bm2-lab / prep

PREP: Prioritizing of RNA Editing-based Peptides

PREP: Prioritizing of RNA Editing-based Peptides

RNA editing is a source of transcriptomic diversity, mainly in non-coding regions, and is altered in cancer. Recent studies demonstrated that A-to-I RNA editing events are manifested at the proteomic level and contribute to protein heterogeneity in cancer. Given somatic RNA-editing mutation as input, PREP identify and evaluate the potential immunogenicity of RNA editing based peptides. Detailed information please refer to citation.

Authors:

Chi Zhou and Qi Liu

Citation:

Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer, Submitted, 2019.

Dependencies

Hardware:

PREP currently tested on x86_64 on ubuntu 16.04.

Required software:

• Python 2.7
• NetMHCpan 4.0
• Variant Effect Predictor (VEP)
• BWA
• STAR
• samtools
• Optitype
• 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
• Pyomo
• tables
• pysam
• future
• multiprocessing
• subprocess
• math
• matplotlib

Installation via Docker

Docker image of PREP is at https://hub.docker.com/r/bm2lab/prep/.

1. Install Docker on your computer and make sure it works.

2. Call docker pull bm2lab/prep which will download the Docker image.

3. Run the image in interactive mode with your dataset:

    docker run -it -v /your/path/to/dataset/:/home/bioworker/dataset bm2lab/prep /bin/bash
   

4. Change directory into /home/bioworker/project/PREP:

    cd /home/bioworker/project/PREP
   

5. Download reference data:

    bash data_download.sh
   

6. Edit config.yaml and fill the proper path of input files.

7. Run the program with follow commands:

    python PREP.py RE -i config.yaml
   

Install from source

1. Install all software and python package listed above.

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

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

3. Change NetMHCpan, vep and vep_cache to your directory in config.yaml. Please refer to user manual for a detailed description.

Usage

To run PREP, first fill file path of you rna-seq FASTQ file in confg.yaml, then use:

python PREP.py RE -i config.yaml

User Manual

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

Contact

Chi Zhou
[email protected]

Qi Liu
[email protected]

Biological and Medical Big data Mining Lab
Tongji University
Shanghai, China.

prep's Issues

stop at Loaded 3099750718 letters in 194 sequences

created fasta file /home/bioworker/dataset/SRR15466855/alignments/SRR15466855.noBlat.vcf_tmp.fa ***
*** [DONE] Duration [0:43:14.671427] ***
*** INFO: [Mon May 6 07:21:18 2024] * * * do blat search for unique reads * * * ***
Loaded 3099750718 letters in 194 sequences

The pipline stop at this step for 5 days, Please!