Loop detection using Hi-C data with HiCExplorer

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 ∼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

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: GigaScience. - 11, 22 (2022) , giac061, ISSN: 2047-217X

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Freiburg
• (who): Universität
• (when): 2022
• Creator: Wolff, Joachim ; Backofen, Rolf ; Grüning, Björn

Further information

• DOI: 10.1093/gigascience/giac061
• URN: urn:nbn:de:bsz:25-freidok-2288995
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:35 AM CEST