m-mahgoub / nf-core-wgsnano

Nextflow pipeline for analysis of Nanopore Whole Genome Sequencing

wgsnano

Whole Genome Sequencing by Nanopore data analysis

Introduction

nf-core-wgsnano is a bioinformatics best-practice analysis pipeline for Nanopore Whole Genome Sequencing.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible.

Pipeline summary

Pipeline summary

1. Basecalling (Dorado) - with GPU run option.Optional for pod5/fast5 formats.
2. Basecalling QC (PycoQC)
3. Alignment (Dorado with minimap2)
4. Merge all aligned bam files into a single file (samtools)
5. Haplotyping and phased variants calling (PEPPER-Margin-DeepVariant)
6. Methylation calls extraction from bam to bed files (modkit).- Optional step.
7. Depth calculation (mosdepth)
8. MultiQC (MultiQC) for Basecalling (PycoQC) and Depth (mosdepth)

Quick Start

1. Install Nextflow (>=22.10.1)

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (this pipeline can NOT be run with conda)). This requirement is not needed for running the pipeline in WashU RIS cluster.

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run dhslab/nf-core-wgsnano -profile test,YOURPROFILE(S) --outdir <OUTDIR>

4. Start running your own analysis!

   nextflow run dhslab/nf-core-wgsnano --input samplesheet.csv --fasta <FASTA> -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --outdir <OUTDIR>

Usage

Required parameters:

1. Input: samplesheet.csv - This file provides directory/file paths for fast5|pod5|bam reads along with their metadata. It can be specified in a configuration file or supplied directly as a command-line parameter using --input path/to/samplesheet.csv. An example of the samplesheet is available at assets/samplesheet.csv.
2. Reference genome fasta file, either in a configuration file or as --fasta path/to/genome.fasta command line parameter.

Pipeline Execution and Customization Parameters:

Parameters for customizing the workflow sequences and entry points, along with options specifically tailored to the Dorado and PEPPER components within the pipeline. For details read the usage documentaion

Running a pipeline test in LSF cluster (configured to WashU RIS cluster environment)

1) Directly from GitHub:

NXF_HOME=${PWD}/.nextflow LSF_DOCKER_VOLUMES="/storage1/fs1/dspencer/Active:/storage1/fs1/dspencer/Active $HOME:$HOME" bsub -g /dspencer/nextflow -G compute-dspencer -q dspencer -e nextflow_launcher.err -o nextflow_launcher.log -We 2:00 -n 2 -M 12GB -R "select[mem>=16000] span[hosts=1] rusage[mem=16000]" -a "docker(ghcr.io/dhslab/docker-nextflow)" nextflow run dhslab/nf-core-wgsnano -r dev -profile test,ris,dhslab --outdir results

Notice that three profiles are used here:

1. test-> to provide input and fasta paths for the test run
2. ris-> to set general configuration for RIS LSF cluster
3. dhslab-> to set lab-specific cluster configuration

2) Alternatively, clone the repository and run the pipeline from local directory:

git clone https://github.com/dhslab/nf-core-wgsnano.git
cd nf-core-wgsnano/
chmod +x bin/*
LSF_DOCKER_VOLUMES="/storage1/fs1/dspencer/Active:/storage1/fs1/dspencer/Active $HOME:$HOME" bsub -g /dspencer/nextflow -G compute-dspencer -q dspencer -e nextflow_launcher.err -o nextflow_launcher.log -We 2:00 -n 2 -M 12GB -R "select[mem>=16000] span[hosts=1] rusage[mem=16000]" -a "docker(ghcr.io/dhslab/docker-nextflow)" "NXF_HOME=${PWD}/.nextflow ; nextflow run main.nf -profile test,ris,dhslab --outdir results"

Notes:

• The pipeline is developed and optimized to be run in WashU RIS (LSF) HPC, but could be deployed in any HPC environment supported by Nextflow.
• The pipeline does NOT support conda because some of the tools used are not available as conda packages.
• The pipeline can NOT be fully tested in a personal computer as basecalling step is computationally intense even for small test files. For testing/development purposes, the pipeline can be run in stub (dry-run) mode (see below).