MPXV Nanopore sequencing bioinformatics protocol

Preparation

This SOP is purely for command line usage, if you are uncomfortable using the command line for bioinformatics data analysis do not worry! We have an SOP for performing the exact same steps using the Epi2me front end available here: epi2me MPXV bioinformatics.

If you are using a windows PC we recommend that you do any bioinformatics in a WSL2 environment, a tutorial for installing WSL2 is available here: Microsoft WSL Install Tutorial, this will be required before moving to the next steps.

Firstly, you will need Conda >= v23.10.0 (or a lower version with mamba installed), this will handle installation of software that the pipeline needs to run. You can find the appropriate installer for miniforge (a conda distribution) by going to this link: miniforge downloads, if you are using WSL2 you should download the Linux version of the installer.

Once you have downloaded the installer you can install it by opening a terminal session, moving to the path where the file was downloaded, then running it, like this:

bash Miniforge3-Linux-x86_64.sh

You will then be asked to confirm the install location of miniforge, it defaults to the current users home directory (~/miniforge3/) which is fine for the majority of usecases. After, you will be asked to read and agree the end-user license agreement, once you have done so enter yes to agree. Miniforge will then install into the previously specified directory, after which you will be asked if you would like to automatically initialise Conda when starting a new terminal session, most users will want to select yes here.

Once this completes you should have a working Conda install and can move onto the next step.

Creating the environment

First time only, clone the fieldbioinformatics github repository and checkout the 1.4.0-dev branch, once 1.4.0 has been properly released this section will be updated to reflect this:

git clone https://github.com/artic-network/fieldbioinformatics.git
cd fieldbioinformatics
git checkout 1.4.0-dev

conda env create -f environment.yml

This has created a conda environment named artic containing all the software requirements for the fieldbioinformatics pipeline, you can activate this environment like so:

conda activate artic

Finally, to install the pipeline itself, we use pip install while inside the fieldbioinformatics directory:

pip install .

Make a new directory for analysis

Give your analysis directory a meaningful name, e.g.. analysis/run_name

mkdir analysis
cd analysis

mkdir run_name
cd run_name

Activate the ARTIC environment:

All steps in this tutorial should be performed in the artic conda environment (if you have previously activated the environment in the previous steps you will not need to do so again):

conda activate artic

Read filtering

Because ARTIC protocol can generate chimeric reads, we perform length filtering.

This step is performed for each barcode in the run.

We first collect all the FASTQ files into a single file.

To collect and filter the reads for barcode03, we would run:

artic guppyplex --min-length 1500 --max-length 3000 --directory output_directory/barcode03 --prefix run_name

This will perform a quality check. If you are only using “pass” reads you can speed up the process with:

artic guppyplex --skip-quality-check --min-length 1500 --max-length 3000 --directory output_directory/barcode03 --prefix run_name

We use a length filter here of between 1500 and 3000 to remove obviously chimeric reads.

You may need to change these numbers if you are using different length primer schemes. Try the minimum lengths of the amplicons as the minimum, and the maximum length of the amplicons plus 200 as the maximum.

I.e. if your amplicons are 300 base pairs, use –min-length 300 –max-length 500

You will now have a files called: run_name_barcode03.fastq

Run the MinION pipeline (Medaka variant caller)

Fieldbioinformatics supports two variant callers, Clair3 and Medaka, in our testing both appear to perform well but we have far more experience with Medaka and therefore recommend it as the default variant caller, if you wish to try the experimental clair3 workflow, you can add the argument --clair3 to the command below. The --model parameter should be changed to an appropriate clair3 model, information about these can be found in the clair3 repository.

This command will automatically pull primer schemes from the PrimalScheme primerschemes repository based on the --scheme-name, --scheme-version, and --scheme-length arguments, the scheme length arg is optional in most cases since the vast majority of primer schemes are only available in a single amplicon length. If the scheme you specify in this command is available in multiple different lengths you will be prompted to specify which length should be downloaded.

For each barcode you wish to process (e.g. run this command 12 times for 12 barcodes), replacing the file name and sample name as appropriate:

E.g. for barcode03

artic minion --normalise 200 --threads 4 --scheme-directory ~/primer_schemes --scheme-name yale-mpox --scheme-version v1.0.1 --read-file run_name_barcode03.fastq --model r941_e81_hac_g514 samplename

Replace samplename as appropriate.

In this example --model r941_e81_hac_g514 is provided which is the most recent medaka model for r9.4.1 flowcell, basecalled with a high accuracy model, on guppy 5.1.4 or later. This obviously is not applicable to all data so users should replace this with the most appropriate model for their data, guidance on doing so is available here, a useful command is medaka tools list_models which will print a list of all models available to medaka.

It is vitally important that the correct medaka / clair3 model is chosen here, incorrect model choice can lead to systematic errors in the output sequence so this must be chosen with extreme care.

Custom primer schemes

If you wish to utilise a custom primer scheme not available in the PrimalScheme repository you may instead provide the scheme bedfile and reference fasta directly using the --bed and --ref arguments, for example:

artic minion --normalise 200 --threads 4 --bed ~/primer_schemes/some-scheme/some_virus.scheme.bed --ref ~/primer_schemes/some-scheme/some_virus.reference.fasta --read-file run_name_barcode03.fastq --model r941_e81_hac_g514 samplename

Output files

• samplename.rg.primertrimmed.bam - BAM file for visualisation after primer-binding site trimming
• samplename.trimmed.bam - BAM file with the primers left on (used in variant calling)
• samplename.merged.vcf - all detected variants in VCF format
• samplename.pass.vcf - detected variants in VCF format passing quality filter
• samplename.fail.vcf - detected variants in VCF format failing quality filter
• samplename.primers.vcf - detected variants falling in primer-binding regions
• samplename.consensus.fasta - consensus sequence
• samplename.amplicon_depths.tsv - a TSV (tab delimited) file containing mean amplicon depths across the genome

To put all the consensus sequences in one file called my_consensus_genomes.fasta, run

cat *.consensus.fasta > my_consensus_genomes.fasta

Software credits

The ARTIC pipeline and fieldbioinformatics software include a number of software packages:

• Reference alignment with minimap2
• Variant calling with medaka and longshot

Funded by the Wellcome Trust

Collaborators Award 206298/Z/17/Z --- ARTIC network