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

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] |