A neural network that predicts and designs alternative DNA encodings for proteins, aiming to fine-tune their expression
Project description
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
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