When we see cases in which most of the reads are poorly taxonomically assigned, it is likely that your sequences are not in the same orientation as the reference database. Here are a few threads that may help:
Welcome to the forum @victoriamesa !
The 50% unassigned is a pretty clear indicator: your reads are probably in mixed orientations. The classify-sklearn method currently only supports classification of sequences that are in a single orientation. However, the classify-consensus-vsearch method can classify mixed orientation reads just fine. Could you give that method a try and let us know what you see? Thanks!
But @sixvable is also correct:
Even though this is probably not what is causing this…
Hello everyone,
My colleagues and I are doing the analysis of some low biomass samples with Qiime 2 (v2021.4). It's the first time that we do a microbiota analysis and we have found some strange results. In our Feature table we have a total of 56 samples, 4 commercial mock community replicas and some negative control samples. We have trained the classifier with the SILVA database following the instructions described in "Moving Pictures" and "Parkinson's Mouse" tutorials and then we have taxonom…
Hi @David_Bradshaw ,
I think much of what you have looks fine. Just a couple comments / suggestions:
EDIT (05/22/2023): If you are running qiime rescript get-silva-data, there is no need to run the above command, as get-silva-data runs this for you behind the scenes. My mistake, this command should be run.
I'd avoid removing Eukaryotes. A good classifier will contain a bunch of "outgroup" taxa. That is you should want to identify Eukaryotes, or any other "bad" items. Otherwise, Eukaryotic se…
When it is not possible to achieve an exact or near-hit to any of the reference sequences, the classifier will return the Lowest Common Ancestor (LCA) taxonomy of similar sequences.
-Cheers!
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