| --- |
| library_name: multimolecule |
| license: agpl-3.0 |
| pipeline: splice-variant-effect |
| pipeline_tag: other |
| tags: |
| - Biology |
| - RNA |
| - Splicing |
| - rna |
| widget: |
| - example_title: microRNA 21 |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: UAGCUUAUCAGACUGAUGUUGA |
| - example_title: microRNA 146a |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: UGAGAACUGAAUUCCAUGGGUU |
| - example_title: microRNA 155 |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: UUAAUGCUAAUCGUGAUAGGGGUU |
| - example_title: RNA component of mitochondrial RNA processing endoribonuclease |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: GGUUCGUGCUGAAGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA |
| - example_title: 7SK small nuclear RNA |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: GGAUGUGAGGGCGAUCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU |
| - example_title: telomerase RNA component |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: GGGUUGCGGAGGGUGGGCCUGGGAGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC |
| - example_title: vault RNA 2-1 |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: CGGGUCGGAGUUAGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA |
| - example_title: brain cytoplasmic RNA 1 |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: GGCCGGGCGCGGUGGCUCACGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU |
| - example_title: HIV-1 TAR-WT |
| pipeline_tag: splice-variant-effect |
| sequence_type: ncRNA |
| task: splice-variant-effect |
| text: GGUCUCUCUGGUUAGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC |
| - example_title: prion protein (Kanno blood group) |
| pipeline_tag: splice-variant-effect |
| sequence_type: mRNA |
| task: splice-variant-effect |
| text: AUGGCGAACCUUGGCUGCUGGAUGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC |
| - example_title: interleukin 10 |
| pipeline_tag: splice-variant-effect |
| sequence_type: mRNA |
| task: splice-variant-effect |
| text: AUGCACAGCUCAGCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC |
| - example_title: Zaire ebolavirus |
| pipeline_tag: splice-variant-effect |
| sequence_type: mRNA |
| task: splice-variant-effect |
| text: AAUGUUCAAACACUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU |
| - example_title: SARS coronavirus |
| pipeline_tag: splice-variant-effect |
| sequence_type: mRNA |
| task: splice-variant-effect |
| text: AUGUUUAUUUUCUUAUUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU |
| - example_title: insulin |
| pipeline_tag: splice-variant-effect |
| sequence_type: mRNA |
| task: splice-variant-effect |
| text: AUGGCCCUGUGGAUGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG |
| - example_title: cyclin dependent kinase inhibitor 2A |
| pipeline_tag: splice-variant-effect |
| sequence_type: mRNA |
| task: splice-variant-effect |
| text: AUGGAGCCGGCGGCGGGGAGCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA |
| - example_title: human papillomavirus type 16 E6 |
| pipeline_tag: splice-variant-effect |
| sequence_type: mRNA |
| task: splice-variant-effect |
| text: AUGCACCAAAAGAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA |
| - example_title: NRAS proto-oncogene |
| pipeline_tag: splice-variant-effect |
| sequence_type: 5' UTR |
| task: splice-variant-effect |
| text: GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUCAUGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA |
| - example_title: amyloid beta precursor protein |
| pipeline_tag: splice-variant-effect |
| sequence_type: 5' UTR |
| task: splice-variant-effect |
| text: GUCAGUUUCCUCGGCAGCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG |
| - example_title: RUNX family transcription factor 1 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 5' UTR |
| task: splice-variant-effect |
| text: ACUUCUUUGGGCCUCAUAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG |
| - example_title: fragile X messenger ribonucleoprotein 1 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 5' UTR |
| task: splice-variant-effect |
| text: CUCAGUCAGGCGCUCAGCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG |
| - example_title: MYC proto-oncogene |
| pipeline_tag: splice-variant-effect |
| sequence_type: 5' UTR |
| task: splice-variant-effect |
| text: AACUCGCUGUAGUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG |
| - example_title: activating transcription factor 4 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 5' UTR |
| task: splice-variant-effect |
| text: CAUUUCUACUUUGCCCGCCCACAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC |
| - example_title: Human GPI protein p137 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 3' UTR |
| task: splice-variant-effect |
| text: UUUUUAAAAGGAAAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC |
| - example_title: nucleophosmin 1 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 3' UTR |
| task: splice-variant-effect |
| text: GAAAAUAGUUUAAACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA |
| - example_title: superoxide dismutase 1 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 3' UTR |
| task: splice-variant-effect |
| text: ACAUUCCCUUGGAUGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA |
| - example_title: hemoglobin subunit alpha 2 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 3' UTR |
| task: splice-variant-effect |
| text: CUGGAGCCUCGGUAGCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA |
| - example_title: BRAF proto-oncogene |
| pipeline_tag: splice-variant-effect |
| sequence_type: 3' UTR |
| task: splice-variant-effect |
| text: AACAAAUGAGUGAGAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG |
| - example_title: H3 clustered histone 1 |
| pipeline_tag: splice-variant-effect |
| sequence_type: 3' UTR |
| task: splice-variant-effect |
| text: UUACUGUGGUCUCUCUGACGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC |
| --- |
| |
| # MMSplice |
|
|
| Modular modeling of the effects of genetic variants on splicing. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of the [MMSplice: modular modeling improves the predictions of genetic variant effects on splicing](https://doi.org/10.1186/s13059-019-1653-z) by Jun Cheng, et al. |
|
|
| The OFFICIAL repository of MMSplice is at [gagneurlab/MMSplice_MTSplice](https://github.com/gagneurlab/MMSplice_MTSplice). |
|
|
| > [!TIP] |
| > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. |
|
|
| **The team releasing MMSplice did not write this model card for this model so this model card has been written by the MultiMolecule team.** |
|
|
| ## Model Details |
|
|
| MMSplice is a _modular_ neural network for predicting the effect of genetic variants on pre-mRNA splicing. It decomposes an exon together with its flanking introns into five regions and scores each region with an independent small convolutional sub-network. For variant-effect estimation, the model is run on both the reference and the alternative sequence, and the per-module score deltas are combined by a fixed linear model into a delta-logit-PSI splicing-effect score. Please refer to the [Training Details](#training-details) section for more information on the training process. |
|
|
| ### Model Specification |
|
|
| | Num Modules | Num Parameters (M) | FLOPs (M) | MACs (M) | |
| | ----------- | ------------------ | --------- | -------- | |
| | 5 | 0.057 | 5.71 | 2.79 | |
|
|
| (FLOPs and MACs measured on a 220 bp exon-with-flanks input.) |
|
|
| ### Links |
|
|
| - **Code**: [multimolecule.mmsplice](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/mmsplice) |
| - **Data**: Human splice-site and exon data with MPRA exon-skipping variant-effect measurements |
| - **Paper**: [MMSplice: modular modeling improves the predictions of genetic variant effects on splicing](https://doi.org/10.1186/s13059-019-1653-z) |
| - **Developed by**: Jun Cheng, Thi Yen Duong Nguyen, Kamil J. Cygan, Muhammed Hasan Çelik, William G. Fairbrother, Žiga Avsec, Julien Gagneur |
| - **Model type**: Modular 1D CNN with five region-specific sub-networks for splice variant-effect prediction |
| - **Original Repository**: [gagneurlab/MMSplice_MTSplice](https://github.com/gagneurlab/MMSplice_MTSplice) |
|
|
| ## Usage |
|
|
| The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: |
|
|
| ```bash |
| pip install multimolecule |
| ``` |
|
|
| ### Direct Use |
|
|
| #### Module Scores |
|
|
| ```python |
| >>> import torch |
| >>> from multimolecule import RnaTokenizer, MmSpliceForSequencePrediction |
| |
| >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/mmsplice") |
| >>> model = MmSpliceForSequencePrediction.from_pretrained("multimolecule/mmsplice") |
| >>> _ = model.eval() |
| >>> left_intron = "A" * 100 |
| >>> exon = "C" * 20 |
| >>> right_intron = "G" * 100 |
| >>> reference = tokenizer(left_intron + exon + right_intron, add_special_tokens=False, return_tensors="pt") |
| >>> output = model.model(**reference) |
| >>> output["logits"].shape |
| torch.Size([1, 5]) |
| ``` |
|
|
| #### Variant Effect |
|
|
| ```python |
| >>> import torch |
| >>> from multimolecule import RnaTokenizer, MmSpliceForSequencePrediction |
| |
| >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/mmsplice") |
| >>> model = MmSpliceForSequencePrediction.from_pretrained("multimolecule/mmsplice") |
| >>> _ = model.eval() |
| >>> left_intron = "A" * 100 |
| >>> exon = "C" * 20 |
| >>> right_intron = "G" * 100 |
| >>> reference = tokenizer(left_intron + exon + right_intron, add_special_tokens=False, return_tensors="pt") |
| >>> alternative_exon = exon[:10] + "U" + exon[11:] |
| >>> alternative = tokenizer(left_intron + alternative_exon + right_intron, add_special_tokens=False, return_tensors="pt") |
| >>> output = model( |
| ... reference["input_ids"], |
| ... alternative_input_ids=alternative["input_ids"], |
| ... ) |
| >>> output["logits"].shape |
| torch.Size([1, 1]) |
| ``` |
|
|
| ### Interface |
|
|
| - **Input length**: exon sequence with 100 nt upstream intronic context + 100 nt downstream intronic context |
| - **Tokenization**: disable special tokens; the embedding layer maps `A/C/G/U` ids to the four upstream channels and maps `N`, padding, special, and unknown tokens to all-zero columns |
| - **Output (reference-only call, `input_ids` / `inputs_embeds`)**: per-module score vector `logits` of shape `(batch_size, 5)` |
|
|
| ### Variant Effect |
|
|
| - **Reference + alternative call** (also pass `alternative_input_ids` / `alternative_inputs_embeds`): additionally returns `alternative_logits` and per-module `delta_logits = alternative_logits - logits` |
| - **`MmSpliceForSequencePrediction`**: requires both reference and alternative; returns the combined scalar delta-logit-PSI score of shape `(batch_size, 1)` |
|
|
| ## Training Details |
|
|
| MMSplice was trained as five independent modules on splicing data and the modules were combined with a linear model to predict variant effects on percent-spliced-in (PSI). |
|
|
| ### Training Data |
|
|
| The acceptor, donor, exon, and intron modules were trained on splice-site and exon data derived from human reference transcripts. The combining linear model was fit against a massively parallel reporter assay (MPRA) of exon-skipping variants. |
|
|
| ### Training Procedure |
|
|
| #### Pre-training |
|
|
| Each module was trained with a sequence-to-scalar objective scoring its region. The module scores (and their reference/alternative deltas) were then combined by a fixed linear model into a delta-logit-PSI splicing-effect score. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{cheng2019mmsplice, |
| title = {MMSplice: modular modeling improves the predictions of genetic variant effects on splicing}, |
| author = {Cheng, Jun and Nguyen, Thi Yen Duong and Cygan, Kamil J and {\c{C}}elik, Muhammed Hasan and Fairbrother, William G and Avsec, {\v{Z}}iga and Gagneur, Julien}, |
| journal = {Genome Biology}, |
| volume = 20, |
| number = 1, |
| pages = {48}, |
| year = 2019, |
| publisher = {Springer}, |
| doi = {10.1186/s13059-019-1653-z} |
| } |
| ``` |
|
|
| > [!NOTE] |
| > The artifacts distributed in this repository are part of the MultiMolecule project. |
| > If MultiMolecule supports your research, please cite the MultiMolecule project as follows: |
|
|
| ```bibtex |
| @software{chen_2024_12638419, |
| author = {Chen, Zhiyuan and Zhu, Sophia Y.}, |
| title = {MultiMolecule}, |
| doi = {10.5281/zenodo.12638419}, |
| publisher = {Zenodo}, |
| url = {https://doi.org/10.5281/zenodo.12638419}, |
| year = 2024, |
| month = may, |
| day = 4 |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. |
|
|
| Please contact the authors of the [MMSplice paper](https://doi.org/10.1186/s13059-019-1653-z) for questions or comments on the paper/model. |
|
|
| ## License |
|
|
| This model implementation is licensed under the [GNU Affero General Public License](license.md). |
|
|
| For additional terms and clarifications, please refer to our [License FAQ](license-faq.md). |
|
|
| ```spdx |
| SPDX-License-Identifier: AGPL-3.0-or-later |
| ``` |
