virus_assembly

A bash pipeline for the de novo assembly of viral genomes generated via Illumina NGS. Currently handles the following viruses: HIV-1, RSV, RRV and HMPV. This pipeline includes quality checks and reports via FastQc and MultiQC, trimming and mapping of reads via BBTools and de novo assembly using MEGAHIT.

Current version: V1

Table of contents

• System Requirements
• Installation
• Usage
• Example

System Requirements

Hardware requirements

The pipeline can be run on either a standard computer or a HPC server. We tested the pipeline on a standard desktop computer with the following specifications:

RAM: 16+ GB CPU: 4+ cores, @1.90 GHz

Software requirements

OS requirements

The pipeline has been tested on several Linux operating systems including the following systems:

Linux: Ubuntu 16.04, Ubuntu 18.04, Ubuntu 20.04

Dependencies

A conda package manager like Miniconda3. Instructions on how to install:

1. Download the latest miniconda installation script

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

2. Make the miniconda installation script executable

chmod +x Miniconda3-latest-Linux-x86_64.sh

3. Run miniconda installation script

bash ./Miniconda3-latest-Linux-x86_64.sh

Installation

Time to install the pipeline took less than 15 minutes on standard desktop computer.

1. Download the initial environment installation file

wget https://raw.githubusercontent.com/laulambr/virus_assembly/main/scripts/install_env.sh

2. Run the script in the terminal

 bash ./install_env.sh

3. Check if installation worked

 conda activate virus_assembly

Usage

Pipeline: NGS pipeline for viral assembly.
usage: virus_assembly [-h -v -p -q] (-i dir -m value -t value )
(-s string) 
with:
 -h  Show help text
 -v  Version of the pipeline
 -n  Name of RUN.
 -i  Input directory
 -s  Viral species [HIV, RSV, RRV, HMPV]
 -c  Perform clipping of primers
 -q  Perform quality check using fastQC
 -m  Memory
 -t  Number of threads

Quick start

1. Activate environment.

conda activate virus_assembly

2. Head to the directory where you will perform the analysis.
3. Place the raw fastq.gz files in a directory called source.
4. Create a list holding the sample names from you sequencing files called IDs.list and place it in the main directory.
5. Start the pipeline using the following command

virus_assembly -i path/to/main/directory -s VIRUS 

6. When the pipeline has finished, 5 additional folders will have been created:
   • 1_reads: Includes the trimmed and normalised reads.
   • 2_ref_map: Includes a bam file of the trimmed reads against the viral reference genome and a pdf with the qualimap results.
   • 3_contigs: Includes the de novo assembled contigs by megahit for each sample.
   • 4_filter: Includes the high converage contigs generated by megahit (_ *hicov.fasta), filtered and reorientated against the viral reference genome (_ *reoriented.fa)
   • 5_remap: Includes both a fasta file holding the viral contigs for that sample and a bam file of the trimmed reads agains those contigs.

Example

We provide an example analysis using pre-installed test data, which took less than 1 minute on standard desktop.

1. Activate environment.

conda activate virus_assembly

2. Head to the directory with the github clone of this repository and head to the test data folder

cd $CONDA_PREFIX/virus_assembly/test_data

3. Run the following command

virus_assembly -i $CONDA_PREFIX/virus_assembly/test_data -s HIV

4. The pipeline will run, check the newly created 5_read directory for a fasta file containing the new de novo assembled HIV-1 provirus.

Included viral reference genomes

• HIV: K03455.1
• RSV: MH760627; MH760652
• RRV: RRV_ref (Accession pending)
• HMPV: HMPV205; HMPV218 (Accessions pending)