[!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 3UTRBERT did not write this model card for this model so this model card has been written by the MultiMolecule team.

Model Details

3UTRBERT is a bert-style model pre-trained on a large corpus of 3’ untranslated regions (3’UTRs) 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/utrbert-3mer: The 3UTRBERT model pre-trained on 3-mer data.
multimolecule/utrbert-4mer: The 3UTRBERT model pre-trained on 4-mer data.
multimolecule/utrbert-5mer: The 3UTRBERT model pre-trained on 5-mer data.
multimolecule/utrbert-6mer: The 3UTRBERT model pre-trained on 6-mer data.

Model Specification

Variants	Num Layers	Hidden Size	Num Heads	Intermediate Size	Num Parameters (M)	FLOPs (G)	MACs (G)	Max Num Tokens
3UTRBERT-6mer	12	768	12	3072	98.05	96.86	48.32	512
3UTRBERT-5mer	88.45
3UTRBERT-4mer	86.53
3UTRBERT-3mer	86.14

Usage

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

pip install multimolecule

Direct Use

Masked Language Modeling

[!WARNING] Default transformers pipeline does not support K-mer tokenization.

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/utrbert-3mer")
output = predictor("gguc<mask><mask><mask>cugguuagaccagaucugagccu")[1]

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, UtrBertModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/utrbert-3mer")
model = UtrBertModel.from_pretrained("multimolecule/utrbert-3mer")

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, UtrBertForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/utrbert-3mer")
model = UtrBertForSequencePrediction.from_pretrained("multimolecule/utrbert-3mer")

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, UtrBertForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/utrbert-3mer")
model = UtrBertForTokenPrediction.from_pretrained("multimolecule/utrbert-3mer")

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, UtrBertForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/utrbert-3mer")
model = UtrBertForContactPrediction.from_pretrained("multimolecule/utrbert-3mer")

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

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

Training Details

3UTRBERT 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 3UTRBERT model was pre-trained on human mRNA transcript sequences from GENCODE. GENCODE aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. The GENCODE release 40 used by this work contains 61,544 genes, and 246,624 transcripts.

3UTRBERT collected the human mRNA transcript sequences from GENCODE, including 108,573 unique mRNA transcripts. Only the longest transcript of each gene was used in the pre-training process. 3UTRBERT only used the 3’ untranslated regions (3’UTRs) of the mRNA transcripts for pre-training to avoid codon constrains in the CDS region, and to reduce increased complexity of the entire mRNA transcripts. The average length of the 3’UTRs was 1,227 nucleotides, while the median length was 631 nucleotides. Each 3’UTR sequence was cut to non-overlapping patches of 510 nucleotides. The remaining sequences were padded to the same length.

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

3UTRBERT 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

Since 3UTRBERT used k-mer tokenizer, it masks the entire k-mer instead of individual nucleotides to avoid information leakage.

For example, if the k-mer is 3, the sequence "UAGCGUAU" will be tokenized as ["UAG", "AGC", "GCG", "CGU", "GUA", "UAU"]. If the nucleotide "C" is masked, the adjacent tokens will also be masked, resulting ["UAG", "<mask>", "<mask>", "<mask>", "GUA", "UAU"].

Pre-training

The model was trained on 4 NVIDIA Quadro RTX 6000 GPUs with 24GiB memories.

Batch size: 128
Steps: 200,000
Optimizer: AdamW(β1=0.9, β2=0.98, e=1e-6)
Learning rate: 3e-4
Learning rate scheduler: Linear
Learning rate warm-up: 10,000 steps
Weight decay: 0.01

Citation

bibtex

@article {yang2023deciphering,
	author = {Yang, Yuning and Li, Gen and Pang, Kuan and Cao, Wuxinhao and Li, Xiangtao and Zhang, Zhaolei},
	title = {Deciphering 3{\textquoteright} UTR mediated gene regulation using interpretable deep representation learning},
	elocation-id = {2023.09.08.556883},
	year = {2023},
	doi = {10.1101/2023.09.08.556883},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {The 3{\textquoteright}untranslated regions (3{\textquoteright}UTRs) of messenger RNAs contain many important cis-regulatory elements that are under functional and evolutionary constraints. We hypothesize that these constraints are similar to grammars and syntaxes in human languages and can be modeled by advanced natural language models such as Transformers, which has been very effective in modeling protein sequence and structures. Here we describe 3UTRBERT, which implements an attention-based language model, i.e., Bidirectional Encoder Representations from Transformers (BERT). 3UTRBERT was pre-trained on aggregated 3{\textquoteright}UTR sequences of human mRNAs in a task-agnostic manner; the pre-trained model was then fine-tuned for specific downstream tasks such as predicting RBP binding sites, m6A RNA modification sites, and predicting RNA sub-cellular localizations. Benchmark results showed that 3UTRBERT generally outperformed other contemporary methods in each of these tasks. We also showed that the self-attention mechanism within 3UTRBERT allows direct visualization of the semantic relationship between sequence elements.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2023/09/12/2023.09.08.556883},
	eprint = {https://www.biorxiv.org/content/early/2023/09/12/2023.09.08.556883.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:

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 3UTRBERT 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/utrbert-4mer

3UTRBERT

Disclaimer

Model Details

Variants

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/
utrbert-4mer