| --- |
| datasets: |
| - multimolecule/gencode |
| library_name: multimolecule |
| license: agpl-3.0 |
| pipeline: splice-site |
| pipeline_tag: other |
| tags: |
| - Biology |
| - RNA |
| - Splicing |
| - rna |
| widget: |
| - example_title: microRNA 21 |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: UAGCUUAUCAGACUGAUGUUGA |
| - example_title: microRNA 146a |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: UGAGAACUGAAUUCCAUGGGUU |
| - example_title: microRNA 155 |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: UUAAUGCUAAUCGUGAUAGGGGUU |
| - example_title: RNA component of mitochondrial RNA processing endoribonuclease |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: GGUUCGUGCUGAAGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA |
| - example_title: 7SK small nuclear RNA |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: GGAUGUGAGGGCGAUCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU |
| - example_title: telomerase RNA component |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: GGGUUGCGGAGGGUGGGCCUGGGAGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC |
| - example_title: vault RNA 2-1 |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: CGGGUCGGAGUUAGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA |
| - example_title: brain cytoplasmic RNA 1 |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: GGCCGGGCGCGGUGGCUCACGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU |
| - example_title: HIV-1 TAR-WT |
| pipeline_tag: splice-site |
| sequence_type: ncRNA |
| task: splice-site |
| text: GGUCUCUCUGGUUAGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC |
| - example_title: prion protein (Kanno blood group) |
| pipeline_tag: splice-site |
| sequence_type: mRNA |
| task: splice-site |
| text: AUGGCGAACCUUGGCUGCUGGAUGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC |
| - example_title: interleukin 10 |
| pipeline_tag: splice-site |
| sequence_type: mRNA |
| task: splice-site |
| text: AUGCACAGCUCAGCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC |
| - example_title: Zaire ebolavirus |
| pipeline_tag: splice-site |
| sequence_type: mRNA |
| task: splice-site |
| text: AAUGUUCAAACACUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU |
| - example_title: SARS coronavirus |
| pipeline_tag: splice-site |
| sequence_type: mRNA |
| task: splice-site |
| text: AUGUUUAUUUUCUUAUUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU |
| - example_title: insulin |
| pipeline_tag: splice-site |
| sequence_type: mRNA |
| task: splice-site |
| text: AUGGCCCUGUGGAUGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG |
| - example_title: cyclin dependent kinase inhibitor 2A |
| pipeline_tag: splice-site |
| sequence_type: mRNA |
| task: splice-site |
| text: AUGGAGCCGGCGGCGGGGAGCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA |
| - example_title: human papillomavirus type 16 E6 |
| pipeline_tag: splice-site |
| sequence_type: mRNA |
| task: splice-site |
| text: AUGCACCAAAAGAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA |
| - example_title: NRAS proto-oncogene |
| pipeline_tag: splice-site |
| sequence_type: 5' UTR |
| task: splice-site |
| text: GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUCAUGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA |
| - example_title: amyloid beta precursor protein |
| pipeline_tag: splice-site |
| sequence_type: 5' UTR |
| task: splice-site |
| text: GUCAGUUUCCUCGGCAGCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG |
| - example_title: RUNX family transcription factor 1 |
| pipeline_tag: splice-site |
| sequence_type: 5' UTR |
| task: splice-site |
| text: ACUUCUUUGGGCCUCAUAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG |
| - example_title: fragile X messenger ribonucleoprotein 1 |
| pipeline_tag: splice-site |
| sequence_type: 5' UTR |
| task: splice-site |
| text: CUCAGUCAGGCGCUCAGCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG |
| - example_title: MYC proto-oncogene |
| pipeline_tag: splice-site |
| sequence_type: 5' UTR |
| task: splice-site |
| text: AACUCGCUGUAGUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG |
| - example_title: activating transcription factor 4 |
| pipeline_tag: splice-site |
| sequence_type: 5' UTR |
| task: splice-site |
| text: CAUUUCUACUUUGCCCGCCCACAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC |
| - example_title: Human GPI protein p137 |
| pipeline_tag: splice-site |
| sequence_type: 3' UTR |
| task: splice-site |
| text: UUUUUAAAAGGAAAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC |
| - example_title: nucleophosmin 1 |
| pipeline_tag: splice-site |
| sequence_type: 3' UTR |
| task: splice-site |
| text: GAAAAUAGUUUAAACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA |
| - example_title: superoxide dismutase 1 |
| pipeline_tag: splice-site |
| sequence_type: 3' UTR |
| task: splice-site |
| text: ACAUUCCCUUGGAUGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA |
| - example_title: hemoglobin subunit alpha 2 |
| pipeline_tag: splice-site |
| sequence_type: 3' UTR |
| task: splice-site |
| text: CUGGAGCCUCGGUAGCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA |
| - example_title: BRAF proto-oncogene |
| pipeline_tag: splice-site |
| sequence_type: 3' UTR |
| task: splice-site |
| text: AACAAAUGAGUGAGAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG |
| - example_title: H3 clustered histone 1 |
| pipeline_tag: splice-site |
| sequence_type: 3' UTR |
| task: splice-site |
| text: UUACUGUGGUCUCUCUGACGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC |
| --- |
| |
| # SpTransformer |
|
|
| Transformer network for predicting tissue-specific splicing from pre-mRNA sequences. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of [SpliceTransformer predicts tissue-specific splicing linked to human diseases](https://doi.org/10.1038/s41467-024-53088-6) by Ningyuan You, et al. |
|
|
| The OFFICIAL repository of SpliceTransformer (SpTransformer) is at [ShenLab-Genomics/SpliceTransformer](https://github.com/ShenLab-Genomics/SpliceTransformer). |
|
|
| > [!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 SpTransformer did not write this model card for this model so this model card has been written by the MultiMolecule team.** |
|
|
| ## Model Details |
|
|
| SpTransformer (SpliceTransformer) is a deep neural network that predicts tissue-specific splicing from primary pre-mRNA sequence. |
| It combines two pretrained SpliceAI-style dilated-residual convolutional feature extractors with a trainable input-projection path; the concatenated features are processed by a Sinkhorn transformer attention block with axial positional embeddings. |
| For each position the network predicts a 3-channel splice-site score (no-splice / acceptor / donor) and a per-position splice-site usage score across 15 human tissues. |
| The model uses a fixed flanking context of 4,000 nucleotides on each side of every predicted position. |
| SpTransformer is typically used to estimate the effect of genetic variants on tissue-specific splicing by scoring reference and alternate sequences and taking the difference. |
| Please refer to the [Training Details](#training-details) section for more information on the training process. |
|
|
| ### Model Specification |
|
|
| | Num Layers | Hidden Size | Num Heads | Intermediate Size | Max Seq Len | Num Parameters (M) | FLOPs (G) | MACs (G) | Context | |
| | ---------- | ----------- | --------- | ----------------- | ----------- | ------------------ | --------- | -------- | ------- | |
| | 8 | 256 | 8 | 1024 | 8192 | 17.07 | 290.72 | 144.65 | 4000 | |
|
|
| ### Links |
|
|
| - **Code**: [multimolecule.sptransformer](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/sptransformer) |
| - **Data**: GTEx human RNA-seq across 15 tissues with gene annotations from GENCODE and multi-species sequence data |
| - **Paper**: [SpliceTransformer predicts tissue-specific splicing linked to human diseases](https://doi.org/10.1038/s41467-024-53088-6) |
| - **Developed by**: Ningyuan You, Chang Liu, Yuxin Gu, Rong Wang, Hanying Jia, Tianyun Zhang, Song Jiang, Jinsong Shi, Ming Chen, Min-Xin Guan, Siqi Sun, Shanshan Pei, Zhihong Liu, Ning Shen |
| - **Model type**: Transformer encoder with windowed-local and Sinkhorn sorted-bucket attention for tissue-specific splicing prediction |
| - **Original Repository**: [ShenLab-Genomics/SpliceTransformer](https://github.com/ShenLab-Genomics/SpliceTransformer) |
|
|
| ## 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 |
|
|
| #### RNA Splicing Site Prediction |
|
|
| You can use this model directly to predict per-nucleotide tissue-specific splicing of a pre-mRNA sequence: |
|
|
| ```python |
| >>> from multimolecule import RnaTokenizer, SpTransformerModel |
| |
| >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/sptransformer") |
| >>> model = SpTransformerModel.from_pretrained("multimolecule/sptransformer") |
| >>> output = model(tokenizer("AGCAGUCAUUAUGGCGAA", return_tensors="pt")["input_ids"]) |
| |
| >>> output.keys() |
| odict_keys(['last_hidden_state', 'logits']) |
| ``` |
|
|
| The `logits` tensor reproduces the original SpTransformer output: a 3-channel splice-site score (no-splice / acceptor / donor) and a per-tissue (15 tissues) splice-site usage score for each position. |
|
|
| ### Downstream Use |
|
|
| #### Token Prediction |
|
|
| You can fine-tune SpTransformer for per-nucleotide tissue-specific splicing regression with [`SpTransformerForTokenPrediction`][multimolecule.models.SpTransformerForTokenPrediction], which adds a shared token prediction head on top of the backbone. |
|
|
| ### Interface |
|
|
| - **Input length**: variable pre-mRNA sequence |
| - **Flanking context**: fixed 4,000 nt on each side of every predicted position |
| - **Padding**: ends padded with `N` |
| - **Output**: per-position 3-channel splice-site score (`no-splice` / `acceptor` / `donor`) + per-tissue (15 tissues) splice-site usage score |
|
|
| ## Training Details |
|
|
| SpTransformer was trained to predict tissue-specific splicing from primary pre-mRNA sequence. |
|
|
| ### Training Data |
|
|
| SpTransformer was trained on splicing measurements derived from RNA-seq data across 15 human tissues, using gene annotations from [GENCODE](https://multimolecule.danling.org/datasets/gencode), together with multi-species sequence data. |
| The two convolutional feature extractors were pre-trained as SpliceAI-style splice-site predictors and remain trainable submodules for downstream fine-tuning. |
| For each predicted nucleotide, a sequence window centered on that nucleotide was used, with the flanking context padded with `N` (unknown nucleotide) when near transcript ends. |
|
|
| ### Training Procedure |
|
|
| #### Pre-training |
|
|
| The model was trained to minimize a combination of cross-entropy loss over splice-site classification and a regression loss over per-tissue splice-site usage, comparing predictions against measurements derived from RNA-seq. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{You2024, |
| author = {You, Ningyuan and Liu, Chang and Gu, Yuxin and Wang, Rong and Jia, Hanying and Zhang, Tianyun and Jiang, Song and Shi, Jinsong and Chen, Ming and Guan, Min-Xin and Sun, Siqi and Pei, Shanshan and Liu, Zhihong and Shen, Ning}, |
| title = {{SpliceTransformer predicts tissue-specific splicing linked to human diseases}}, |
| journal = {Nature Communications}, |
| year = {2024}, |
| volume = {15}, |
| number = {1}, |
| pages = {9129}, |
| month = {oct}, |
| doi = {10.1038/s41467-024-53088-6}, |
| issn = {2041-1723}, |
| url = {https://doi.org/10.1038/s41467-024-53088-6} |
| } |
| ``` |
|
|
| > [!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 [SpliceTransformer paper](https://doi.org/10.1038/s41467-024-53088-6) 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 |
| ``` |
