different taxa result between deblur and dada2

Dear Matthew,
I am a new user, and I hope to discuss the following problems with you!

(My qiime2 version is 2020.6, platform is Ubuntu 18.04.)

I used both dada2 and deblur to denoise the samedata. After annotation, I found that the results were quite different (as follow.

demux result is like this

I kept the 10-220 forward reads and 0-220 reverse read to proceed the dada2, the result goes like

I kept the 0-300 joined-read to proceed the deblur, the result goes like

I tried a lot of lengths of cuts but the results didn’t vary much, it showed defferent beween dada2 and deblur after feature classifier. The different results caused great trouble to my later data analysis.


  • Here is the code I use to proceed with the data, to make sure there are no errors in denoiseing processing

qiime vsearch join-pairs
–i-demultiplexed-seqs paired-end-demux.qza
–o-joined-sequences demux-joined.qza

qiime quality-filter q-score-joined
–i-demux demux-joined.qza
–o-filtered-sequences joined-filtered.qza
–o-filter-stats joined-filtered-stats.qza

time qiime deblur denoise-16S
–i-demultiplexed-seqs joined-filtered.qza
–p-trim-length 300
–p-left-trim-len 0
–p-min-reads 10
–p-jobs-to-start 60
–o-representative-sequences rep-seqs.qza
–o-table table.qza
–o-stats deblur-stats.qza

time qiime dada2 denoise-paired
–i-demultiplexed-seqs paired-end-demux.qza
–p-trim-left-f 10
–p-trim-left-r 0
–p-trunc-len-f 220
–p-trunc-len-r 220
–o-representative-sequences rep-seqs-dada2.qza
–o-table table-dada2.qza
–p-n-threads 0
–o-denoising-stats denoising-stats.qza

qiime feature-classifier classify-sklearn
–i-classifier /home/dell/micro/database/2020.6/silva-138-99-nb-classifier.qza
–i-reads rep-seqs.qza
–p-n-jobs 60
–o-classification taxonomysslivaFL.qza

Waiting for your reply!
thanks and hope you have a wonderful day !!

@Kry4tle, this topic (originally a direct message) appears to be a duplicate: different taxa result between deblur and dada2.

For future reference, we generally prefer that questions be posted publicly rather than sent as DMs. This allows the community at large to respond to them, and allows others to benefit from the response.

With this in mind, I’m closing this as a duplicate. Thanks for understanding!
Chris :dog: