RNA secondary structure prediction using deep neural networks with thermodynamic integrations
Project description
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.
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