some confusion about the feature table of qiime2

[jwdebelius (Justine Debelius)]

Hi @Daryl,

Let's take a step back. A "feature table" is a tabular representation of your data where we map samples to some kind of feature (ASVs, OTUs, species, metabolites, genes, cytokines, cities visited, nutrients... if you can represent it as a feature table, you can represent it as a feature table!). In QIIME (and microbiome bioinformatics specifically), the feature table typically looks something like this under the hood:

sample-id
	5	0	500
	100	200	50
	0	5000	30
	0	0	10

Due to legacy reasons, we typically represent the features (here , , , ) as the rows and the samples (, , ) are the columns. We can also represent the data as relative abundance, rather than counts. But... those are details.

@Daryl

As far as I know, there are three ways to generate feature table in qiime2:

1. dada2
2. delur

3)the method of dereplicate-sequences in q2-vesearch

I’m not sure whether the feature table generated by these three methods is the same thing.

They primary ways to generate a feature table in QIIME 2 from amplicon data dada2, deblur, and clustering via vsearch to make OTUs (I recommend the tutorial here for more details on OTU clustering.) Essentially, though, after you take the dereplicated sequences from vsearch, you then cluster those to generate a table like the one above.

You can learn more about all the different kinds of features here:

What is a "Feature" exactly?

Hi I am doing the “Moving Pictures” tutorial, but I don’t understand the “feature” meaning, and it seems to be a core concept in QIIME 2. Can you help me with that? Sorry if the question is too basic. Thank you

@Daryl

Some people said that feature table is different from OTUs and can be regarded as 100% OTUs. I know that OTU is the result of sequence clustering. So 100% OTU means no clustering, just the sequence after dereplicated?

A 100% OTU (also zOTU or sOTU [zeroOTU or subOTU]) is usually the same as an ESV (exact sequence variant) or ASV (amplicon sequence variant). This should be the output of one of the denosing algorithm: dada2 or deblur. You want to denoise to get this output because denoising addresses error in your sequences. If you use the directly dereplicated sequences, you'll introduce error from PCR and sequencing.

The ASVs or raw sequences can then be clustered into operational taxonomic units (OTUs) which are just clusters of sequences with some threshold of similarity.

Here's a really nice discussion about OTUs and ASVs, etc

To cluster or not to cluster?

Hello - I would like to bring up a topic that has been discussed before (A quick clarification on clustering) and provide an example from my own analysis. I am looking at one particular taxonomic group within environmental samples of bacterial populations. The method of denoising is dada2, so I am assuming that OTU clustering was not necessary since sequencing errors are accounted for during the denoising process. However, this analysis resulted in many OTU's being defined, some of them quite r…

ASVs and OTUs are one kind of feature a table can contain. But if you used something like q2-PICRUSt, your feature table might contain genes. If you use q2-taxa and collapsed your taxonomy, your feature table would contain the collapsed taxonomy. If you have metabolomics and you have q2-metabolomics then your features are molecules. You could even make a feature table out of metagenomics data using q2-metaphlan or q2-shogun.

Best,
Justine