Hi!
I have encountered a problem that has not been solved for a long time. I have checked the forum articles, but the effect is not good.
I used QIIME2 to generate the relative abundance table of the phyla level, and used the R language to draw a stacked histogram. The graph showed that the top 4 phyla in relative abundance was completely different from the phyla sequence made by QIIME1.
My data is paired-end 16S rRNA sequencing. First, I use dada2 to generate table and rep seqs, and the interception length is 220 (Is this value setting reasonable?):
time qiime dada2 denoise-paired
--i-demultiplexed-seqs paired-end-demux.qza
--p-trim-left-f 0
--p-trim-left-r 0
--p-trunc-len-f 220
--p-trunc-len-r 220
--o-table table.qza
--o-representative-sequences rep-seqs.qza
--o-denoising-stats dada2-stats.qza
--p-n-threads 0
Then, use the comparison with the silva database to obtain taxonomy:
time qiime feature-classifier classify-sklearn
--i-classifier silva-138-99-515-806-nb-classifier.qza
--i-reads rep-seqs.qza
--o-classification taxonomy.qza
Finally, output the relative abundance table of the phylum level and draw a histogram in R language:
time qiime taxa collapse
--i-table table.qza
--i-taxonomy taxonomy.qza
--p-level 2
--o-collapsed-table phyla-table.qza
qiime feature-table relative-frequency
--i-table phyla-table.qza
--o-relative-frequency-table rel-phyla-table.qza
qiime tools export
--input-path rel-phyla-table.qza
--output-path rel-phyla-table
biom convert -i feature-table.biom -o rel-phyla-table.tsv --to-tsv
However, after this, there was a significant difference between the results of QIIME1 and QIIME2. Figure 1 shows the results of QIIME1 and Figure 2 shows the results of QIIME2. I don't know why this difference occurred.
Thanks for any suggestions!