pTuneos: prioritizing Tumor neoantigen from next-generation sequencing data

pTuneos is the state-of-the-art computational pipeline for identifying personalized tumor neoantigens from next-generation sequencing data. With raw whole-exome sequencing data and/or RNA-seq data, pTuneos calculates five important immunogenicity features to construct a machine learning-based classifier (Pre&RecNeo) to predict and prioritize neoantigens recognized by T cell, followed by an efficient score scheme (RefinedNeo) to ealuate naturally processed, MHC presented and T cell recognized probability of a predicted neoepitope.

Authors:

Chi Zhou and Qi Liu

Citation:

Zhou, C., Wei, Z., Zhang, Z. et al. pTuneos: prioritizing tumor neoantigens from next-generation sequencing data. Genome Med 11, 67 (2019) doi:10.1186/s13073-019-0679-x

Web sever:

TBD

Dependencies

Hardware:

pTuneos currently test on x86_64 on ubuntu 16.04.

Required software:

• Python 2.7
• R 3.2.3
• NetMHCpan 4.0
• Variant Effect Predictor (VEP)
• BWA
• samtools
• Optitype
• Pyclone
• GATK 3.8
• Picard tools
• Java 8
• kallisto
• trimmomatic
• vcftools
• blast
• tabix
• gawk

Required Python package:

• yaml
• XGboost
• biopython
• scikit-learn==0.19.1
• pandas
• numpy
• imblearn
• Pyomo
• tables
• pysam
• PypeR
• multiprocessing
• subprocess
• math
• matplotlib
• collections

Required R package:

• cpynumber
• sequenza 2.1.2
• squash

Installation

Install via Docker

Docker image of pTuneos is at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos. See the user manual for a detailed description usage.

Install from source

1. Install all software listed above.

2. Download or clone the pTuneos repository to your local system:

    git clone https://github.com/bm2-lab/pTuneos.git
   

3. Obtain the reference files from GRCh38. These include cDNA, peptide; please refer to user manual for a detailed description.

Usage

pTuneos has two modes, WES mode and VCF mode.

PairMatchDna mode accepts WES and RNA-seq sequencing data as input, it conduct sequencing quality control, mutation calling, hla typing, expression profiling and neoantigen prediction, filtering, annotation.

VCF mode accepts mutation VCF file, expression profile, copy number profile and tumor cellularity as input, it performs neoantigen prediction, filtering, annotation directly on input file.

You can use these two mode by:

    python pTuneos.py WES -i config_WES.yaml

or

    python pTuneos.py VCF -i config_VCF.yaml

User Manual

For detailed information about usage, input and output files, test examples and data preparation please refer to the pTuneos User Manual

Contact

[email protected] or [email protected] Tongji University, Shanghai, China