DeepHLApan is a deep learning approach used for predicting high-confidence neoantigens by considering both the presentation possibilities of mutant peptides and the potential immunogenicity of pMHC.

Contact: [email protected]

There are two ways to install the DeepHLApan.

The Installation of Docker (v18.09) can be seen in https://docs.docker.com/

Pull the image of deephlapan from dockerhub:

  sudo docker pull biopharm/deephlapan:v1.1  

run the image in bash mode:

  sudo docker run -it --rm biopharm/deephlapan:v1.1 bash

Linux

perl
python
cuda 9
cudnn 7

Download the latest version of DeepHLApan from https://github.com/jiujiezz/deephlapan

git clone https://github.com/jiujiezz/deephlapan.git

Unzip the source code and go into the directory by using the following command:

tar xvzf deephlapan-*.tar.gz

cd deephlapan

Invoke the setup script:

sudo python setup.py install

Single peptide and HLA:

deephlapan -P LNIMNKLNI -H HLA-A02:01 

List of peptides and HLA alleles in a file:

deephlapan -F [file] -O [output directory]  

DeepHLApan takes csv files as input with head of "Annotation,HLA,peptide" (requisite).
It supports to rank the HLA-peptide pairs if all the mutant peptides belong to one sample.

For example (demo/1.csv):

  Annotation,HLA,peptide
  NCI-3784,HLA-A01:01,MKRFVQWL
  NCI-3784,HLA-A03:01,MKRFVQWL
  NCI-3784,HLA-B07:02,MKRFVQWL
  NCI-3784,HLA-B07:02,MKRFVQWL
  NCI-3784,HLA-C07:02,MKRFVQWL
  NCI-3784,HLA-C07:02,MKRFVQWL
  NCI-3784,HLA-A01:01,KRFVQWLK
  NCI-3784,HLA-A03:01,KRFVQWLK
  NCI-3784,HLA-B07:02,KRFVQWLK
  NCI-3784,HLA-B07:02,KRFVQWLK
  NCI-3784,HLA-C07:02,KRFVQWLK
  NCI-3784,HLA-C07:02,KRFVQWLK

The content in Annotation can be changed as users wanted.

V1.1.1
Improve the prediction speed

V1.1
Add the function of immunogeneicity prediction

V1.0
Test the suitabilty of different RNN variants (GRU,LSTM,BGRU,BLSTM,att-BGRU and att-BLSTM) on the binding prediction and select the best (att-BGRU) one for model construction.