eFold

This repo contains the pytorch code for our paper “Diverse Database and Machine Learning Model to narrow the generalization gap in RNA structure prediction”

[BioRXiv] [Data]

Install

pip install efold

Inference mode

Using the command line

From a sequence:

efold AAACAUGAGGAUUACCCAUGU -o seq.txt
cat seq.txt

AAACAUGAGGAUUACCCAUGU
..(((((.((....)))))))

or a fasta file:

efold --fasta example.fasta

Using different formats:

efold AAACAUGAGGAUUACCCAUGU -bp # base pairs
efold AAACAUGAGGAUUACCCAUGU -db # dotbracket (default)

Output can be .json, .csv or .txt

efold AAACAUGAGGAUUACCCAUGU -o output.csv

Run help:

efold -h

Using python

>>> from efold import inference
>>> inference('AAACAUGAGGAUUACCCAUGU', fmt='dotbracket')
..(((((.((....)))))))

Inference speed

Tested on a AMD EPYC 7272 12 core processor, with 32GB RAM and a RTX3090 GPU

File structure

efold/
    api/    # for inference calls
    core/   # backend 
    models/ # where we define eFold and other models
    resources/
        efold_weights.py # our best model weights
scripts/
    efold_training.py # our training script
    [...]
LICENSE
requirements.txt
pyproject.toml

Data

List of the datasets we used

A breakdown of the data we used is summarized here. All the data is stored on the HuggingFace.

Get the data

You can download our datasets using rouskinHF:

pip install rouskinhf

And in your code, write:

>>> import rouskinhf
>>> data = rouskinhf.get_dataset('ribo500-blast') # look at the dataset names on huggingface

Reproducing our results

Training

A training script is provided to train eFold from scratch.

Testing

A notebook is provided to run eFold inference on the four test sets, compute the F1 score and check the validity of the structures.

Citation

Plain text:

Albéric A. de Lajarte, Yves J. Martin des Taillades, Colin Kalicki, Federico Fuchs Wightman, Justin Aruda, Dragui Salazar, Matthew F. Allan, Casper L’Esperance-Kerckhoff, Alex Kashi, Fabrice Jossinet, Silvi Rouskin. “Diverse Database and Machine Learning Model to narrow the generalization gap in RNA structure prediction”. bioRxiv 2024.01.24.577093; doi: https://doi.org/10.1101/2024.01.24.577093. 2024

BibTex:

@article {Lajarte_Martin_2024,
	title = {Diverse Database and Machine Learning Model to narrow the generalization gap in RNA structure prediction},
	author = {Alb{\'e}ric A. de Lajarte and Yves J. Martin des Taillades and Colin Kalicki and Federico Fuchs Wightman and Justin Aruda and Dragui Salazar and Matthew F. Allan and Casper L{\textquoteright}Esperance-Kerckhoff and Alex Kashi and Fabrice Jossinet and Silvi Rouskin},
	year = {2024},
	doi = {10.1101/2024.01.24.577093},
	URL = {https://www.biorxiv.org/content/early/2024/01/25/2024.01.24.577093},
	journal = {bioRxiv}
}