Inferring Chromosome Radial Organization from Hi-C Data

Motivation: The nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Microscopy techniques can reveal radial positioning of CTs, but have limited throughput. In comparison, biochemical techniques such as Hi-C has been used extensively to obtain spatial genome organization. To take advantage of the widely available Hi-C data to infer the radial organization of territories, computational techniques are required. Results: We constructed a computational pipeline that can infer the radial arrangement of CTs using a force-directed network layout algorithm under a regression scheme. The pipeline9s computational prediction has a high correlation with the microscopy imaging data for various cell shapes (lymphoblastoid, skin fibroblast, and breast epithelial cells). Furthermore, this computational technique can infer meaningful changes in the arrangement of the CTs in cells having an irregular nuclear shape. Availability and implementation: The algorithm is implemented using Python. The source code of the algorithm is available at https://github.com/priyojitDas/HIRAC.