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A pipeline for Viral genome assembly from NGS data

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RSVrecon - RSV Genome Reconstruction Pipeline

Bioinformatics Pipeline Python R License

Please visit our nextflow implementation if you're familar with image.

Table of Contents

Features

  • Parallel processing with configurable thread usage
  • Supports BWA alignment
  • Generates consensus sequences
  • Creates phylogenetic trees for RSV-A/RSV-B subtypes
  • Produces PDF and HTML reports
  • Quality control metrics including coverage statistics

Installation

1. Clone Repository

Option A: Clone with Git (recommended)

git clone https://github.com/yourusername/RSVrecon.git
cd RSVrecon

Option B: Download ZIP

Download the latest release from Github Unzip the package:

gunzip RSVrecon-main.zip
cd RSVrecon-main

1.1: Download reference database

Download the pre-built reference database and unzip it to a location that you have read/write permission https://github.com/stjudecab/RSVreconPy/releases/download/Pre-release/Reference.zip

2. Set Up Environment

We use conda to manage all dependencies. Please install conda and 'mamba'

A1. Install Conda/Mamba (If you are not on a HPC)

Install Miniconda

Please check conda website for a comprehansive instruction: https://www.anaconda.com/docs/getting-started/miniconda/install

Install Mamba (recommended, it's much faster than conda)

Installing mamba

Once conda is installed, installing mamba with conda:

conda install mamba -c conda-forge

A2. Load module Conda/Mamba (If you are on a HPC)

Most high-performance computing (HPC) systems come with Conda/Mamba preinstalled. To use them: Using our system as an example (please contact your HPC mamager for more details):

module load conda
module load mamba

B. Setup Env for RSVrecon

bash Set_env.sh

Configuration

Example config.yaml:

# Required paths
DATA_DIR: /path/to/input/fastq_files         # Please put all your paired-FASTQ files under this input folder
REFERENCE_DIR: /path/to/reference/sequences  # Please download our pre-built reference, unzip it, then paste the path here. Make sure you have both read and write permission
OUTPUT_DIR: /path/to/output/directory        # please specify a output folder path

# Performance parameters
THREAD_N: 2                     # Threads per sample, for BWA-MEM
MAX_CONCURRENT_JOBS: 10         # Parallel samples to process, notice: THREAD_N * MAX_CONCURRENT_JOBS should < than your number of CPUs

# Analysis parameters
TOOL: BWA                       # Currently only BWA supported
COV_CUTOFF: 50                  # Coverage cutoff threshold

# Optional
RSV_NEXT_PIPE_RES: /path/to/additional/results  # We allow users to compare RSVrecon with RSV-NEXT-PIPE results. Please specify the "consensus" folder of RSV-NEXT-PIPE output for the same batch of data.

Quick Start

1. Download test dataset and prebuilt reference

Download test dataset from here. FastQ files are under "fastqs" folder.

Download the pre-built reference database from here

2. Edit config.yaml with your paths

Here is an example:

# Required paths
DATA_DIR: /path/to/input/fastq_files         # Please put all your paired-FASTQ files under this input folder
REFERENCE_DIR: /path/to/reference/sequences  # Please download our pre-built reference, unzip it, then paste the path here. Make sure you have both read and write permission
OUTPUT_DIR: /path/to/output/directory        # please specify a output folder path

# Performance parameters
THREAD_N: 2                     # Threads per sample, for BWA-MEM
MAX_CONCURRENT_JOBS: 10         # Parallel samples to process, notice: THREAD_N * MAX_CONCURRENT_JOBS should < than your number of CPUs

# Analysis parameters
TOOL: BWA                       # Currently only BWA supported
COV_CUTOFF: 50                  # Coverage cutoff threshold

# Optional
RSV_NEXT_PIPE_RES: /path/to/additional/results  # We allow users to compare RSVrecon with RSV-NEXT-PIPE results. Please specify the "consensus" folder of RSV-NEXT-PIPE output for the same batch of data.

3. Run pipeline:

# export path to your PATH
export PATH=/path/to/your/RSVrecon/folder:$PATH
# activate conda env
conda activate RSVreconEnv
# if you're on your local server
python rsvrecon_pipeline.py config.yaml

# If you're on HPC (using LSF as example)
# number of CPUs requested should >= THREAD_N * MAX_CONCURRENT_JOBS
bsub -n 20 -R "rusage[mem=10001]" -P CAB -J RSV -q priority -cwd $(pwd -P) "python rsvrecon_pipeline.py config.yaml"

Output

Report/
├── Mapping/          # Alignment results
├── log/              # Log files
├── Temp/             # Temporary files
├── Report.csv        # Summary table
├── Sequence_*.fasta  # Consensus sequences
├── Report.pdf        # PDF report
└── Report.html       # HTML report

Dependencies

Managed via RSV_env.yml:

dependencies:
  # R related
  - r-base=4.3
  - r-ggplot2
  - r-biocmanager
  - bioconductor-ggtree=3.10.0
  - bioconductor-treeio
  - r-tidyverse
  - r-devtools
  
  # Python related
  - python=3.10
  - pandas=2.2.2
  - biopython=1.78
  - pyhocon
  - reportlab
  - matplotlib
  - seaborn
  - Pillow
  - pyyaml

  # Bioinformatics tools
  - bioconda::fastp=0.23.4
  - bioconda::igvtools=2.3.93
  - bioconda::kma=1.4.9
  - bioconda::nextclade
  - bioconda::samtools=1.18
  - bioconda::blast=2.14.1
  - bioconda::bwa
  - bioconda::mafft=7.505
  - bioconda::fasttree=2.1.11

Troubleshooting

Common Issues:

  • Environment creation fails → Try conda env create -f RSV_env.yml
  • Pipeline errors → Check log/*.err.log files
  • Memory issues → Reduce MAX_CONCURRENT_JOBS

Citation

Our preprint is on-line at bioRxiv

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A pipeline for Viral genome assembly from NGS data

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