I'm trying to process multiple samples using Qiime2. I imported all samples as one .qza file and went through the pipeline to process them. When I output the files, there's no indication of what sample the DNA originally came from. i.e. I want the labels to state the first 4 DNA sequences in the .fasta file came from Sample_1, the next 4 DNA sequences came from Sample_2, etc.
QIIME 2 assumes that you've divided your sequences into samples before you start procesing the data. The first step in OTU clustering breaks the mapping between a sequence identifier and the counts of that sequence. (So, we can't say OTU 1 has sequences labeled seq1.sample1, seq2.sample1, seq1.sample3, etc).
You need to demultiplex your sample before you do this. If you have a fastq file, there are a lot of options for demultiplexing, based on how the data is formatted.
With a fasta file, you might be able to filter it, but you might also just need to do that outside qiime2.
Thanks for the response! My data is demutliplexed. I imported the folder containing all 33 fastq files into the one .qza file. Is there any way to determine which fastq file the data originally came from? Do I just need to import each fastq as a different .qza?
This is a screenshot of the feature table I output with my final fasta file, but the MD5sum doesn't match the labels in the fasta file.There's also only 53 listed in feature table, but I have 95 dna sequences in the fasta file.
I feel like I'm so close, but things don't quite make sense.
I think you're confusing the feature table as an artifact as a feature table object and the representation of the table. So, the Artifact file you see wraps the feature table. That feature table can be to other functions (summarize, visualize, rarefy, etc). But, this funny format has some advantages, like that it carries the history of everything that has happened to the file. You're currently viewing the information from the import step. It shows the data was imported as a manifest. The md5sum is a way to make sure the data was imported correctly. Its a really simple way to summarize the whole sequencing file.
But, that doesn't help you if you actually want to see the table. For that, you need a transformation function. I tend to use qiime feature-table summarize to get a sense of the sequencing counts for samples and features. I am like 75% confident you can also just use qiime metadata tabulate to display the table.
So, there are a couple of possibilities here. One is that you had some of the same sequence repeated over and over again. You might have 95 original sequences, but only 53 of them are unique. This is pretty common and actually desirable. You can be more confident that the sequence is "right" if you see it more than once.
A second possibility is that the sequences were discarded during denoising. Some of the sequences might be low quality in one way or another: a chimera, or they didn't sequence well, or there were several PCR errors. Those sequences are removed during denoising becuase they are noise. You can check the dada2 stats (I'm assuming you used dada2, otherwise sub for the deblur stats) to check what happened to your data. This can be helpful to diagnose and maybe get more sequences.
Finally, because I'm that person:tm:, I really recommend considering how much you can learn from 95 sequences, especially 95 unique sequences. If your samples are low biomass, you might want to look at the KatharoSeq paper about titrating actual reads from noise. My personal rule of thumb for my favorite high biomass enviroment () is that I need about 1000 good reads to be able to make any inference, and I'm much happier when I have 5000, or 10,000,