TAD callers. We demonstrate the strengths of our caller through both simulated data and the IMR90 real dataset.