Considerations and caveats for analyzing chromatin compartments

Abstract

Genomes are organized into nuclear compartments, separating active from inactive chromatin. Chromatin compartments are readily visible in a large number of species by experiments that map chromatin conformation genome-wide. When analyzing these maps, a common step is the identification of genomic intervals that interact within A (active) and B (inactive) compartments. It has also become increasingly common to identify and analyze subcompartments. We review different strategies to identify A/B and subcompartment intervals, including a discussion of various machine-learning approaches to predict these features. We then discuss the strengths and limitations of current strategies and examine how these aspects of analysis may have impacted our understanding of chromatin compartments.

Chromatin alternates between A and B compartments at kilobase scale for subgenic organization

“…Loci in the same spatial compartment exhibit relatively frequent contacts in a Hi-C map, even when they lie far apart along a chromosome or on entirely different chromosomes 1 , 2 . Accurate classification of the resulting genome-wide contact patterns requires a large number of contacts to be characterized at each locus 3 . As such, genome-wide compartment profiles in human cells are typically generated at resolutions ranging from 40 kb to 1 Mb 1 , 2 , 4 .…”

“…Even recently published fine-scale maps using Micro-C did not investigate compartment eigenvector at <100 kb resolution 5 , 6 . This may be because extant compartment detection algorithms require operations, such as calculating principal eigenvectors 1 , which are computationally intractable when the underlying matrices have millions of rows and columns—high-resolution Hi-C matrices 3 . Indeed, fine-scale compartment analysis has been more feasible in organisms with smaller genomes, such as Drosophila melanogaster 7 , 8 .…”