How to train the classifier for V3-V4 region with 99% identity using full length seuqnces from new relase of GreenGenes-2022??

Hello All,

I want to perform functional annotation using Picrust2 plugin.


First of all I want to train my classifier for V3-V4 region with 99% identity for latest release of greengenes-2022

Blockquote. I tried to check files from Index of /greengenes_release/2022.10 new release of GG-2022 but confused that which file is of my use!!!
Also, I am facing problem to use my QIIME2 outputs as a input file file for PICRUSt2. Please help me out for this, so that i can start my further data processing and analysis.

Any help will be appreciated.

Thanks and Regards,

Rashmi Ira

Hi @Rashmi_Ira,

I think what you would want to do is use q2-feature-classifier to extract-reads based on your primers from the Greengenes2 backbone sequences, and then train a Naive Bayes classifier on the result



Hi @wasade

Thanks for your response.
Okay I will check it out.

Can you please guide me with few commands and also which file is actually of my use to start with read extraction!??!

Thanks in advance.

Best Regards,
Rashmi Ira


the readme file looks like you'd need the following files:


The following method should help point you in the right direction. First take the sequence files and trim them based on your primers (obviously I've just added a random sequence here!). You can also add truncation and min/max lengths of sequences based on your experimental design, for example:

qiime feature-classifier extract-reads \
  --i-sequences 2022.10.backbone.full-length.fna.qza \
  --p-min-length 100 \
  --p-max-length 600 \
  --o-reads gg_12_10_ref_primer_region_seqs.qza

then use your newly trimmed sequence file along with the backbone taxonomy to train your classifier:

qiime feature-classifier fit-classifier-naive-bayes \
  --i-reference-reads gg_12_10_ref_primer_region_seqs.qza \
  --i-reference-taxonomy \
  --o-classifier gg_12_10_primer_region-classifier.qza

I hope that helps, there are lots of walkthroughs and helpful documents in the qiime2 forum and docs, for example here


Hi @buzic

Thank you so much for your response. I will follow the same as suggested.

Best Regards,
Rashmi Ira