From: MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

MMSplice model | Training data | Architecture | Loss function | Target value | Parameters |
---|---|---|---|---|---|

Donor module | GENCODE 24, positive: annotated donors, negative: random sequence (“Methods” section) | Four layer neural network with dropout and batch normalization, Additional file 1: Figure S1A | Binary cross entropy | Positive vs. negative | 18,049 |

Acceptor module | GENCODE 24, positive: annotated acceptors, negative: random sequence (“Methods” section) | Two layer conv. neural network with dropout and batch normalization, Additional file 1: Figure S1B | Binary cross entropy | Positive vs. negative | 4833 |

Exon 5 ^{′} module
| MPRA [18] exonic sequence |
One conv. layer shared with the Exon 3 ^{′} module, followed with one specific dense layer, Additional file 1: Figure S2
| Binary cross entropy |
Ψ
_{5}
| 6145 |

Exon 3 ^{′} module
| MPRA [18] exonic sequence |
One conv. layer shared with the Exon 5 ^{′} module, followed with one specific dense layer, Additional file 1: Figure S2
| Binary cross entropy |
Ψ
_{3}
| 6145 |

Intron 5 ^{′} module
| MPRA [18] intronic sequence |
One conv. layer shared with the Intron 3 ^{′} module, followed with one specific dense layer, Additional file 1: Figure S2
| Binary cross entropy |
Ψ
_{3}
| 13,825 |

Intron 3 ^{′} module
| MPRA [18] intronic sequence |
One conv. layer shared with the Intron 5 ^{′} module, followed with one specific dense layer, Additional file 1: Figure S2
| Binary cross entropy |
Ψ
_{5}
| 13,825 |

Δlogit(Ψ) model
| Vex-seq [29] | Linear regression | Huber loss |
Δlogit(Ψ), Eq. 2
| 9 |

Splicing efficiency model (in vivo) | MaPSy (“Methods” section) | Linear regression | Huber loss | Splicing efficiency, Eq. 10 | 5 |

Splicing efficiency model (in vitro) | MaPSy (“Methods” section) | Linear regression | Huber loss | Splicing efficiency, Eq. 10 | 5 |

Pathogenicity model (w/o phyloP and CADD) | ClinVar [30] [ − 10, 10] around donor, [ − 40, 10] around acceptor | Logistic regression | Binary cross entropy | Pathogenic vs. benign | 14 |

Pathogenicity model (with phyloP and CADD) | ClinVar [30] [ − 10, 10] around donor, [ − 40, 10] around acceptor | Logistic regression | Binary cross entropy | Pathogenic vs. benign | 18 |