Why the result different so much with same parameter?

We want to obtain the OTU (ASV) table from fastq, but the results of two people using the same qiime2 parameters are different: one person has more than 5,000 ASV, and the other has more than 3,000 ASV.
Parameter:
SILVA_132_QIIME_release
Raw sequencing data were analysed by QIIME2. In brief, the data were imported into QIIME2 and demultiplexed, a DADA2 pipeline was used for sequencing quality control, and a feature table was constructed with the following options: qiime dada2 denoise-paired—i-demultiplexed-seqs demux.qza—p-trim-left-f 0—p-trim-left-r 0—p-trunc-len-f 248 —p-trunc-len-r 200 —o-table table—o-representative-sequences rep-seqs.
Thank for your attention!

Hi! Is the version of Qiime 2 also the same?

No. He has gone for another lab. I think the qiime2 version is different.
However, different qiime2 version should not produce so much difference. Right? :joy:

Hi @Wang_cs001632 - we need more information in order to effectively help you:

  • Please provide the version of QIIME 2 used
  • Please list all relevant commands run (copy and paste please)
  • Please provide detailed information about what you think is the error you are seeing.

If possible, please upload QZVs (so that we can view data provenance).

1 Like

Thank for your reply.trimzch.qzv (299.7 KB)
This is my qzv. I don’t have his qzv. My version is qiime2 2019.7.
Might you can infer the reason of different through your experience?

You have only provided one result - there is nothing to compare it to to ascertain a difference.

Sorry, his version is lost. Could you provide a few possible reason? Before this qzv, my result is same with his. However, we different after qiime dada2 denoise-paired—i-demultiplexed-seqs demux.qza—p-trim-left-f 0—p-trim-left-r 0—p-trunc-len-f 248 —p-trunc-len-r 200 —o-table table—o-representative-sequences rep-seqs

  • Changing the trunc or trim parameters, even by 1 nt, will produce entirely new ASVs
  • Different versions of DADA2/q2-dada2

Without the provenance for both datasets its not possible for us to say anything more specific.

Hope that helps!

:qiime2: