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Table 1 Summary of footprinting tools, including software category, programming language, algorithm or statistical method, bias correction for DNase-seq or ATAC-seq, and output statistics. In addition, the second last column exemplifies the application of tools in ATAC-seq data

From: From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis

ToolCategoryLanguageAlgorithmBias correction?StatisticsUsed for ATAC in literature?Reference
NephDe novoC++Slide windowNFootprint occupancy score (FOS)N[47]
HINTPythonHMMNProbabilityN[133]
HINT-BCPythonHMMY (DNase-seq)ProbabilityY [48][130]
HINT-ATACPythonHMMY (ATAC-seq)ProbabilityY [134][134]
BoyleNAHMMNProbabilityN[135]
WellingtonPythonBinomial testN (visualize bias)P value, FDRY [48][136]
Wellington-bootstrapPythonBootstrap DE analysisN (visualize bias)P value, FDRY [48][137]
DNase2TFRBinomial test, iteratively mergeY (DNase-seq)FDRY [134][129]
CENTIPEDEMotif-centricRBayesian mixture model, unsupervisedNPosterior probabilityY [138][139]
msCentipedePython and CythonBayesian multiscale model, unsupervisedY (can extend to ATAC-seq)Posterior probabilityY [140][140]
RomulusRBayesian mixture model, unsupervisedNPosterior probabilityN[141]
PIQRGaussian process model, unsupervisedNProbability of binding times local chromatin accessibilityY [134][147]
BinDNaseRLogistic regression, supervisedNProbabilityN[142]
MILLIPEDERLogistic regression, supervisedN (robust to bias)ProbabilityN[143]
DeFCoMPythonSVM, supervisedNRankingY [131, 134][131]
BPACPythonRandom forest, supervisedNProbabilityN[144]
BaGFootRDifferential motif activityYP valueY [132][132]
  1. FDR false discovery rate, HMM hidden Markov model, SVM support vector machine