SpliceBERT

Pre-trained model on messenger RNA precursor (pre-mRNA) using a masked language modeling (MLM) objective.

Disclaimer

This is an UNOFFICIAL implementation of the Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction by Ken Chen, et al.

The OFFICIAL repository of SpliceBERT is at chenkenbio/SpliceBERT.

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

Model Details

SpliceBERT is a bert-style model pre-trained on a large corpus of messenger RNA precursor sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the Training Details section for more information on the training process.

Variants

• multimolecule/splicebert: The SpliceBERT model.
• multimolecule/splicebert.510: The intermediate SpliceBERT model.
• multimolecule/splicebert-human.510: The intermediate SpliceBERT model pre-trained on human data only.

Model Specification

Links

• Code: multimolecule.splicebert
• Data: UCSC Genome Browser
• Paper: Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction
• Developed by: Ken Chen, Yue Zhou, Maolin Ding, Yu Wang, Zhixiang Ren, Yuedong Yang
• Model type: BERT
• Original Repository: chenkenbio/SpliceBERT

Usage

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

pip install multimolecule

Direct Use

Masked Language Modeling

You can use this model directly with a pipeline for masked language modeling:

import multimolecule  # you must import multimolecule to register models
from transformers import pipeline

predictor = pipeline("fill-mask", model="multimolecule/splicebert")
output = predictor("gguc<mask>cucugguuagaccagaucugagccu")

Downstream Use

Extract Features

Here is how to use this model to get the features of a given sequence in PyTorch:

from multimolecule import RnaTokenizer, SpliceBertModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertModel.from_pretrained("multimolecule/splicebert")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")

output = model(**input)

Sequence Classification / Regression

[!NOTE] This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.

Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:

import torch
from multimolecule import RnaTokenizer, SpliceBertForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertForSequencePrediction.from_pretrained("multimolecule/splicebert")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])

output = model(**input, labels=label)

Token Classification / Regression

[!NOTE] This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.

Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:

import torch
from multimolecule import RnaTokenizer, SpliceBertForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertForTokenPrediction.from_pretrained("multimolecule/splicebert")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))

output = model(**input, labels=label)

Contact Classification / Regression

[!NOTE] This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.

Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:

import torch
from multimolecule import RnaTokenizer, SpliceBertForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/splicebert")
model = SpliceBertForContactPrediction.from_pretrained("multimolecule/splicebert")

text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))

output = model(**input, labels=label)

Training Details

SpliceBERT used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.

Training Data

The SpliceBERT model was pre-trained on messenger RNA precursor sequences from UCSC Genome Browser. UCSC Genome Browser provides visualization, analysis, and download of comprehensive vertebrate genome data with aligned annotation tracks (known genes, predicted genes, ESTs, mRNAs, CpG islands, etc.).

SpliceBERT collected reference genomes and gene annotations from the UCSC Genome Browser for 72 vertebrate species. It applied bedtools getfasta to extract pre-mRNA sequences from the reference genomes based on the gene annotations. The pre-mRNA sequences are then used to pre-train SpliceBERT. The pre-training data contains 2 million pre-mRNA sequences with a total length of 65 billion nucleotides.

Note [RnaTokenizer][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing replace_T_with_U=False.

Training Procedure

Preprocessing

SpliceBERT used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:

• Mask rate: 15%
• Replacement: <mask> for 80% of masked tokens
• Replacement: random token for 10% of masked tokens
• Replacement: unchanged token for 10% of masked tokens

Pre-training

The model was trained on 8 NVIDIA V100 GPUs.

• Optimizer: AdamW
• Learning rate: 1e-4
• Learning rate scheduler: ReduceLROnPlateau(patience=3)

SpliceBERT trained model in a two-stage training process:

1. Pre-train with sequences of a fixed length of 510 nucleotides.
2. Pre-train with sequences of a variable length between 64 and 1024 nucleotides.

The intermediate model after the first stage is available as multimolecule/splicebert.510.

SpliceBERT also pre-trained a model on human data only to validate the contribution of multi-species pre-training. The intermediate model after the first stage is available as multimolecule/splicebert-human.510.

Citation

@article {chen2023self,
	author = {Chen, Ken and Zhou, Yue and Ding, Maolin and Wang, Yu and Ren, Zhixiang and Yang, Yuedong},
	title = {Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction},
	elocation-id = {2023.01.31.526427},
	year = {2023},
	doi = {10.1101/2023.01.31.526427},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {RNA splicing is an important post-transcriptional process of gene expression in eukaryotic cells. Predicting RNA splicing from primary sequences can facilitate the interpretation of genomic variants. In this study, we developed a novel self-supervised pre-trained language model, SpliceBERT, to improve sequence-based RNA splicing prediction. Pre-training on pre-mRNA sequences from vertebrates enables SpliceBERT to capture evolutionary conservation information and characterize the unique property of splice sites. SpliceBERT also improves zero-shot prediction of variant effects on splicing by considering sequence context information, and achieves superior performance for predicting branchpoint in the human genome and splice sites across species. Our study highlighted the importance of pre-training genomic language models on a diverse range of species and suggested that pre-trained language models were promising for deciphering the sequence logic of RNA splicing.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2023/05/09/2023.01.31.526427},
	eprint = {https://www.biorxiv.org/content/early/2023/05/09/2023.01.31.526427.full.pdf},
	journal = {bioRxiv}
}

[!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 SpliceBERT 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