Skip to main content

Table 2 Methods for identification of statistically significant interactions for Hi-C data

From: Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C

Method name Type Base model Specific features Reference
Duan et al. 2010 Global background Binomial Specifically designed for yeast genome [121]
Fit-Hi-C/FitHiC2 Global background Binomial Spline fitting procedure, compatible with different formats [122, 123]
HOMER Global background Binomial Highly compatible with the HOMER Hi-C analysis pipeline [124]
GOTHiC Global background Binomial Use relative coverage to estimate biases [125]
FitHiChIP Global background Binomial Specifically designed for HiChIP data [126]
HIPPIE Global background Negative binomial Account for fragment length and distance biases [72, 127]
HiC-DC Global background Negative binomial Use zero-inflated model [128]
HMRFBayesHiC Global background Negative binomial Use hidden Markov random field model [129]
FastHiC Global background Negative binomial An updated version of HMRFBayesHi, with improved computing speed [130]
MaxHiC Global background Negative binomial Use ADAM algorithm, identify interactions with enrichment for regulatory elements [131]
CHiCAGO Global background Negative binomial Specifically designed for CHi-C data [132]
ChiCMaxima Global background Local maxima Specifically designed for CHi-C data, more stringent and robust when comparing biological replicates [133]
HICCUP Local background Local enrichment Robust for finding chromatin loops [3]
cLoops Local background DBSCAN Loop detection with less computational resource [134]
Automated identification of stripes Local background Local enrichment Specifically designed to identify architectural stripes [135]