Hi everyone, I’m trying to run taxonomy classification using the SILVA V3 classifier (for forward read only analysis), but I keep running into a scikit-learn version mismatch error in QIIME2-amplicon-2025.7. Here is the error I get: Plugin error from feature-classifier:
The scikit-learn version (0.24.1) used to generate this artifact does not match
the current version of scikit-learn installed (1.4.2). Please retrain your classifier
for your current deployment to prevent data-corruption errors.
I understand why the version mismatch occurs, but I am unsure how to proceed for the SILVA V3 region specifically. My questions:
Is there an officially supported SILVA V3 classifier compatible with QIIME2-amplicon-2025.7 (scikit-learn 1.4.2)?
If not, what is the recommended workflow to retrain a SILVA V3 classifier?
Are there updated SILVA reference sequences/taxonomies recommended for this version of QIIME2?
Is there any chance that the QIIME2 team plans to release updated region-specific classifiers (e.g., V3, V4, V3–V4) for 2025.x distributions?
I want to ensure that I follow the correct procedure rather than manually downgrading scikit-learn or breaking my environment.
There can be skbio conflicts between older and newer versions of QIIME 2. So, the version used to train the classifier should be identical to the one within your QIIME 2 environment.
The best way to get around this is to make your own claassifiers with RESCTIPt. You can follow the main example tutorial here. The part for amplicon specific classifiers is here
Note the tutorial is not meant to be an standard operating procedure (SOP), but just a set of examples of how you can make your own classifier, as mentioned here and here.
Thanks so much @SoilRotifer for helping me out. I did come across RESCTIPt during my research on qiime2 forum and I will consider exploring this option for sure. As an alternative approach, I’m considering extracting the V3-V4 region (target region of our NGS run) directly from the full-length SILVA 138-99 NR sequence file using:
In my dataset, I will be using forward reads only because the reverse reads are too short to be usable. I believe the forward reads cover the full V3 region plus a part of V4.
Initially, I planned to use a SILVA V3-only classifier, but when I tried that, I encountered a scikit-learn error in QIIME2 2025.7.
Given this situation, I’m unsure which approach would be more appropriate:
Extract V3-V4 region from SILVA and train a classifier on that, even though I will classify using only forward reads, or
Use a V3-only classifier, even though my reads extend into the beginning of V4.
Could you (or anyone at the forum) please advise which option would be more appropriate for a dataset where the forward reads fully cover V3 and partially cover V4? Any suggestions on fixing the scikit-learn error with the V3-only classifier would also be helpful.
To be honest, you might not see a significant improvement using an amplicon region specific classifier over a full length classifier. Making an amplicon specific classifier is often required for users with limited memory and/or storage space. That is, it's best to use if you only have 16/24GB RAM or less. Otherwise, if you have 32/48 GB RAM or more, a full-length classifier will be fine.
Assuming you actually amplified your targets with V3V4 primers, and if you'd still like to make an amplicon-region specific classifier: then I'd use the full extracted segment for your reference database (#1), then just use your forward reads against that. I often do this myself, when my reads are too short to merge.
If you make the classifier in your existing environment you should not get the warning message.
Hi @SoilRotifer, thank you very much for the clarification and prompt response. If I understood correctly, using an amplicon-specific classifier versus a full-length classifier will not substantially change the taxonomic assignment accuracy (I remember reading the same at the forum or qiime2 guidelines).
I just wanted to confirm one point: since I’m working with forward reads only (as you mentioned you too do it oftenly), it should still be fine to proceed with the full-length SILVA classifier, and the taxonomic classification should not be negatively impacted by the incomplete read length - correct?
Additionally, how acceptable is this approach in general within the community, given that you also run analyses with only forward reads?
Quite acceptable. I myself have published a couple papers in which I was only able to use the forward reads. Either because the reads were too long to merge, or the reverse read was of such poor quality we could not merge them. Just make it clear that is what you only used te forward reads in your methods section. It's quite common.
@SoilRotifer When I performed a trial analysis just to compare the outcomes of extracted V3-V4 and full length, I realised that V3-V4 extracted approach is giving me species-level resolution, while full length could not. Have you also observed the same in your analyses?
I'd not be overly confident in most species-level classification with short reads. In fact, even with full-length 16S it can be difficult to identify between genus and species level. This is not to say they are not true, ... I just want to present the caveat that one should not trust a classification just because you get 'better' resolution.
That being said... it is quite possible that an amplicon-region based classifier is better for your data. But I've also seen the opposite, where the full-length classifier was 'better'. Sometimes each is better for different groups of taxa... But it's hard to know what that means, as each data set is different.
You know your system best, so I'd suggest going with whatever makes the most sense given your knowledge of the system.
Right! That makes complete sense, especially considering how unreliable species-level annotations can be with short reads. Even with full-length 16S, distinguishing genus from species can be challenging (unlike WGS data), so I agree that higher “resolution” in the output doesn’t necessarily mean it’s biologically reliable.
I also came across similar cautions - “Species-labels: caveat emptor!” note on the QIIME2 2024.2 data resources page, which reinforces the need to interpret species-level classifications very carefully.
... in many cases at least.