multimolecule / spliceai

SpliceAI

Convolution neural network trained to predict RNA splicing sites (splice acceptors and donors) using pre-mRNA sequences annotated by GENCODE and novel splice junctions identified from RNA-seq data in the GTEx cohort.

Disclaimer

This is an UNOFFICIAL implementation of the Predicting Splicing from Primary Sequence with Deep Learning by Kishore Jaganathan, Sofia Kyriazopoulou Panagiotopoulou and Jeremy F. McRae.

The OFFICIAL repository of SpliceAI is at Illumina/SpliceAI .

The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.

The team releasing SpliceAI did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

SpliceAI is a Convolutional neural network trained to predict RNA splicing sites on a large corpus of pre-mRNA sequences. Please refer to the Training Details section for more information on the training process.

Model Specification

Links

• Code : multimolecule.spliceai
• Weights : multimolecule/spliceai
• Paper : Predicting Splicing from Primary Sequence with Deep Learning
• Developed by : Kishore Jaganathan, Sofia Kyriazopoulou Panagiotopoulou, Jeremy F. McRae, Siavash Fazel Darbandi, David Knowles, Yang I. Li, Jack A. Kosmicki, Juan Arbelaez, Wenwu Cui, Grace B. Schwartz, Eric D. Chow, Efstathios Kanterakis, Hong Gao, Amirali Kia, Serafim Batzoglou, Stephan J. Sanders, Kyle Kai-How Farh
• Original Repository : Illumina/SpliceAI

Usage

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

pip install multimolecule

Direct Use

You can use this model directly to predict the splicing sites of an RNA sequence:

>>> from multimolecule import RnaTokenizer, SpliceAiModel

>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/spliceai")
>>> model = SpliceAiModel.from_pretrained("multimolecule/spliceai")
>>> output = model(tokenizer("agcagucauuauggcgaa", return_tensors="pt")["input_ids"])

>>> output.keys()
odict_keys(['logits'])

>>> output.logits.squeeze()
tensor([[ 8.5123, -4.9607, -7.6787],
        [ 8.6559, -4.4936, -8.6357],
        [ 5.8514, -1.9375, -6.8030],
        [ 7.3739, -5.3444, -5.2559],
        [ 8.6336, -5.3187, -7.5741],
        [ 6.1947, -1.5497, -7.6286],
        [ 9.0482, -6.1002, -7.1229],
        [ 7.9647, -5.6973, -6.5327],
        [ 8.8795, -6.3714, -7.0204],
        [ 7.9459, -5.4744, -6.0865],
        [ 8.4272, -5.2556, -7.9027],
        [ 7.7523, -5.8517, -6.9109],
        [ 7.3027, -4.6946, -5.9420],
        [ 8.1432, -4.3085, -7.7892],
        [ 7.9060, -4.9454, -7.0091],
        [ 8.9770, -5.3971, -7.3313],
        [ 8.4292, -5.7455, -6.7811],
        [ 8.2709, -6.1388, -6.6784]], grad_fn=<SqueezeBackward0>)

Citation

BibTeX :

@article{jaganathan2019the,
  abstract  = {The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9\%-11\% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.},
  author    = {Jaganathan, Kishore and Kyriazopoulou Panagiotopoulou, Sofia and McRae, Jeremy F and Darbandi, Siavash Fazel and Knowles, David and Li, Yang I and Kosmicki, Jack A and Arbelaez, Juan and Cui, Wenwu and Schwartz, Grace B and Chow, Eric D and Kanterakis, Efstathios and Gao, Hong and Kia, Amirali and Batzoglou, Serafim and Sanders, Stephan J and Farh, Kyle Kai-How},
  copyright = {http://www.elsevier.com/open-access/userlicense/1.0/},
  journal   = {Cell},
  keywords  = {artificial intelligence; deep learning; genetics; splicing},
  language  = {en},
  month     = jan,
  number    = 3,
  pages     = {535--548.e24},
  publisher = {Elsevier BV},
  title     = {Predicting splicing from primary sequence with deep learning},
  volume    = 176,
  year      = 2019
}

Contact

Please use GitHub issues of MultiMolecule for any questions or comments on the model card.

Please contact the authors of the SpliceAI paper for questions or comments on the paper/model.

License

This model is licensed under the AGPL-3.0 License and the CC-BY-NC-4.0 License .

SPDX-License-Identifier: AGPL-3.0-or-later AND CC-BY-NC-4.0