Hi @gregcaporaso thanks for your kind reply and suggestions.
The very reason I used the command is given by the fact that I was using that with qiime1 and spent some time also for debugging one of the releases of qiime 2 since there were problems with time of completing the command, solved also thanks to my pointing that out.
The reason for 0.85 is the one you pointed out: I have followed the tutorial and not changed in time.
may I ask you why with gut samples the problem does not manifest while with low biomass yes?
To conclude:
Should I finally run only the following
taxa_classi:
$(CONDA_ACTIVATE) Miqiime2-2021.8;\
qiime feature-classifier classify-sklearn \
--i-classifier gg-13-8-99-nb-classifier.qza \
--i-reads rep-seqs-or-85.qza \
--o-classification taxonomy10C.qza
suppressing in toto the previous step?
joined_import_filter_derep_OTU:
$(CONDA_ACTIVATE) Miqiime2-2021.8;\ # I have a conda environment
qiime vsearch cluster-features-open-reference \
--i-table table.qza \
--i-sequences rep-seqs.qza \
--i-reference-sequences 85_otus.qza \
--p-perc-identity 0.85 \
--o-clustered-table table-or-85.qza \
--o-clustered-sequences rep-seqs-or-85.qza \
--o-new-reference-sequences new-ref-seqs-or-85.qza \
--p-threads 8 \
--verbose
so the input sequence for the classification are not clustered sequences coming from the following command:
joined_import_filter_derep:
export HDF5_USE_FILE_LOCKING='FALSE';\
$(CONDA_ACTIVATE) Miqiime2-2021.8;\
qiime vsearch dereplicate-sequences \
--i-sequences fil_joined.qza \
--o-dereplicated-table table.qza \
--o-dereplicated-sequences rep-seqs.qza
in the coming to the command
taxa_classi:
$(CONDA_ACTIVATE) Miqiime2-2021.8;\
qiime feature-classifier classify-sklearn \
--i-classifier gg-13-8-99-nb-classifier.qza \
--i-reads rep-seqs.qza \ # instead of clustered sequences **rep-seqs-or-85.qza**
--o-classification taxonomy10C.qza
Could you please confirm?
I thank you so much
Michela