Running viralrecon

In the previous section we downloaded the data we’re going to analyse and created the samplesheet we’ll use as input for Nextflow

We should now have everything we need to run viralrecon. However, before we can run viralrecon, we need to activate the nextflow environment we created earlier:

conda activate nextflow

viralrecon has a number of different parameters and options we could use to analyse our data and these are listed on the viralrecon pipeline page. We’re going to skip some of the steps such as removing host classified reads and lineage assignment in the interests of time but feel free to have a play with the different parameters. Ok, let’s run viralrecon using the command below:

nextflow run nf-core/viralrecon -profile singularity 
--max_memory '12.GB' --max_cpus 4 
--input samplesheet.csv 
--outdir results/viralrecon 
--protocol amplicon 
--genome 'MN908947.3' 
--primer_set artic 
--primer_set_version 3 
--skip_kraken2 
--skip_assembly 
--skip_pangolin 
--skip_nextclade 
--platform illumina

The parameters we’ve used are described in the table below:

If the pipeline starts running successfully, you should see output like the figure below:

Once the pipeline starts submitting jobs and these jobs start running, you’ll see the progress of all the steps in the pipeline that are running (see below). The number of jobs running at any time will depend on the amount of RAM and number of CPUs you have available. The more you have, the more likely it is that multiple jobs will run at the same time. Fortunately, you don’t have to worry about any of this as Nextflow will take care of this based on what you supply with the –max_memory and –max_cpus flags.

The pipeline will take a little while to run so time to go and make a coffee. If the pipeline completes successfully you should see a message like the one below:

Now, let’s move on to the next section where we’ll examine the results created by viralrecon.

How is it going so far? Do leave your comments in the discussion section below.