Generating consensus sequence (2)

First of all, if not active, activate the artic-ncov2019 conda environment:

conda activate artic-ncov2019

Then use the command:

artic minion

with the following parameters:

What? parameter Our value
Use medaka –medaka
The directory containing primer schemes –scheme-directory ~/artic-ncov2019/primer_schemes
The input read file –read-file ~/workdir/data_artic/basecall_filtered_01.fastq
Number of threads to use –threads 14
Normalise to max 200fold coverage –normalise 200
The primer scheme to use positional (1) nCoV-2019/V3
The sample name (prefix for output) positional (2) barcode_01

Enter the newly created results directory first:

cd ~/workdir/results_artic/

Then you can run the ARTIC pipeline for one dataset:

artic minion --medaka --normalise 200 --threads 14 --scheme-directory ~/artic-ncov2019/primer_schemes --read-file ~/workdir/data_artic/basecall_filtered_01.fastq nCoV-2019/V3 barcode_01

Perform that step for the first (01) dataset only to save time. Do the other datasets later, when there is time left.

A loop to process all datasets would look like this:

for i in {1..5}
do
artic minion --medaka --normalise 200 --threads 14 --scheme-directory ~/artic-ncov2019/primer_schemes --read-file ~/workdir/data_artic/basecall_filtered_0$i.fastq nCoV-2019/V3 barcode_0$i
done

When you are done, consensus files have been generated:

~/workdir/results_artic/barcode_01.consensus.fasta

If you want, you can map the consensus to the Wuhan reference and view the results in GenomeView, or use QUAST, to compare the sequences.