@article{Wolff_Backofen_Gruning-Loop_detec_using-2022,
author = {Wolff, Joachim and Backofen, Rolf and Grüning, Björn},
title = {Loop detection using {Hi}-{C} data with {HiCExplorer}},
journal = {Gigascience},
year = {2022},
doi = {10.1093/gigascience/giac061},
volume = {11},
user = {backofen},
pmid = {35809047},
pages = {},
number = {},
issn = {2047-217X},
abstract = {BACKGROUND: Chromatin loops are an essential factor in the 
            structural organization of the genome; however, their 
            detection in Hi-C interaction matrices is a challenging and 
            compute-intensive task. The approach presented here, 
            integrated into the HiCExplorer software, shows a chromatin 
            loop detection algorithm that applies a strict candidate 
            selection based on continuous negative binomial 
            distributions and performs a Wilcoxon rank-sum test to 
            detect enriched Hi-C interactions. RESULTS: HiCExplorer's 
            loop detection has a high detection rate and accuracy. It is 
            the fastest available CPU implementation and utilizes all 
            threads offered by modern multicore platforms. CONCLUSIONS: 
            HiCExplorer's method to detect loops by using a continuous 
            negative binomial function combined with the donut approach 
            from HiCCUPS leads to reliable and fast computation of 
            loops. All the loop-calling algorithms investigated provide 
            differing results, which intersect by $\sim 50\%$ at most. 
            The tested in situ Hi-C data contain a large amount of 
            noise; achieving better agreement between loop calling 
            algorithms will require cleaner Hi-C data and therefore 
            future improvements to the experimental methods that 
            generate the data.}
}