[!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 DeltaSplice did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

DeltaSplice predicts splice-site usage (SSU) and splicing-altering mutation effects from sequence. The model uses a valid-convolution dilated residual encoder and three prediction modules: splice-site usage, reference-informed delta-SSU, and an auxiliary splice-site head. The official package uses the average prediction of five checkpoints for SSU and delta-SSU prediction; MultiMolecule stores the five seed checkpoints of each released data variant as internal ensemble members and returns their average prediction.

Variants

multimolecule/deltasplice: DeltaSplice trained on the multi-species training set used by the upstream default checkpoints.
multimolecule/deltasplice-human: DeltaSplice human-only comparison checkpoint set released by the upstream project.

Model Specification

Variant	Num Layers	Hidden Size	Context	Ensemble Members	Num Parameters (M)	FLOPs (M)	MACs (M)
DeltaSplice	24	64	30000	5	40.376	1642965.72	820284.36
DeltaSplice-Human	24	64	30000	5	40.376	1642965.72	820284.36

(FLOPs and MACs measured on one requested output nucleotide with the default 30 kb padded context.)

Usage

The model file depends on the multimolecule library. You can install it using pip:

pip install multimolecule

Direct Use

Splice-Site Usage

python

>>> from multimolecule import RnaTokenizer
>>> from multimolecule.models.deltasplice import DeltaSpliceModel

>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/deltasplice")
>>> model = DeltaSpliceModel.from_pretrained("multimolecule/deltasplice")
>>> inputs = tokenizer("AGCAGUCAUUAUGGCGAAUCUGGCAAGUA", return_tensors="pt")
>>> output = model(**inputs)
>>> output["probabilities"].shape
torch.Size([1, 30, 3])

Variant Effect

python

>>> from multimolecule import RnaTokenizer
>>> from multimolecule.models.deltasplice import DeltaSpliceModel

>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/deltasplice")
>>> model = DeltaSpliceModel.from_pretrained("multimolecule/deltasplice")
>>> reference = tokenizer("AGCAGUCAUUAUGGCGAAUCUGGCAAGUA", return_tensors="pt")
>>> alternative = tokenizer("AGCAGUCAUUAUGGCUAAUCUGGCAAGUA", return_tensors="pt")
>>> output = model(reference["input_ids"], alternative_input_ids=alternative["input_ids"], use_reference=True)
>>> output["delta"].shape
torch.Size([1, 30, 3])

Interface

Input: RNA sequence tokenized with RnaTokenizer; N is encoded as zero nucleotide channels
Output channels: no_splice, acceptor, donor
Reference-only call: returns per-position splice-site usage probabilities in probabilities
Reference + alternative call: pass the reference sequence as input_ids and the alternate sequence as alternative_input_ids
Reference usage: pass reference_usage with shape (batch_size, sequence_length, 3) or omit it to use the model's own reference usage as the reference signal

Training Details

DeltaSplice was trained to predict splice-site usage from gene sequence and to improve mutation-effect prediction by incorporating reference splice-site usage.

Training Data

The upstream repository describes training from gene_dataset.tsu.txt, which contains splice-site usage in adult brains of eight mammalian species.

Training Procedure

The official release provides five seed checkpoints with the same architecture and data split. MultiMolecule represents these seed checkpoints as internal ensemble members rather than public model variants.

Citation

bibtex

@article{xu2024deltasplice,
  title     = {Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences},
  author    = {Xu, Chencheng and Bao, Suying and Wang, Ye and Li, Wenxing and Chen, Hao and Shen, Yufeng and Jiang, Tao and Zhang, Chaolin},
  journal   = {Genome Research},
  volume    = {34},
  number    = {7},
  pages     = {1052--1065},
  year      = {2024},
  doi       = {10.1101/gr.279044.124}
}

[!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 for any questions or comments on the model card.

Please contact the authors of the DeltaSplice 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

multimolecule/deltasplice

DeltaSplice

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