semvec/pypi-packages huggingface dataset:
mxfold2 0.1.1
RNA secondary structure prediction using deep neural networks with thermodynamic integrations
License: MIT
Requires Python: >=3.7,<4.0

# 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)
* cmake (>=3.10) (optional)

### Install from wheel

We provide the wheel python packages for several platforms at [the release](https://github.com/keio-bioinformatics/mxfold2/releases). 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](https://github.com/keio-bioinformatics/mxfold2/releases) 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``](https://github.com/keio-bioinformatics/mxfold2/releases/download/v0.1.0/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](https://github.com/keio-bioinformatics/mxfold2/releases/tag/v0.1.0). 

    % 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 integrations,  [preprint](https://doi.org/10.1101/2020.08.10.244442).
sshkp 1.2
Script to execute an SSH command using a password from a KeePass database
License: MIT
Requires Python: >=3.8.10
Keywords: ssh database script password environment-variables keepass sshpass pykeepass sshkp kdbx command cli


[![GitHub release workflow](https://img.shields.io/github/workflow/status/dmotte/sshkp/release?logo=github&label=release&style=flat-square)](https://github.com/dmotte/sshkp/actions)
[![PyPI](https://img.shields.io/pypi/v/sshkp?logo=python&style=flat-square)](https://pypi.org/project/sshkp/)

Script to execute an **SSH command** using a password from a **KeePass database**.

Please refer to the [GitHub repository](https://github.com/dmotte/sshkp) for more information.