xenusia 0.0.2

A predictor of DNA-binding proteins in archaea

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About Xenusia

Xenusia is a Ppredictor of DNA-binding proteins in archaea.

If you use Xenusia in your research, please consider citing:

Installation

Package installation should only take a few minutes with any of these methods (pip, source).

Installing Xenusia with pip:

We suggest to create a local conda environment where to install xenusia. it can be done with:

conda create -n xenusia

and activated with

conda activate xenusia

or

source activate xenusia

We also suggest to install pytorch separately following the instructions from https://pytorch.org/get-started/locally/

pip install xenusia

The procedure will install xenusia in your computer.

Installing Xenusia from source:

If you want to install Xenusia from this repository, you need to install the dependencies first. First, install PyTorch separately following the instructions from https://pytorch.org/get-started/locally/.

Then install the other required libraries:

pip install numpy scikit-learn requests

Finally, you can clone the repository with the following command:

git clone https://github.com/grogdrinker/xenusia/

Usage

the pip installation will install a script called xenusia_standalone that is directly usable from command line (at least on linux and mac. Most probably on windows as well if you use a conda environemnt).

Using the standalone

The script can take a fasta file or a sequence as input and provide a prediction as output

xenusia_standalone AWESAMEPRTEINSEQENCEASINPT

or, for multiple sequences, do

xenusia_standalone fastaFile.fasta

To write the output in a file, do

xenusia_standalone fastaFile.fasta -o outputFilePath

Using Xenusia into a python script

Xenusia can be imported as a python module

from xenusia.run_prediction import predict
proteinSeq1 = "ASDASDASDASDASDASDDDDASD"
proteinSeq2 = "ASDADDDDDDDDDDDDDASDASDDDDASD"
proteinSeq2 = "ASDADFFFFFFFFFDDDDDDDDFFFFFFFFFASD"
inputSequences = {"ID1":proteinSeq1,"ID2":proteinSeq2,"ID3":proteinSeq3}

xenusia_output = predict(inputSequences) # which is a dict containig the predictions

Help

For bug reports, features addition and technical questions please contact [e-mail]