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.
ARTIC bioinformatics SOP https://artic.network/ncov-2019/ncov2019-bioinformatics-sop.html