MXfold2

RNA secondary structure prediction using deep learning with thermodynamic integrations

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)
• cmake (>=3.10) (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 mxfold2-0.1.1-cp38-cp38-macosx_10_15_x86_64.whl

Install from sdist

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

% pip3 install mxfold2-0.1.1.tar.gz

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

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

Comming soon.

References

• Sato, K., Akiyama, M., Sakakibara, Y.: RNA secondary structure prediction using deep learning with thermodynamic integrations, preprint.