# ESV vs OTU after dada2

**URL:** https://forum.qiime2.org/t/esv-vs-otu-after-dada2/13616
**Category:** User Support
**Created:** [February 13, 2020, 10:53pm UTC](https://forum.qiime2.org/t/esv-vs-otu-after-dada2/13616 "2020-02-13T22:53:14Z")
**Posts on this page:** 1
**Showing post:** 3

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### Author: ![kmz](https://forum.qiime2.org/letter_avatar_proxy/v4/letter/k/977dab/32.png) [@kmz](https://forum.qiime2.org/u/kmz)
#### Post date: [February 14, 2020, 6:09am UTC](https://forum.qiime2.org/t/esv-vs-otu-after-dada2/13616/3 "2020-02-14T06:09:28Z")

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Hi @Mehrbod_Estaki,

Thanks a lot for your detailed response!

> [@Mehrbod\_Estaki](#):
>
> OTUs are traditionally referred to sequence bins that have been clustered at some percent identity, typically 97%. So a 100% OTU would be like an ASV, where even a single nucleotide difference between sequences will be called a unique variant, or OTU if you will.

Yes, indeed I am aware of this, which is why I want to compare the ESVs vs OTUs.

> [@Mehrbod\_Estaki](#):
>
> Since you haven't actually collapsed your ASVs into OTUs, the expectation should be that they should be the same.

So, according to the moving pictures tutorial, after demultiplexing the reads, and de-noising (in my case using dada2), I followed the steps that result in a "feature table". Aren't the reads clustered into OTUs in the feature table? I thought they would be because, when I exported the feature table to a .tsv, the column header said #OTU. I read [this](https://forum.qiime2.org/t/how-to-make-classic-otu-table-with-qiime2/3612) post, and followed your advice there. I didn't do the last step because I don't really need the actual names of the species, which is what I thought the last step was doing. But I guess I was wrong - I guess [this](https://docs.qiime2.org/2019.10/tutorials/otu-clustering/) will tell me what needs to be done.

> [@Mehrbod\_Estaki](#):
>
> > [@kmz](#):
> >
> > Now, this .tsv file has 1319 OTUs, whereas I had 1653 ESVs.
> 
> This is odd, they should be identical. Can you clarify how you determined these 2 values? Are you sure the same feature-table was used in both situations?

The data was the same but the pipelines were different. For one case I did excatly what the moving pictures tutorial suggested (which I described in the first post), and for the other, I used the [dada2 pipeline](https://benjjneb.github.io/dada2/tutorial_1_8.html) in R. Could that explain the difference?

Thanks!

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