That makes sense. Just making sure! Still, would be good to know how this performs when running the single-end data. For some reason merging reads appears to be failing in dada2. What primers are you using?
It looks like your parameter settings should leave enough overlap to permit merging. The sequences are being dropped at the merge step, not denoising, so your truncation parameters do not appear to be the issue. @benjjneb do you have any thoughts on what's happening here?
Yes. As a matter of fact, this may be beneficial because the quality-filter step before deblur is going to trim sequences at different sites, wherever the quality drops below a certain threshold. So deblur's trimming step is going to do two things: drop any sequences shorter than that length (unless if you turn off trimming) and trim sequences longer than that length. Hence, depending on how much your sequences are being pre-trimmed in quality-filter you may want to set a lower deblur trimming length than what you are using in dada2.
You can use q2-cutadapt to trim out primers. See a tutorial here.
Oops, looks like I misread what you wrote above. Thanks for clarifying!
Still, the different taxonomic results here do not concern me — because it seems that these data are being over-filtered already, so the results from either analysis is not representative of the whole. If you get wildly different results after improving sequence yields, then I think we have something to worry about!
Let me know how it goes!