What are differences for the files in Silva database which used to train the classifier?

[Nicholas_Bokulich]

@Lei

These two files has the same rows (55145) however, the order of the name in first column is different. The content in the second column under the same name of column 1. Just from the files, it is difficult to see what are the exact differences between two files.

The two files are different. Look for "Ambiguous_taxa" labels in the consensus taxonomy and compare to taxonomy_7_levels... the ambiguous taxa occur in consensus taxonomy because there is not consensus at that level, but the fill label is listed in taxonomy_7_levels because (presumably) it is the cluster rep seq.

@Lei

1) Regarding the method selection: If I really want to get all_levels taxonomy, I know that I need to use other method. Among the available method in Qiime2 which can handle this: classify-consensus-blast and classify-consensus-vsearch, which one do you recommend for the 18s data?

"all levels" presents problems for really any classifier, since the taxonomy becomes a knotty mess (and this is not an issue specific to q2-feature-classifier either). The BLAST and VSEARCH-based classifiers will have the same issue unless if you use maxaccepts=1, which is still going to grab the top hit (like classify-sklearn confidence=-1) so is sub-optimal.

The best solution is really to use the 7-level taxonomy if you can...

@Lei

The ARTIFACT required should be the qza file, but I only have the “fna” and "“txt” file which obtained from the SILVA database. How can use these file to make ARTIFACT which are needed for the command?

You need to import to QIIME 2 — see the feature classifier tutorial on qiime2.org for specific examples.

@Lei

The sequence in 5 of the files are from 5’’ to 3’’. The sequence in the other 5 samples are from 3’’ to 5’’.

Dear oh dear — mixed orientation is bad news and not just for taxonomy analysis. dada2 is effectively going to duplicate all ASVs, because the reverse complement of any ASV is a new ASV. Make sense? That's bad news for all analyses, especially if the samples are stratified by orientation.

@Lei

I used the classify-sklearn method to do taxonomic classification for the files which generated by DADA2 (contain all 10 samples). Only 5 of the samples has high resolution taxonomy. But the other 5 only assigned to Bacteria kingdom (However, the sequence read in these samples is not low (>10000)). Do you think this is because the classify-sklearn is confused with the order of sequence in the samples?

Yes — classify-sklearn looks at the first 100 or so seqs to decide the orientation, and classifies based on that. Your mixed orientations leave it confused .

You have a few solutions. Fortunately, it sounds like your samples are stratified by orientation (e.g., sample 1 is all in forward orientation and sample 2 is all reverse) . So you could:

1. [BEST] reverse the orientation of any reads in the reverse orientation and proceed (starting with dada2).
2. classify your sample sets in two sets, separated based on read orientation

But perhaps I misunderstand and all samples are in mixed orientations, in which case use classify-consensus-vsearch, which can already handle mixed-orientation reads.

@Lei

Do you think it is necessary to use some tools to flip the sequence by some tools (if available; but I don’t know which tools can do this work) to make all of them from 5’’ to 3’’?

Yes! VSEARCH comes pre-installed with QIIME 2 and has a method to reverse read orientations. This will only be useful if your read orientation is stratified by sample, not if all samples are in mixed orientations.