Prediction of B-cell epitopes from amino acid sequences using deep neural networks. Supported on Linux and Mac.
8 GB RAM should be available. With 8GB even processing protein sequences longer than 6000 amino acids and/or multiple hundreds of sequences shouldn't be problematic.
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Install mamba in your conda base repository
conda install -c conda-forge mamba
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Download the enviroment yml file
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Create new repository and install epidope via mamba
mamba env create -f epidope.yml
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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
Example
epidope -i /path_to/multifasta.fa -o ./results/ -e /known/epitopes.txtOptions:
| 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 |
We also provide a Docker image for EpiDope.
Simply pull and run a ready-to-use image from Dockerhub:
docker run -t --rm -v /path/to/input/files:/in -v /path/to/output:/out \
flomock/epidope:v0.3 -i /in/proteins.fasta -o /out/epidope_results(you need to mount files/folders that you want to access in the Docker via -v)
Or if you want you can build the image yourself locally from the Dockerfile in this repo:
docker build -t epidope .Run as non-root user under linux:
docker run -t --rm -v /path/to/input/files:/in -v /path/to/output:/out -u `id -u $USER`:`id -g $USER` \
flomock/epidope:v0.3 -i /in/proteins.fasta -o /out/epidope_resultsRun docker with a different memory allocation see System requirements (default is 2GB for linux and mac):
(e.g. 8GB)
docker run -t --rm -v -m=8g /path/to/input/files:/in -v /path/to/output:/out \
flomock/epidope:v0.3 -i /in/proteins.fasta -o /out/epidope_resultsIf you are interested, you find most of the code which was used to create this tool under: