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

Neph

De novo

C++

Slide window

N

Footprint occupancy score (FOS)

N

[47]

HINT

Python

HMM

N

Probability

N

[133]

HINT-BC

Python

HMM

Y (DNase-seq)

Probability

Y [48]

[130]

HINT-ATAC

Python

HMM

Y (ATAC-seq)

Probability

Y [134]

[134]

Boyle

NA

HMM

N

Probability

N

[135]

Wellington

Python

Binomial test

N (visualize bias)

P value, FDR

Y [48]

[136]

Wellington-bootstrap

Python

Bootstrap DE analysis

N (visualize bias)

P value, FDR

Y [48]

[137]

DNase2TF

R

Binomial test, iteratively merge

Y (DNase-seq)

FDR

Y [134]

[129]

CENTIPEDE

Motif-centric

R

Bayesian mixture model, unsupervised

N

Posterior probability

Y [138]

[139]

msCentipede

Python and Cython

Bayesian multiscale model, unsupervised

Y (can extend to ATAC-seq)

Posterior probability

Y [140]

[140]

Romulus

R

Bayesian mixture model, unsupervised

N

Posterior probability

N

[141]

PIQ

R

Gaussian process model, unsupervised

N

Probability of binding times local chromatin accessibility

Y [134]

[147]

BinDNase

R

Logistic regression, supervised

N

Probability

N

[142]

MILLIPEDE

R

Logistic regression, supervised

N (robust to bias)

Probability

N

[143]

DeFCoM

Python

SVM, supervised

N

Ranking

Y [131, 134]

[131]

BPAC

Python

Random forest, supervised

N

Probability

N

[144]

BaGFoot

R

Differential motif activity

Y

P value

Y [132]

[132]