@Article{Wolff_Backofen_Gruning-Loop_detec_using-2022,
  author =	 {Wolff, Joachim and Backofen, Rolf and Gruning, Bjorn},
  title =	 {Loop detection using {Hi}-{C} data with {HiCExplorer}},
  journal =	 {Gigascience},
  year =	 {2022},
  volume =	 {11},
  number =	 {},
  pages =	 {},
  user =	 {backofen},
  pmid =	 {35809047},
  doi = 	 {10.1093/gigascience/giac061},
  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.}
}