Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning is published in Nature Machine Intelligence, which is a Closed Access / Author-Fee journal.

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Disclaimer

This is an UNOFFICIAL implementation of the RNAErnie: An RNA Language Model with Structure-enhanced Representations by Ning Wang, Jiang Bian, Haoyi Xiong, et al.

The OFFICIAL repository of RNAErnie is at CatIIIIIIII/RNAErnie.

[!WARNING] The MultiMolecule team is unable to confirm that the provided model and checkpoints are producing the same intermediate representations as the original implementation. This is because

The proposed method is published in a Closed Access / Author-Fee journal.

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

Model Details

RNAErnie is a bert-style model pre-trained on a large corpus of non-coding RNA 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.

Note that during the conversion process, additional tokens such as [IND] and ncRNA class symbols are removed.

Model Specification

Num Layers	Hidden Size	Num Heads	Intermediate Size	Num Parameters (M)	FLOPs (G)	MACs (G)	Max Num Tokens
12	768	12	3072	86.06	96.88	48.32	512

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:

python

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

predictor = pipeline("fill-mask", model="multimolecule/rnaernie")
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:

python

from multimolecule import RnaTokenizer, RnaErnieModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnaernie")
model = RnaErnieModel.from_pretrained("multimolecule/rnaernie")

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:

python

import torch
from multimolecule import RnaTokenizer, RnaErnieForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnaernie")
model = RnaErnieForSequencePrediction.from_pretrained("multimolecule/rnaernie")

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:

python

import torch
from multimolecule import RnaTokenizer, RnaErnieForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnaernie")
model = RnaErnieForTokenPrediction.from_pretrained("multimolecule/rnaernie")

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:

python

import torch
from multimolecule import RnaTokenizer, RnaErnieForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnaernie")
model = RnaErnieForContactPrediction.from_pretrained("multimolecule/rnaernie")

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

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

Training Details

RNAErnie 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 RNAErnie model was pre-trained on RNAcentral. RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of Expert Databases representing a broad range of organisms and RNA types.

RNAErnie used a subset of RNAcentral for pre-training. The subset contains 23 million sequences. RNAErnie preprocessed all tokens by replacing "T"s with "S"s.

Note that [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

RNAErnie 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

RNAErnie used a special 3-stage training pipeline to pre-train the model, each with a different masking strategy:

Base-level Masking: The masking applies to each nucleotide in the sequence. Subsequence-level Masking: The masking applies to subsequences of 4-8bp in the sequence. Motif-level Masking: The model is trained on motif datasets.

The model was trained on 4 NVIDIA V100 GPUs with 32GiB memories.

Batch size: 50
Steps: 2,580,000
Optimizer: AdamW
Learning rate: 1e-4
Learning rate warm-up: 129,000 steps
Learning rate cool-down: 129,000 steps
Minimum learning rate: 5e-5
Weight decay: 0.01

Citation

Citation information is not available for papers published in Closed Access / Author-Fee journals.

[!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 RNAErnie 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/rnaernie

RNAErnie

Statement

Disclaimer

Model Details

Model Specification

Links

Usage

Direct Use

Masked Language Modeling

Downstream Use

Extract Features

Sequence Classification / Regression

Token Classification / Regression

Contact Classification / Regression

Training Details

Training Data

Training Procedure

Preprocessing

Pre-training

Citation

Contact

License

multimolecule/
rnaernie