Loop detection using Hi-C data with HiCExplorer. (9th July 2022)

• Record Type: Journal Article
• Authors: Wolff, Joachim; Backofen, Rolf; Grüning, Björn
• Is Part Of: GigaScience. Volume 11(2022)
• Publication Date: 2022-07-09
• Subjects: Hi-C -- Hi-C loop detection -- DNA loops; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giac061
• Languages: English
• ISSNs: 2047-217X

Abstract

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.