Abstract
HiChIP/PLAC-seq is increasingly becoming popular for profiling 3D chromatin contacts among regulatory elements and for annotating functions of genetic variants. Here we describe FitHiChIP, a computational method for loop calling from HiChIP/PLAC-seq data, which jointly models the non-uniform coverage and genomic distance scaling of contact counts to compute statistical significance estimates. We also develop a technique to filter putative bystander loops that can be explained by stronger adjacent loops. Compared to existing methods, FitHiChIP performs better in recovering contacts reported by Hi-C, promoter capture Hi-C and ChIA-PET experiments and in capturing previously validated promoter-enhancer interactions. FitHiChIP loop calls are reproducible among replicates and are consistent across different experimental settings. Our work also provides a framework for differential HiChIP analysis with an option to utilize ChIP-seq data for further characterizing differential loops. Even though designed for HiChIP, FitHiChIP is also applicable to other conformation capture assays.
Highlights
HiChIP/PLAC-seq is increasingly becoming popular for profiling 3D chromatin contacts among regulatory elements and for annotating functions of genetic variants
We show that further filtering of such detected 3D differences by restricting them to be overlapping with a FitHiChIP loop call in at least one input sample produces a loop set with significant enrichment differences in APA analysis of the HiChIP data from the compared cell types as well as significant differences in underlying Hi-C contact counts (Supplementary Note 11 and Supplementary Fig. 37c–e)
Here we describe FitHiChIP, an empirical null-based, flexible computational method for statistical significance estimation and loop calling from HiChIP/PLAC-seq data
Summary
HiChIP/PLAC-seq is increasingly becoming popular for profiling 3D chromatin contacts among regulatory elements and for annotating functions of genetic variants. We describe FitHiChIP, a computational method for loop calling from HiChIP/PLAC-seq data, which jointly models the non-uniform coverage and genomic distance scaling of contact counts to compute statistical significance estimates. FitHiC, on the other end, estimates a background model from the global set of contact counts to find enrichment of each pixel with respect to overall expectation at that genomic distance. Both methods assume that each genomic bin is represented by roughly equal number of overall contacts, an assumption that is not valid for HiChIP and other targeted conformation capture assays such as ChIA-PET and promoter capture Hi-C (PCHiC)[4,8]. We provide extensive comparisons of our tool to both of these existing methods using a number of different and complementary metrics given the lack of a gold standard validation set
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