MXfold2

RNA secondary structure prediction using deep learning with thermodynamic integration

Installation

System requirements

• python (>=3.7)
• pytorch (>=1.4)
• C++17 compatible compiler (tested on Apple clang version 12.0.0 and GCC version 7.4.0) (optional)

Install from wheel

We provide the wheel python packages for several platforms at the release. You can download an appropriate package and install it as follows:

% pip3 install mxfold2-0.1.2-cp310-cp310-manylinux_2_17_x86_64.whl

Install from sdist

You can build and install from the source distribution downloaded from the release as follows:

% pip3 install mxfold2-0.1.2.tar.gz

To build MXfold2 from the source distribution, you need a C++17 compatible compiler.

Prediction

You can predict RNA secondary structures of given FASTA-formatted RNA sequences like:

% mxfold2 predict test.fa
>DS4440
GGAUGGAUGUCUGAGCGGUUGAAAGAGUCGGUCUUGAAAACCGAAGUAUUGAUAGGAAUACCGGGGGUUCGAAUCCCUCUCCAUCCG
(((((((........(((((..((((.....))))...)))))...................(((((.......)))))))))))). (24.8)

By default, MXfold2 employs the parameters trained from TrainSetA and TrainSetB (see our paper).

We provide other pre-trained models used in our paper. You can download models-0.1.0.tar.gz and extract the pre-trained models from it as follows:

% tar -zxvf models-0.1.0.tar.gz

Then, you can predict RNA secondary structures of given FASTA-formatted RNA sequences like:

% mxfold2 predict @./models/TrainSetA.conf test.fa
>DS4440
GGAUGGAUGUCUGAGCGGUUGAAAGAGUCGGUCUUGAAAACCGAAGUAUUGAUAGGAAUACCGGGGGUUCGAAUCCCUCUCCAUCCG
(((((((.((....))...........(((((.......))))).(((((......))))).(((((.......)))))))))))). (24.3)

Here, ./models/TrainSetA.conf specifies a lot of parameters including hyper-parameters of DNN models.

Training

MXfold2 can train its parameters from BPSEQ-formatted RNA sequences. You can also download the datasets used in our paper at the release.

% mxfold2 train --model MixC --param model.pth --save-config model.conf data/TrainSetA.lst

You can specify a lot of model's hyper-parameters. See mxfold2 train --help. In this example, the model's hyper-parameters and the trained parameters are saved in model.conf and model.pth, respectively.

Web server

A web server is working at http://www.dna.bio.keio.ac.jp/mxfold2/.

References

• Sato, K., Akiyama, M., Sakakibara, Y.: RNA secondary structure prediction using deep learning with thermodynamic integration. Nat Commun 12, 941 (2021). https://doi.org/10.1038/s41467-021-21194-4