datasets:
- multimolecule/gencode
language: rna
library_name: multimolecule
license: agpl-3.0
mask_token: <mask>
pipeline_tag: fill-mask
tags:
- Biology
- RNA
- 3' UTR
widget:
- example_title: Human GPI protein p137
mask_index: 17
mask_index_1based: 18
masked_char: U
output:
- label: AACCA
score: 0.923713
- label: AAGCA
score: 0.053575
- label: AAGAA
score: 0.020241
- label: AAUCA
score: 0.000717
- label: AAACA
score: 0.000538
pipeline_tag: fill-mask
sequence_type: 3UTR
source_tag: 3' UTR
task: fill-mask
text: UUUUUAAAAGGAAAAGA<mask>ACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUU
- example_title: microRNA 21
mask_index: 13
mask_index_1based: 14
masked_char: U
output:
- label: AGAUG
score: 0.99984
- label: AGACU
score: 0.000044
- label: AGACG
score: 0.000027
- label: AGACA
score: 0.000025
- label: AGACC
score: 0.00001
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: UAGCUUAUCAGAC<mask>GAUGUUGA
- example_title: microRNA 146a
mask_index: 11
mask_index_1based: 12
masked_char: U
output:
- label: UGACA
score: 0.754348
- label: UGCCA
score: 0.213237
- label: UGAAA
score: 0.015207
- label: UGAGA
score: 0.00541
- label: UGGCA
score: 0.003168
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: UGAGAACUGAA<mask>UCCAUGGGUU
- example_title: microRNA 155
mask_index: 10
mask_index_1based: 11
masked_char: U
output:
- label: CUAUG
score: 0.607864
- label: CUGUG
score: 0.285921
- label: CUAAG
score: 0.090377
- label: CUAAA
score: 0.002207
- label: CAGUG
score: 0.001669
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: UUAAUGCUAA<mask>CGUGAUAGGGGUU
- example_title: metastasis associated lung adenocarcinoma transcript 1
mask_index: 27
mask_index_1based: 28
masked_char: U
output:
- label: GGGUG
score: 0.770874
- label: GGCUG
score: 0.164034
- label: GGGCG
score: 0.045813
- label: GGAUG
score: 0.003769
- label: GGGCU
score: 0.003184
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: AGGCAUUGAGGCAGCCAGCGCAGGGGC<mask>UCUGCUGAGGGGGCAGGCGGAGCUUGAGGAAA
- example_title: Pvt1 oncogene
mask_index: 10
mask_index_1based: 11
masked_char: U
output:
- label: CUCGG
score: 0.999209
- label: CCCGG
score: 0.000742
- label: GCCGG
score: 0.000022
- label: CGCGG
score: 0.00001
- label: CUCAG
score: 0.000007
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: CCCGCGCUCC<mask>CCGGGCAGAGCGCGUGUGGCGGCCGAGCACAUGGGCCCGCGGGCCGGGC
- example_title: telomerase RNA component
mask_index: 13
mask_index_1based: 14
masked_char: U
output:
- label: AGGGC
score: 0.99999
- label: GGGGC
score: 0.000007
- label: UGGGC
score: 0.000001
- label: AGGGG
score: 0.000001
- label: AGGGU
score: 0.000001
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: GGGUUGCGGAGGG<mask>GGGCCUGGGAGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAG
- example_title: vault RNA 2-1
mask_index: 10
mask_index_1based: 11
masked_char: U
output:
- label: GGAGC
score: 0.999994
- label: GGAGG
score: 0.000002
- label: GGAGA
score: 0.000001
- label: GGAGU
score: 0.000001
- label: GAAGC
score: 0
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: >-
CGGGUCGGAG<mask>UAGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA
- example_title: brain cytoplasmic RNA 1
mask_index: 12
mask_index_1based: 13
masked_char: U
output:
- label: GGGCU
score: 0.996062
- label: UGGCU
score: 0.002014
- label: CGGCU
score: 0.001811
- label: AGGCU
score: 0.00006
- label: GCGCU
score: 0.000027
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: >-
GGCCGGGCGCGG<mask>GGCUCACGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU
- example_title: HIV-1 TAR-WT
mask_index: 11
mask_index_1based: 12
masked_char: U
output:
- label: CUGGA
score: 0.978694
- label: CUAGA
score: 0.020178
- label: CUGGG
score: 0.000159
- label: CUGGC
score: 0.000119
- label: UUAGA
score: 0.000036
pipeline_tag: fill-mask
sequence_type: ncRNA
source_tag: RNA
task: fill-mask
text: GGUCUCUCUGG<mask>UAGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC
- example_title: prion protein (Kanno blood group)
mask_index: 10
mask_index_1based: 11
masked_char: U
output:
- label: AAGGC
score: 0.662737
- label: AUGGC
score: 0.173229
- label: AACGC
score: 0.139515
- label: AGGGC
score: 0.006084
- label: UUGGC
score: 0.004228
pipeline_tag: fill-mask
sequence_type: mRNA
source_tag: RNA
task: fill-mask
text: AUGGCGAACC<mask>UGGCUGCUGGAUGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC
- example_title: interleukin 10
mask_index: 16
mask_index_1based: 17
masked_char: U
output:
- label: GCCUC
score: 0.500996
- label: GCAUC
score: 0.32872
- label: GCACC
score: 0.074776
- label: GGCUC
score: 0.066717
- label: GCAGC
score: 0.007106
pipeline_tag: fill-mask
sequence_type: mRNA
source_tag: RNA
task: fill-mask
text: AUGCACAGCUCAGCAC<mask>GCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC
- example_title: Zaire ebolavirus
mask_index: 13
mask_index_1based: 14
masked_char: U
output:
- label: ACAGU
score: 0.478941
- label: ACUGU
score: 0.471011
- label: AUUGU
score: 0.030197
- label: ACAAU
score: 0.003791
- label: ACACU
score: 0.003351
pipeline_tag: fill-mask
sequence_type: mRNA
source_tag: RNA
task: fill-mask
text: >-
AAUGUUCAAACAC<mask>UUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU
- example_title: SARS coronavirus
mask_index: 10
mask_index_1based: 11
masked_char: U
output:
- label: AUUUA
score: 0.999965
- label: ACUUA
score: 0.000028
- label: AUUUU
score: 0.000003
- label: AUUUC
score: 0.000001
- label: AUUUG
score: 0.000001
pipeline_tag: fill-mask
sequence_type: mRNA
source_tag: RNA
task: fill-mask
text: >-
AUGUUUAUUU<mask>CUUAUUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU
3UTRBERT
Pre-trained model on 3’ untranslated region (3’UTR) using a masked language modeling (MLM) objective.
Disclaimer
This is an UNOFFICIAL implementation of the Deciphering 3’ UTR mediated gene regulation using interpretable deep representation learning by Yuning Yang, Gen Li, et al.
The OFFICIAL repository of 3UTRBERT is at yangyn533/3UTRBERT.
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-3mer | 12 | 768 | 12 | 3072 | 86.14 | 22.36 | 11.17 | 512 |
| 3UTRBERT-4mer | 86.53 | |||||||
| 3UTRBERT-5mer | 88.45 | |||||||
| 3UTRBERT-6mer | 98.05 |
Links
- Code: multimolecule.utrbert
- Data: multimolecule/gencode-human
- Paper: Deciphering 3’ UTR mediated gene regulation using interpretable deep representation learning
- Developed by: Yuning Yang, Gen Li, Kuan Pang, Wuxinhao Cao, Xiangtao Li, Zhaolei Zhang
- Model type: BERT - FlashAttention
- Original Repository: yangyn533/3UTRBERT
Usage
The model file depends on the multimolecule library. You can install it using pip:
pip install multimolecule
Direct Use
Masked Language Modeling
Default transformers pipeline does not support K-mer tokenization.
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/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:
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
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, 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
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, 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
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, 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:
- 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.
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}
}
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 is licensed under the AGPL-3.0 License.
SPDX-License-Identifier: AGPL-3.0-or-later