Back

optimalTAD: annotation of topologically associating domains based on chromatin marks enrichment

Smirnov, D.; Toiber, D.; Gelfand, M.; Khrameeva, E.

2023-03-07 bioinformatics
10.1101/2023.03.06.531254 bioRxiv
Show abstract

In many eukaryotes, chromosomes are organized as strings of spatially segregated Topologically Associating Domains (TADs), characterized by a substantially increased frequency of interactions within them. Boundaries of TADs are highly enriched in histone acetylation chromatin marks and occupied binding sites of architectural proteins, highlighting the functional role of TADs in the regulation of gene expression. While many computational approaches have been developed for TAD identification, it remains challenging because of their nested structure, resulting in weakly overlapping sets of TADs at different scales. Here, we propose a novel algorithm optimalTAD for identifying the optimal set of TADs based on epigenetic marks enrichment. Assuming that the most dramatic enrichment corresponds to the best annotation of TAD boundaries, our algorithm optimizes TAD calling parameters by maximizing the difference in chromatin mark levels between TADs and their boundaries. Using this algorithm, we annotated TADs in multiple publicly available fruit fly and mammalian Hi-C datasets and identified a set of epigenetic marks that are best suited for TAD prediction. Through the analysis of diverse organisms and cell types with distinct underlying principles of TAD organization, we have shown that optimalTAD is a universal tool suitable for studying TAD structure, functions, and properties unique to specific cell types and organisms. optimalTAD is freely available at GitHub: https://github.com/cosmoskaluga/optimalTAD. Key PointsO_LIWe assume that the most dramatic enrichment of epigenetic marks corresponds to the best annotation of TAD boundaries. C_LIO_LIOur algorithm optimizes TAD calling parameters by maximizing the difference in chromatin mark levels between TADs and their boundaries. C_LIO_LIoptimalTAD is a universal tool that is applicable for studying TAD characteristics in diverse organisms and cell types. C_LIO_LIoptimalTAD enables the identification of a specific set of epigenetic marks that are most suitable for annotating TADs. C_LI

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.