Instructions to use multimolecule/mmsplice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/mmsplice with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/mmsplice") model = AutoModel.from_pretrained("multimolecule/mmsplice") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
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 by Jun Cheng, et al.
The OFFICIAL repository of MMSplice is at gagneurlab/MMSplice_MTSplice.
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 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
- 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
- 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
Usage
The model file depends on the multimolecule library. You can install it using pip:
pip install multimolecule
Direct Use
Module Scores
>>> 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
>>> 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/Uids to the four upstream channels and mapsN, padding, special, and unknown tokens to all-zero columns - Output (reference-only call,
input_ids/inputs_embeds): per-module score vectorlogitsof shape(batch_size, 5)
Variant Effect
- Reference + alternative call (also pass
alternative_input_ids/alternative_inputs_embeds): additionally returnsalternative_logitsand per-moduledelta_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
@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}
}
The artifacts distributed in this repository are part of the MultiMolecule project. If MultiMolecule supports your research, please cite the MultiMolecule project as follows:
@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 for any questions or comments on the model card.
Please contact the authors of the MMSplice paper for questions or comments on the paper/model.
License
This model implementation is licensed under the GNU Affero General Public License.
For additional terms and clarifications, please refer to our License FAQ.
SPDX-License-Identifier: AGPL-3.0-or-later