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

[Lei (Lei Li)]

Hi @Nicholas_Bokulich,

Thank you for your detail explanation. Now I have better understanding of my questions.
1.

@Nicholas_Bokulich

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.

After you give me this specific examples, I was able to understand their differences completely. It looks like the 16s pre-trained classifiers which provided in the Qiime2 data resources website Qiime 2 are using the "taxonomy_7_levels" file instead of the "consensus_taxonomy_7_levels" and I have used it for my 16s data taxonomy classification. To make consistent, I might keep using my old 18s classifier which trained by using "consensus_taxonomy_7_levels". Is it make sense to you?

2

@Nicholas_Bokulich

“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.

I am working on both 16s and 18s data. I am satisfied with the 16s result by using the "7_levels" files. But 18s I am really not sure how the taxonomy of Eukaryote works and what is the best way to present the results. It has so many taxonomy rank compare to bacteria. So I thought putting all of the result in the some level might be better for interpretation purpose? I want to create Phyloseq object which require to specify the name of each taxonomic rank. If I use the 7 level, I do not know what name I should give if I go further than level 3 for the 18s data.

3

@Nicholas_Bokulich

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

I used to follow the classifier tutorial to train my Bayesian classifier for 18s data several month ago. After refreshing my mind, I was able to remember how to import the files to qza files.
4

@Nicholas_Bokulich

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.

@Nicholas_Bokulich

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

It make sense to me. DADA2 used extra long time to process all the 10 samples due to orientation problems. I understand other download stream analysis like beta diversity can also be affected since the distance matrices might not be correct under this conditions.
You mentioned that I can either classify my sample sets into two sets and run the classify-sklearn separately or using the classify-consensus-vsearch to solve this issues. However, I still cannot run the beta diversity analysis if I did not correct the orientation problem, right?
5

@Nicholas_Bokulich

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.

Do you mean the vsearch plugin for clustering and dereplicating the sequence? Did you suggest to use this method instead of using the DADA2 method? If I use vsearch method for clustering, do I still need to concern downstream taxonomic classification and beta diversity issue due to the orientation problems? For example, can I use classifier-skearn to do taxonomy after using the vsearch method?
6

@Nicholas_Bokulich

[BEST] reverse the orientation of any reads in the reverse orientation and proceed (starting with dada2).

If I still want to use DADA2 for denoise, as you recommend, I need to reverse the orientation of the reads for the raw fastq. But how can I do that for the fastq file. Can you please let me know by using which tools I can change the orientation of the fastq file.
Thank you so much for sharing your time to help me