sborf 0.0.3

About sborf

Installation

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

Installing sborf with pip:

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

conda create -n sborf

and activated with

conda activate sborf

or

source activate sborf

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

pip install sborf

The procedure will install sborf in your computer.

Installing sborf from source:

If you want to install sborf 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/sborf/

Usage

the pip installation will install a script called sborf_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

sborf_standalone AWESAMEPRTEINSEQENCEASINPT

or, for multiple sequences, do

sborf_standalone fastaFile.fasta

To write the output in a file, do

sborf_standalone fastaFile.fasta -o outputFilePath

Using sborf into a python script

sborf can be imported as a python module

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

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

Help

For bug reports, features addition and technical questions please contact gabriele.orlando@kuleuven.be