There are some confusion in quality control step, and I am new in this field, so it can be very basic thing..
Well for qc step in human genome sequencing, such as GWAS, I remember doing quality control that exclude samples with bad quality. But for here, I see that this kind of filtering step is not included in Qiime2 tutorial.
There are only steps that trim or exclude "reads" based on per-nucleotide basis (according to the paper reference mentioned in tutorial basic quality-score-based filtering ) or other filtering options are in qiime2 tutorial, but I see that this step seems to be run after having feature table.
Also, DADA2 and Deblur is other tools that do qc but I see there is no process that exclude samples, only poor reads.
Well, for this following example is not about "base-quality" but about "read counts", but for example there are some samples with only 43 reads and samples with over 100,000 counts. Very skewed. In this case, should I remove samples with very low reads and with too much reads, in case of contamination or other factors..?
Adjusting sampling depth (in this case some samples under sampling depth will be excluded..right?) is related with sampling size bias.
So, to make it simple I'm wondering can I exclude 'samples', based on 'base-quality' or 'reads count' before making feature table?
Thank you in advance