Chromosomes are not positioned randomly within a nucleus, but instead, they adopt preferred spatial conformations to facilitate necessary long-range gene–gene interactions and regulations. Thus, obtaining the 3D shape of chromosomes of a genome is critical for understanding how the genome folds, functions and how its genes interact and are regulated. Here, we describe a method to reconstruct preferred 3D structures of individual chromosomes of the human genome from chromosomal contact data generated by the Hi-C chromosome conformation capturing technique. A novel parameterized objective function was designed for modeling chromosome structures, which was optimized by a gradient descent method to generate chromosomal structural models that could satisfy as many intra-chromosomal contacts as possible. We applied the objective function and the corresponding optimization method to two Hi-C chromosomal data sets of both a healthy and a cancerous human B-cell to construct 3D models of individual chromosomes at resolutions of 1 MB and 200 KB, respectively. The parameters used with the method were calibrated according to an independent fluorescence in situ hybridization experimental data. The structural models generated by our method could satisfy a high percentage of contacts (pairs of loci in interaction) and non-contacts (pairs of loci not in interaction) and were compatible with the known two-compartment organization of human chromatin structures. Furthermore, structural models generated at different resolutions and from randomly permuted data sets were consistent.
It is hard to get the higher order structure of a chromosome as it actually exists. But recent technologies developed over the last decade have opened up some really innovative results.
Chromosome conformation capture identifies regions that come into close contact. High throughput approaches have created a huge amount of data but properly filtering and mining this to provide actual information has required further innovation.
This paper provides a new approach and new math to create models for chromosomes, not only in healthy cells but any possible changes found in cancer cells.
No new lab work. Jusr new math and a different approach. What should we call doing research without really needing a research lab – no bunsen burners, no flasks and no pipettes?