RNABERT

Pre-trained model on non-coding RNA (ncRNA) using masked language modeling (MLM) and structural alignment learning (SAL) objectives.

Disclaimer

This is an UNOFFICIAL implementation of the Informative RNA-base embedding for functional RNA clustering and structural alignment by Manato Akiyama and Yasubumi Sakakibara.

The OFFICIAL repository of RNABERT is at mana438/RNABERT.

!!! Bug "Reproducibility"

The MultiMolecule team is aware of a potential risk in reproducing the results of RNABERT.

The original implementation of RNABERT does not prepend `<cls>` and append `<sep>` tokens to the input sequence.
This may lead to unexpected results when using the model.

!!! Success "Reproducibility"

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

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

Model Details

RNABERT 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.

Model Specification

Num Layers	Hidden Size	Num Heads	Intermediate Size	Num Parameters (M)	FLOPs (G)	MACs (G)	Max Num Tokens
6	120	12	40	0.48	0.15	0.08	440

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 with a pipeline for masked language modeling:

>>> import multimolecule  # you must import multimolecule to register models
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='multimolecule/rnabert')
>>> unmasker("uagc<mask>uaucagacugauguuga")

[{'score': 0.038491372019052505,
  'token': 5,
  'token_str': '<null>',
  'sequence': 'U A G C U A U C A G A C U G A U G U U G A'},
 {'score': 0.03848646208643913,
  'token': 23,
  'token_str': '.',
  'sequence': 'U A G C. U A U C A G A C U G A U G U U G A'},
 {'score': 0.03846566751599312,
  'token': 0,
  'token_str': '<pad>',
  'sequence': 'U A G C U A U C A G A C U G A U G U U G A'},
 {'score': 0.03846472129225731,
  'token': 9,
  'token_str': 'U',
  'sequence': 'U A G C U U A U C A G A C U G A U G U U G A'},
 {'score': 0.03846454620361328,
  'token': 19,
  'token_str': 'W',
  'sequence': 'U A G C W U A U C A G A C U G A U G U U G A'}]

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, RnaBertModel


tokenizer = RnaTokenizer.from_pretrained('multimolecule/rnabert')
model = RnaBertModel.from_pretrained('multimolecule/rnabert')

text = "UAGCUUAUCAGACUGAUGUUGA"
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, RnaBertForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained('multimolecule/rnabert')
model = RnaBertForSequencePrediction.from_pretrained('multimolecule/rnabert')

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

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

Nucleotide 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 nucleotide 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, RnaBertForNucleotidePrediction


tokenizer = RnaTokenizer.from_pretrained('multimolecule/rnabert')
model = RnaBertForNucleotidePrediction.from_pretrained('multimolecule/rnabert')

text = "UAGCUUAUCAGACUGAUGUUGA"
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, RnaBertForContactPrediction


tokenizer = RnaTokenizer.from_pretrained('multimolecule/rnabert')
model = RnaBertForContactPrediction.from_pretrained('multimolecule/rnabert')

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

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

Training Details

RNABERT has two pre-training objectives: masked language modeling (MLM) and structural alignment learning (SAL).

Masked Language Modeling (MLM): 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.
Structural Alignment Learning (SAL): the model learns to predict the structural alignment of two RNA sequences. The model is trained to predict the alignment score of two RNA sequences using the Needleman-Wunsch algorithm.

Training Data

The RNABERT model was pre-trained on RNAcentral. RNAcentral is a comprehensive database of non-coding RNA sequences from a wide range of species. It combines 47 different databases, adding up to around 27 million RNA sequences in total. RNABERT used a subset of 76, 237 human ncRNA sequences from RNAcentral for pre-training.

RNABERT preprocessed all tokens by replacing "U"s with "T"s.

Note that during model conversions, "T" is replaced with "U". [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

RNABERT preprocess the dataset by applying 10 different mask patterns to the 72, 237 human ncRNA sequences. The final dataset contains 722, 370 sequences. The masking procedure is similar to the one used in BERT:

15% of the tokens are masked.
In 80% of the cases, the masked tokens are replaced by <mask>.
In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
In the 10% remaining cases, the masked tokens are left as is.

PreTraining

The model was trained on 1 NVIDIA V100 GPU.

Citation

BibTeX:

@article{akiyama2022informative,
    author = {Akiyama, Manato and Sakakibara, Yasubumi},
    title = "{Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning}",
    journal = {NAR Genomics and Bioinformatics},
    volume = {4},
    number = {1},
    pages = {lqac012},
    year = {2022},
    month = {02},
    abstract = "{Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-training algorithm for the effective embedding of RNA bases to acquire semantically rich representations and apply this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-training algorithm to embed the four bases of RNA in a position-dependent manner using a large number of RNA sequences from various RNA families, a context-sensitive embedding representation is obtained. As a result, not only base information but also secondary structure and context information of RNA sequences are embedded for each base. We call this ‘informative base embedding’ and use it to achieve accuracies superior to those of existing state-of-the-art methods on RNA structural alignment and RNA family clustering tasks. Furthermore, upon performing RNA sequence alignment by combining this informative base embedding with a simple Needleman–Wunsch alignment algorithm, we succeed in calculating structural alignments with a time complexity of O(n2) instead of the O(n6) time complexity of the naive implementation of Sankoff-style algorithm for input RNA sequence of length n.}",
    issn = {2631-9268},
    doi = {10.1093/nargab/lqac012},
    url = {https://doi.org/10.1093/nargab/lqac012},
    eprint = {https://academic.oup.com/nargab/article-pdf/4/1/lqac012/42577168/lqac012.pdf},
}

Contact

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

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

License

This model is licensed under the AGPL-3.0 License.

SPDX-License-Identifier: AGPL-3.0-or-later

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