Trimming MiSeq sequencing data individually

Hi everyone,
I am a novice Qiime2 user and I hope my questions here are not too silly.
The current version I’m using is: qiime2-2020.2
I installed it using conda environment on my Linux.

I have a question with the trimming of 16S sequence based on the quality of reads (currently trying Q20, Q25 and Q30). I had noticed that the trimming of reads was done after all the sequence files were imported, i.e. if the experiment was completed with 18 samples, all the 18 samples were imported at once and the quality trimming was done in the same settings for all sample right after (as in an assumption was made that all the 18 samples have the same exact quality, though, please correct me if I’m wrong).

For my 16S data, I noticed that each sample has different sequence length to its quality. I would like to opt for specific trimming for each sample individually. However, after the trimming of each samples using dada2, I had noticed that the output files is no longer a .fastq file to proceed with qiime analysis. So how do I import all the data? or maybe Qiime2 has another pipeline to tackle this issue?
I would like to apologize one more time if my questions are silly etc. I am new to Qiime2 and still learning the ways to analyze metagenomic data. I also would like to thank you in advance for your time and effort to help.

Hi @farisfauzimuhammad,

It’s not a silly question at all, but I have more questions for you. Are all the samples from the same sequencing run using the same primers? Do you want to use the dada2 quality trimming individually? Are you running dada2 alone?

If its a single run and primer pair, the general assumption in microbiome analysis right now is that you want to work on a per-run or per-study basis for parameters, rather than a per-sample. This is for a couple reasons. First, it makes the samples more comparable, because your settings are consistent. Since DADA2 learns per run, its important that information be consistent across the run. It also makes replication easier because you only need to report/remember one set of settings rather than 18! Sthe po, my recommendation here is that you stick to the per-run settings.


Hi and thank you very much for the tips.


Hi good day.

I thank you again for the insights from before and I am sorry I did not answer your questions. Currently the samples were from different MiSeq runs, however, all the samples uses the same sets of primers of which we are targeting the V3 to V4 region on the 16s.

For the second and third question. Yes I am currently only running dada2 alone and if I trim the samples individually, is it possible to import the trimmed data.

Thanks again in advance for your support.