Length filtering (2)

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

conda activate artic-ncov2019

Then use the command:

artic guppyplex

with the following parameters:

What? parameter Our value
The input directory containing the reads –directory ~/workdir/data_artic/basecall_01/
The output file –output ~/workdir/data_artic/basecall_filtered_01.fastq
Minimum read length –min-length 400
Maximum read length –max-length 700
(optional) Skip quality check –skip-quality-check

Since the quality check has been done along with the basecalling, we can use the flag --skip-quality-check. That will improve runtime, but does not really change much.

To perform the filtering for one dataset, we can use the following command:

artic guppyplex --skip-quality-check --min-length 400 --max-length 700 --directory ~/workdir/data_artic/basecall_01/ --output ~/workdir/data_artic/basecall_filtered_01.fastq

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

If you wanted to do that for all datasaets, you could do that in a loop:

for i in {1..5}
do artic guppyplex --skip-quality-check --min-length 400 --max-length 700 --directory ~/workdir/data_artic/basecall_0$i --output ~/workdir/data_artic/basecall_filtered_0$i.fastq
done

In the next step, we use the filtered reads to generate consensus sequences.