I am new here and I've read lots of posts about dada2 parameters but I couldn't find any answer for me.
I used 515F–806R primers to get amplicons. Samples were extracted from chicken feces swabs. Reads are paired-end 2x300bp. Running QIIME2 2020.6 in a conda environment.
Based on the quality score plot above, I used the following code in the Dada2 step:
qiime dada2 denoise-paired /
--i-demultiplexed-seqs new-analysis-kit_comparison/pe-kit-demux_kit1.qza /
--p-trim-left-f 0 /
--p-trunc-len-f 260 /
--o-representative-sequences rep-seqs-kit1.qza /
--o-table table-kit1-dada2.qza /
--o-denoising-stats stats-dada2-kit1.qza /
--p-n-threads 0 /
As you can see on stats data file, I think I had a good filter and merge results, but I got a low percentage of input non-chimeric, which I think means that I lost 90% of my data.
Any clue on what is happening?
Thanks in advance
demux-kit1.qzv (316.1 KB)
stats-dada2-kit1.qzv (1.2 MB)
Wow, you are losing a lot of reads, definitely an abnormally high number! You should check out these related forum topics for more discussion on how to troubleshoot and adjust… one thing to make sure is that you are removing your primers prior to dada2 (either with q2-cutadapt or with the “trim-left” parameters in dada2:
Just hoping to get some advice, after running our 16S amplicon sequencing data through dada2 it seems to be concluding >80% of the reads are chimeras, leaving us with too few reads to make any robust observations. We were already suspicious the data might have some chimera problems… but I would like to be confident that there are in fact this proportion of chimeras before I conclude it is a bust… any advice? See below for the code & demux.qzv file. I am running this on qiime2-2018.2.
70% is indeed quite a bit, hopefully we can improve on that a bit. Here are some things to consider and follow up questions:
Your trim/truncating parameters look good to me, with plenty of overlap for merging etc. however,
DADA2 can struggle a bit with chimera detection if non-biological sequences are left in your reads, for example if you haven’t removed your adapters, barcodes, primers etc. So this is a good starting point to make sure you only have biological sequences in your…
I’m puzzled by the low proportion of non-chimeric reads I obtain with the command:
qiime dada2 denoise-paired
Thanks Nicholas! I will check them and give you any updates.
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