Table 1.
Tool | Category | Language | Algorithm | Bias correction? | Statistics | Used for ATAC in literature? | Reference |
---|---|---|---|---|---|---|---|
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] |
FDR false discovery rate, HMM hidden Markov model, SVM support vector machine