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Bacterial SNP calling Workflow

This repository contains a nextflow workflow performing haploid variant calling of whole genome data with medaka from basecalls and a reference file. The workflow can optionally run prokka to annotate the resulting consensus sequence.

The pipeline is currently functional but contains little configuration of minimap2, racon, and medaka beyond setting the number of compute threads to use.


The workflow uses nextflow to manage compute and software resources, as such nextflow will need to be installed before attempting to run the workflow.

The workflow can currently be run using either Docker or conda to provide isolation of the required software. Both methods are automated out-of-the-box provided either docker of conda is installed.

See the sections below for installation of these prerequisites in various scenarios. It is not required to clone or download the git repository in order to run the workflow.

Workflow options

To obtain the workflow, having installed nextflow, users can run:

nextflow run epi2me-labs/wf-hap-snps --help

to see the options for the workflow.

Workflow outputs

The primary outputs of the workflow include:

  • a FASTA consensus sequence scaffolded from a provided reference sequence,
  • a VCF file containing variants in the sample compared to the provided reference,
  • an HTML report document detailing QC metrics and the primary findings of the workflow,
  • (optionally) an annotation of the consensus sequence using prokka.

Supported installations and GridION devices

Installation of the software on a GridION can be performed using the command

sudo apt install ont-nextflow

This will install a java runtime, Nextflow and docker. If docker has not already been configured the command below can be used to provide user access to the docker services. Please logout of your computer after this command has been typed.

sudo usermod -aG docker $USER

Installation on Ubuntu devices

For hardware running Ubuntu the following instructions should suffice to install Nextflow and Docker in order to run the workflow.

  1. Install a Jva runtime environment (JRE):

    sudo apt install default-jre

  2. Download and install Nextflow may be downloaded from

    curl -s | bash

    This will place a nextflow binary in the current working directory, you may wish to move this to a location where it is always accessible, e.g:

    sudo mv nextflow /usr/local/bin

  3. Install docker and add the current user to the docker group to enable access:

    sudo apt install
    sudo usermod -aG docker $USER

Running the workflow

The wf-hap-snps workflow can be controlled by the following parameters. The fastq parameter is the most important parameter: it is required to identify the location of the sequence files to be analysed.


  • fastq specifies a directory path to FASTQ files (required)
  • reference specifies a reference sequence FASTA file (required)
  • out_dir the path for the output (default: output)
  • medaka_model the medaka model name to use (default: r941_min_high_g360)
  • run_prokka specify to run prokka to annotate the consensus sequence (default: false)
  • prokka_opts command-line arguments for prokka (default: )

To run the workflow using Docker containers supply the -profile standard argument to nextflow run:

The command below uses test data available from the github repository It can be obtained with git clone

# run the pipeline with the test data
nextflow run epi2me-labs/wf-hap-snps \
    -w ${OUTPUT}/workspace 
    -profile standard
    --fastq test_data --reference test_data/ref/reference.subseq.fa.gz 
    --threads 4 --out_dir ${OUTPUT}

The output of the pipeline will be found in ./output for the above example. This directory contains the nextflow working directories alongside the two primary outputs of the pipeline: a medaka_consensus.fasta file and a medaka_consensus.vcf file.

Running the workflow with Conda

To run the workflow using conda rather than docker, simply replace

-profile standard 


-profile conda

in the command above.

Configuration and tuning

This section provides some minimal guidance for changing common options, see the Nextflow documentation for further details.

The default settings for the workflow are described in the configuration file nextflow.config found within the git repository. The default configuration defines an executor that will use a specified maximum CPU cores (four at the time of writing) and RAM (eight gigabytes).

If the workflow is being run on a device other than a GridION, the available memory and number of CPUs may be adjusted to the available number of CPU cores. This can be done by creating a file my_config.cfg in the working directory with the following contents:

executor {
    $local {
        cpus = 4
        memory = "8 GB"

and running the workflow providing the -c (config) option, e.g.:

# run the pipeline with custom configuration
nextflow run epi2me-labs/wf-hap-snps \
    -c my_config.cfg \

The contents of the my_config.cfg file will override the contents of the default configuration file. See the Nextflow documentation for more information concerning customized configuration.

Using a fixed conda environment

By default, Nextflow will attempt to create a fresh conda environment for any new analysis (for reasons of reproducibility). This may be undesirable if many analyses are being run. To avoid the situation a fixed conda environment can be used for all analyses by creating a custom config with the following stanza:

profiles {
    // profile using conda environments rather than docker
    // containers
    fixed_conda {
        docker {
            enabled = false
        process {
            withLabel:artic {
                conda = "/path/to/my/conda/environment"
            shell = ['/bin/bash', '-euo', 'pipefail']

and running nextflow by setting the profile to fixed_conda:

nextflow run epi2me-labs/wf-hap-snps \
    -c my_config.cfg \
    -profile fixed_conda \

Updating the workflow

Periodically when running the workflow, users may find that a message is displayed indicating that an update to the workflow is available.

To update the workflow simply run:

nextflow pull epi2me-labs/wf-hap-snps

Building the docker container from source

The docker image used for running the wf-hap-snps workflow is available on dockerhub. The image is built from the Dockerfile present in the git repository. Users wishing to modify and build the image can do so with:


git clone
cd wf-hap-snps

docker build \
    -t ${CONTAINER_TAG} -f Dockerfile \
    --build-arg BASEIMAGE=ontresearch/base-workflow-image:v0.1.0 \

In order to run the workflow with this new image it is required to give nextflow the --wfversion parameter:

nextflow run epi2me-labs/wf-hap-snps \
    --wfversion latest

Useful links