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