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
--p-sample-stats
--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
