I'm analyzing gut microbiota data. 2 x 300 pair-ends MISEQ runs on V3-V4. I had a similar issue with the previous ones who posted on this topic. I have QIIME2 version 2019.7 installed in macOS.
Summary: I followed Moving Pictures Tutorial. I denoised with DADA2 , truncated at 420 based on the QC plots.
The majority of samples (around 50%) ended up with unassigned taxonomy. However, when I processed the data through QIIME1, I don't have a lot of unassigned as is the case with QIIME1.
-The commands that I have used:
qiime dada2 denoise-single **
--i-demultiplexed-seqs Gut_microbiota/Sequences/demux-single-end2.qza **
--p-trim-left 30 **
--p-trunc-len 420 **
--o-representative-sequences Gut_microbiota/rep-seqs-dada2.qza **
--o-table Gut_microbiota/table-dada2.qza **
--o-denoising-stats Gut_microbiota/stats-dada2.qza
For the qiime feature-classifier classify-sklearn I have tried with greengenes and with the newest Silva database with my own primer sequences.
-Outputs: Please check the demux plot (demux.png). Barplot from QIIME2 and Barplot from some sequences did in QIIME1.
Hi @victoriamesa
The taxonomy result is so weird.It assigned nothing
The V3-V4 16S amplicon length is about 437bp I remember but your sequences base show there are some amplicons length is longer than 450.
I guess it is because you have merged your paired end reads before quality control.That is not appropriate since Miseq PE300 has poor perfomance on base accuracy in the reads tail.
I suggest you import the paired end read without merging (Type SampleData[PairedEndSeuquencesWithQuality]) and merge through command qiime dada2 denoise-paired
also train your own V3-V4 classifier.
Hope that would help you!
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!
Even though this is probably not what is causing this classification issue, it is probably leading to suboptimal performance with q2-dada2, which assumes that reads are not yet merged. So demultiplex and denoise on the unpaired reads and let q2-dada2 do the merging for you.
Thank you for your fantastic work through this forum and Thank you very much for having taken the time to answer in details and for your explanations. I did the taxonomic assignment with classify-consensus-vsearch method and and now I only have a small fraction of unassigned bacteria. I think it was a problem with the orientation of reads.
