Hi @wasade,
Thanks again for your help.
The new gg2 nb classifier from the resources worked flawlessly with the qiime feature-classifier classify-sklearn command. I was able to follow the subsequent commands easily from the output files generated until running qiime diversity core-metrics-phylogenetic. At this point, I encountered the following error:
Plugin error from diversity:
** module 'skbio.diversity.alpha' has no attribute 'sobs'**
Debug info has been saved to /tmp/qiime2-q2cli-err-t2yrma6i.log
qiime2-q2cli-err-t2yrma6i.txt (3.5 KB)
I noticed an active thread (still unresolved) in the forum addressing this issue. I followed the suggestions but couldn't resolve it. Initially, I was working on qiime2-amplicon-2024.5, but just for the sake of it, tried the command with the same input files on qiime2-amplicon-2024.2 and it worked!!! Please check the screenshot below.
Regarding your recommendation on using non-v4-16s, I understand that the phylogenomic Greengenes2 phylogeny can be used rather than re-estimating from ASVs. However, I still have the same query: When using Qiime2 (qiime feature-classifier classify-sklearn) with either SILVA or Greengenes2 (2022.10.backbone.full-length.nb.sklearn-1.4.2), I observe a significantly higher number of reads per sample, even after filtering out non-bacterial/unassigned sequences, compared to gg2-non-v4-16s (see the attachment for comparison).
SILVA vs. Greengenes2.txt (8.7 KB)
How were the ASVs mapped to SILVA?
For mapping ASVs after DADA-2, I used the "classify-sklearn" method with a SILVA Naive Bayes classifier. The command used is as follows:
qiime feature-classifier classify-sklearn --i-reads rescript-rep-denoise-trimmed-seqs.qza --i-classifier classifier.qza --o-classification rescript-rep-denoise-trimmed-seqs.tax.qza
Thanks again for your help, and apologies if I am repeating myself on some points. Still new to many aspects here and trying to understand things better to get a clearer view of what I'm doing.
Best,
D_S