hoelzer / EpiDope

Prediction of B-cell epitopes from amino acid sequences using deep neural networks.

EpiDope

Prediction of B-cell epitopes from amino acid sequences using deep neural networks. Supported on Linux and Mac.

Installation

1. Download and install Conda.

2. Create a Conda environment with Python 3.7

   conda create -n epidope python=3.7

3. Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use epidope.

   conda activate epidope

4. Install epidope via conda

   conda install -c flomock -c conda-forge -c pytorch epidope

5. Install other dependencies

   pip install allennlp

Usage

Example

epidope -i /path_to/multifasta.fa -o ./results/ -t 0.8 -e /known/epitopes.txt

Options:

command	what it does
-i, --infile	Multi- or Singe- Fasta file with protein sequences. [required]
-o, --outdir	Specifies output directory. Default = .
--delim	Delimiter char for fasta header. Default = White space
--idpos	Position of gene ID in fasta header. Zero based. Default = 0
-t, --threshold	Threshold for epitope score. Default = 0.818
-l, --slicelen	Length of the sliced predicted epitopes. Default = 15
-s, --slice_shiftsize	Shiftsize of the slices on predited epitopes. Default = 5
-p, --processes	Number of processes used for predictions. Default = #CPU-cores
-e, --epitopes	File containing a list of known epitope sequences for plotting
-n, --nonepitopes	File containing a list of non epitope sequences for plotting
-h, --help	show this message and exit