This model is in a future release of MultiMolecule, and is under development. This model card is not final and will be updated in the future.

RibonanzaNet

Pre-trained model for modeling RNA structure.

Disclaimer

This is an UNOFFICIAL implementation of the Ribonanza: deep learning of RNA structure through dual crowdsourcing by Shujun He, Rui Huang, et al.

The OFFICIAL repository of RibonanzaNet is at Shujun-He/RibonanzaNet.

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

The original implementation of RibonanzaNet applied dropout-residual-norm path twice to the output of the Self-Attention layer.

By default, the MultiMolecule follows the original implementation.

You can set fix_attention_norm=True in the model configuration to apply the dropout-residual-norm path once.

See more at issue #3

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

The original implementation of RibonanzaNet does not apply attention mask correctly.

By default, the MultiMolecule follows the original implementation.

You can set fix_attention_mask=True in the model configuration to apply the correct attention mask.

See more at issue #4, issue #5, and issue #7

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

The original implementation of RibonanzaNet applies dropout in an axis different from the one described in the paper.

By default, the MultiMolecule follows the original implementation.

You can set fix_pairwise_dropout=True in the model configuration to follow the description in the paper.

See more at issue #6

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

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

Model Details

RibonanzaNet 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
33	1280	20	5120	650.88	168.92	84.43	1022

Links

• Code: multimolecule.ribonanzanet
• Weights: multimolecule/ribonanzanet
• Data: RNAcentral
• Paper: Ribonanza: deep learning of RNA structure through dual crowdsourcing
• Developed by: Shujun He, Rui Huang, Jill Townley, Rachael C. Kretsch, Thomas G. Karagianes, David B.T. Cox, Hamish Blair, Dmitry Penzar, Valeriy Vyaltsev, Elizaveta Aristova, Arsenii Zinkevich, Artemy Bakulin, Hoyeol Sohn, Daniel Krstevski, Takaaki Fukui, Fumiya Tatematsu, Yusuke Uchida, Donghoon Jang, Jun Seong Lee, Roger Shieh, Tom Ma, Eduard Martynov, Maxim V. Shugaev, Habib S.T. Bukhari, Kazuki Fujikawa, Kazuki Onodera, Christof Henkel, Shlomo Ron, Jonathan Romano, John J. Nicol, Grace P. Nye, Yuan Wu, Christian Choe, Walter Reade, Eterna participants, Rhiju Das
• Model type: BERT
• Original Repository: Shujun-He/RibonanzaNet

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/ribonanzanet")
>>> unmasker("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, RibonanzaNetModel


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ribonanzanet")
model = RibonanzaNetModel.from_pretrained("multimolecule/ribonanzanet")

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, RibonanzaNetForSequencePrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ribonanzanet")
model = RibonanzaNetForSequencePrediction.from_pretrained("multimolecule/ribonanzanet")

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 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, RibonanzaNetForTokenPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ribonanzanet")
model = RibonanzaNetForTokenPrediction.from_pretrained("multimolecule/ribonanzanet")

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, RibonanzaNetForContactPrediction


tokenizer = RnaTokenizer.from_pretrained("multimolecule/ribonanzanet")
model = RibonanzaNetForContactPrediction.from_pretrained("multimolecule/ribonanzanet")

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

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

Training Details

RibonanzaNet 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 RibonanzaNet model was pre-trained on a cocktail of databases including RNAcentral, Rfam, Ensembl Genome Browser, and Nucleotide. The training data contains 36 million unique ncRNA sequences.

To ensure sequence diversity in each training batch, RibonanzaNet clustered the sequences with MMSeqs2 into 17 million clusters and then sampled each sequence in the batch from a different cluster.

RibonanzaNet 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

RibonanzaNet 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.

PreTraining

The model was trained on 7 NVIDIA A100 GPUs with 80GiB memories.

• Learning rate: 5e-5
• Learning rate scheduler: cosine
• Learning rate warm-up: 2,000 steps
• Learning rate minimum: 1e-5
• Epochs: 6
• Batch Size: 1344
• Dropout: 0.1

Citation

BibTeX:

@article{He2024.02.24.581671,
  author       = {He, Shujun and Huang, Rui and Townley, Jill and Kretsch, Rachael C. and Karagianes, Thomas G. and Cox, David B.T. and Blair, Hamish and Penzar, Dmitry and Vyaltsev, Valeriy and Aristova, Elizaveta and Zinkevich, Arsenii and Bakulin, Artemy and Sohn, Hoyeol and Krstevski, Daniel and Fukui, Takaaki and Tatematsu, Fumiya and Uchida, Yusuke and Jang, Donghoon and Lee, Jun Seong and Shieh, Roger and Ma, Tom and Martynov, Eduard and Shugaev, Maxim V. and Bukhari, Habib S.T. and Fujikawa, Kazuki and Onodera, Kazuki and Henkel, Christof and Ron, Shlomo and Romano, Jonathan and Nicol, John J. and Nye, Grace P. and Wu, Yuan and Choe, Christian and Reade, Walter and Eterna participants and Das, Rhiju},
  title        = {Ribonanza: deep learning of RNA structure through dual crowdsourcing},
  elocation-id = {2024.02.24.581671},
  year         = {2024},
  doi          = {10.1101/2024.02.24.581671},
  publisher    = {Cold Spring Harbor Laboratory},
  abstract     = {Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.Competing Interest StatementStanford University is filing patent applications based on concepts described in this paper. R.D. is a cofounder of Inceptive.},
  url          = {https://www.biorxiv.org/content/early/2024/06/11/2024.02.24.581671},
  eprint       = {https://www.biorxiv.org/content/early/2024/06/11/2024.02.24.581671.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 RibonanzaNet 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