Abstract
Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization. These methods have revealed several genomic features, such as chromatin loops, whose disruption can have dramatic effects in gene regulation. Unfortunately, their detection is difficult; current methods require that the users choose the resolution of interaction maps based on dataset quality and sequencing depth. Here, we introduce Binless, a resolution-agnostic method that adapts to the quality and quantity of available data, to detect both interactions and differences. Binless relies on an alternate representation of Hi-C data, which leads to a more detailed classification of paired-end reads. Using a large-scale benchmark, we demonstrate that Binless is able to call interactions with higher reproducibility than other existing methods. Binless, which is freely available, can thus reliably be used to identify chromatin loops as well as for differential analysis of chromatin interaction maps.
Highlights
Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization
For mammalian genomes, compartments are of several megabases (Mb) in size, topologically associated domains (TADs) are of about 1 Mb in size, and chromatin loops are a few kb
Ideally the detection of any 3D genomic feature needs to be done with bin-less interaction matrices, in which the data is fused in cells of varying resolution adapted to the features of interest
Summary
Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization These methods have revealed several genomic features, such as chromatin loops, whose disruption can have dramatic effects in gene regulation. Depend the quality of the data and on the quantity of sequencing reads that determines the genomic resolution to which interaction matrices will be normalized This step is crucial, as genomic features such as TADs16,17 or chromatin loops[4] are detected from normalized matrices. It is still best to perform redundant analyses with several methods to conclude the validity of a set of detected interactions We show that the resulting normalized matrices by Binless, in addition to being visually simpler than regular Hi-C maps, allow for improved and reproducible interaction and difference detection
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