multimolecule/mmsplice

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.

[!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 section for more information on the training process.

Model Specification

(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/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

@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:

@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