I have some datasets from different v regions e.g v3-v4, v4 for analysis. The protocol I'm following is that to trim the the v3_region from v3_v4 region so to retain onlny the v4 region. The way i do that, is the use o cutadapt trim-paired using the 515f primer and 806r primer
So here is question, on classifier step after I trim off the primers can i use a pretraining classifier with out primers?
or build my own classifier with out primers? For example using the GenBank with RESCRIPt. Or other database.
And something more i read from many post that, combine datasets from different v region suggests the fragment-insertion step. can anyone explain me after that step which database we use? because we have multi v regions.
Yes, definitely. Training a region-specific classifier is even shown in the RESCRIPt tutorial on the forum, e.g., here (shown with SILVA, but you can do the same with an NCBI classifier following get-data-ncbi):
It sounds like you may be looking for q2-sidle, depending on the V regions that you have. But if you have a bunch of sequences from different V regions mixed in a single file you could technically also use a full-length 16S classifier to taxonomically classify all of these.
I would be careful with Silde and multiple regions; I've had issues with diversity being inflated because the algorithm couldn't solve the multiple regions. I think the classifier approach may be better.
I’m trying to train the gg_138 V4 reference database with the human-stool.qza as the class-weight within qiime feature-classifier fit-classifier-naive-bayes.
I download the gg_138 i create the ref-seqs-v4.qza ref-taxonomy.qza
and now i try to Retrain the Classifier with bespoke waights human-stool.qza from
The command that i use is,
qiime feature-classifier fit-classifier-naive-bayes
When i run this command comes out this messeg:
Plugin error from feature-classifier:
Number of priors must match number of classes.
Debug info has been saved to /tmp/qiime2-q2cli-err-yaqv434y.log
This means that the weights and database do not match. The readytowear repository contains the sequences and taxonomy that correspond to those weights, so you will need to train a classifier using those files if you want to use the readytowear weights.