RT Journal Article
SR Electronic
T1 Hypothesis-driven probabilistic modelling enables a principled perspective of genomic compartments
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.10.01.510432
DO 10.1101/2022.10.01.510432
A1 Hagai Kariti
A1 Tal Feld
A1 Noam Kaplan
YR 2022
UL http://biorxiv.org/content/early/2022/10/05/2022.10.01.510432.abstract
AB The Hi-C method has revolutionized the study of genome organization, yet interpretation of Hi-C interaction frequency maps remains a major challenge. Genomic compartments are a checkered Hi-C interaction pattern suggested to represent the partitioning of the genome into two self-interacting states associated with active and inactive chromatin. Based on a few elementary mechanistic assumptions, we derive a generative probabilistic model of genomic compartments, called deGeco. Testing our model, we find it can explain observed Hi-C interaction maps in a highly robust manner, allowing accurate inference of interaction probability maps from extremely sparse data without any training of parameters. Taking advantage of the interpretability of the model parameters, we then test hypotheses regarding the nature of genomic compartments. We find clear evidence of multiple states, and that these states self-interact with different affinities. We also find that the interaction rules of chromatin states differ considerably within and between chromosomes. Inspecting the molecular underpinnings of a four-state model, we show that a simple classifier can use histone marks to predict the underlying states with 87% accuracy. Finally, we observe instances of mixed-state loci and analyze these loci in single-cell Hi-C maps, finding that mixing of states occurs mainly at the population level.Competing Interest StatementThe authors have declared no competing interest.