demux view generates weird paired-end graph

[afinaa]

HI all,

We run analysis on amplicon quite frequently but there is one dataset that provides weird looking (to us) graph when we run demux view as below.

rawdata.qzv (313.7 KB)

We noticed that for reverse reads, from 50-110 bp the graph looks not normal?
Does this mean that the quality is actually not good?

What we did to the raw data before we imported into QIIME2, we did primer and quality trimming using bbduk. Our previous analysis that used the same workflow did not have problem like this so I doubt it was because of the bbduk trimming.

Appreciate any thoughts about this.
Thank you.

[colinbrislawn (Colin J Brislawn)]

Hello @afinaa

Thanks for posting that graph

@afinaa

We noticed that for reverse reads, from 50-110 bp the graph is looks not normal?

Yeah, read 2 looks pretty strange!

@afinaa

What we did to the raw data before we imported into QIIME2, we did primer and quality trimming using bbduk. Our previous analysis that used the same workflow did not have problem like this so I doubt it was because of the bbduk trimming.

Thanks for mentioning your preprocessing steps. I don’t think bbduk would cause changes to quality score like that, but you could import the raw data just to double check that the strange quality scores are already inside read 2, and not created by bbduk.

If bbduk is causing problems, don’t worry! There are Qiime 2 plugins like cutadapt that can remove primers and do quality trimming all from within the Qiime 2 ecosystem

Let us know what you try next!
Colin

[afinaa]

Hi @colinbrislawn ,

Thank you for the reply.

I imported the rawdata without any preprocessing and seems like the graph for read 2 is still not normal.

rawdata1.qzv (313.6 KB)

But we have also run cutadapt to the raw data but the read 2 graph still looks strange.

cutadapt-trim.qzv (319.1 KB)

Any idea what might caused this? Could it be from the sequencing run?

[colinbrislawn (Colin J Brislawn)]

Hello again,

@afinaa

Any idea what might caused this? Could it be from the sequencing run?

If it’s in the raw data, then it must be from the sequencing run itself.

You could try processing this with dada2 and see if the denoising process can help correct for these low quality regions, but if that does not work, it might be best to just use the forward run as the quality is higher.

Colin