Hello,
Excuse me I am quite new to the metabarcoding analysis,
I started to analyze 20 samples (Endophytes and soil samples), I run the following lines;
iime tools import
--type 'SampleData[PairedEndSequencesWithQuality]'
--input-format PairedEndFastqManifestPhred33
--input-path manifest.txt
--output-path sequences.qza
qiime cutadapt trim-paired
--i-demultiplexed-sequences sequences.qza
--p-front-f CTTGGTCATTTAGAGGAAGTAA
--p-adapter-f GCATCGATGAAGAACGCAGC
--p-front-r GCTGCGTTCTTCATCGATGC
--p-adapter-r TTACTTCCTCTAAATGACCAAG
--o-trimmed-sequences sequences-trimmed.qza
Thank you for posting your full commands and DADA2 output stats. It looks like most reads are being removed during filtering, so let's work to improve that.
Have you viewed the sequences-trimmed.qzv file? This will show you quality throughout the read.
I usually trim off the low-quality ends of the read, which helps more reads to pass the quality filter.
Truncation both forward and reverse at 200 and this was the results: Image 200
Truncation both forward and reverse at 180 and this was the resultsL image 180
I think the truncation for both ends at 200, improved the results more, while at 180 it wasn't really helpful although some sequences dropped compared to the 200 .. what do you think ?
Because the quality is different in forward and reverse, I think you should choose different truncation settings for forward and reverse
Or don't worry about it! Having >80% of the reads pass filter is great! Having >70% of the reads merge is also very good in general and especially for ITS data.