otus and greengenes?

Hi guys
Hope you are keeping well.
I ran qiime2 before using greengenes database and dada2.
Could you please let me know if possible to have OTUs rather than ASVs in QIIME2?

I am asking as BugBase only accepts biom inputs from greengenes and OTUs.

Thanks
Marwa

Hello Marwa,

Yes, you can create OTUs using Qiime2 using the vsearch plugin. This supports de novo, closed-ref, and open-ref clustering, so you can pick the method that works best for BugBase.

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Many thanks for your reply.
I didn't get the following comment fully; do you think I can pick OTUs (closd-ref) from denoised samples (that I ran dada2 on)?

I think I can contextualize it.

You can take your denoised reads (ASVs) and run closed-ref clustering on them to make closed-ref OTUs. That was done on the American Gut project that Justine linked, and it may make sense in some contexts because it gives you both 1) new-school ASVs and also 2) old-school database OTUs.

In that thread, the user asks if doing de novo clustering then dropping NAs is the same as closed-ref, and no, no it's not.

The options are

  • denoising -> ASVs (this is the modern, recommended option :trophy: )
  • de novo clustering -> de novo OTUs
  • closed-ref counting -> database OTUs
  • closed-ref counting followed by de novo clustering -> open-reference OTUs
  • denoising followed by closed-ref counting -> database OTUs (from your ASVs)
    (this :point_up: is the hybrid approach you asked about!)

Then there's

  • de novo clustering followed by dropping NAs -> this is nothing
2 Likes

Many thanks for your clarification, really helpful.
I was wondering, do you think the closed-ref counting on my ASVs is different from the closed-ref counting approach? or just a lesser number of features?

Yes. But let's start with their major similarity: both methods will match against a selected database, and return the features from that database that match your inputs to a given % identity. The resulting features will be 100% biased by == 100% consistent with that database, and this adherence to the database is needed for some methods.

Denoising before doing this database search will give you ASVs as features you can use for other pipelines or database-independent methods.