neoFusion: a computational pipeline for gene fusion neoantigen prediction and immunogenic potential assessment
Fusion, which is an important class of somatic mutations, is an ideal source of tumor-derived neoantigens for creating an open reading frame. Given RNAseq sequencing data as input, neoFusion predict and evaluate the immunogenic potential of gene fusion based neoantigen. neoFusion is the first pipeline for predicting and prioritizing fusion neoantigen. Detailed information please refer to citation.
The Landscape of Tumor Fusion Neoantigens: A Pan-Cancer Analysis, iScience, 2019.
- NetMHCpan 4.0
- OptiType
- netchop
- STAR-Fusion
- [ pepmatch ] module of MuPeXI
xgboost
biopython
scikit-learn
pandas
numpy
subprocess
multiprocessing
pickle
neoFusion support two mode, denovo and midway. In midway mode, user do not need download reference. In denovo mode,
please download reference as depicted:
The version of STAR-Fusion in our docker is 1.4.0, for compatibility,
download GRCh38 reference from (https://data.broadinstitute.org/Trinity/CTAT_RESOURCE_LIB/__genome_libs_StarFv1.3/)
please refer to STAR-Fusion for more detail.
All the other needed materials can be found at data directory
Docker image of neoFusion is available at https://hub.docker.com/r/bm2lab/neofusion/.
if you have docker installed, you can pull the image like so:
docker pull bm2lab/neofusion
To install neoFusion, you just need download neoFusion.py and all the described dependencies.
if install from docker:
docker run -it bm2lab/neofusion
python /usr/local/neoFusion.py -h
if install from source:
please edit the software path in neoFusion.py to the right path in your environment.
Two mode are provided, denovo and midway. In denovo mode, you should provide fastq files.
python neoFusion.py denovo --left 1.fq --right 2.fq --hla HLA-A02:01 --genome STAR_Fusion/GRCh38/ctat/
In midway mode, just provide fusion proteins in fa format.
python neoFusion.py midway --fusion example_protein.fa --hla HLA-A02:01
The output file "neoscore.txt" contains all putative neoantigens information.
| Column | Description |
|---|---|
| HLA | HLA type |
| mismatch | mismatch between mtpep and wtpep |
| MTpep | fusion derived candidiate neopeptide |
| MTpep_score | predicted score of mtpep output by netMHCpan |
| MTpep_aff | predicted binding affinity of mtpep output by netMHCpan |
| MTpep_rank | predicted binding affinity percent rank of mtpep output by netMHCpan |
| MTpep_comb | Combined score of binding affinity, proteasomal C terminal cleavage, and TAP transport efficiency |
| WTpep | pepmatch_db_x86_64 extracted normal peptide |
| WTpep_score | predicted score of wtpep output by netMHCpan |
| WTpep_aff | predicted binding affinity of wtpep output by netMHCpan |
| WTpep_rank | predicted binding affinity percent rank of wtpep output by netMHCpan |
| WTpep_comb | Combined score of binding affinity, proteasomal C terminal cleavage, and TAP transport efficiency |
| Hydro_Model | peptide immunogenic potential based on amino acid hydrophobicity |
| R | T cell recognition probability |
| Score | Immunogenicity score of neoantigens |
Zhiting Wei
1632738@tongji.edu.cn
Qi Liu
qiliu@tongji.edu.cn
Biological and Medical Big data Mining Lab
Tongji University
Shanghai, China.
