Removing Primers: Cutadapt vs Denoising

[SoilRotifer (Mike Robeson)]

Hi @elsamdea

@elsamdea

So, my question is: if you can use directly the q2-dada2 to remove the primers base pairs, why in most of the papers suggest you use cutadapt before denoising?

These are good questions! You are correct... technically you can go either way... that is use cutadapt, or just trim the primers out directly within deblur or DADA2.

I prefer to use cutadapt to remove primers for the following reasons, which I think I've echoed elsewhere in the forum a few times:

• There may be spurious off-target sequences within your data. Just trimming will retain these reads.
• PCR / sequencing errors can add or remove bases from the beginning or end. Thus potentially inflating differences between sequences creating more sequence variants in the output.
• Quality at the beginning of the read is somewhat indicative of the quality later in the read. That is, if you cant find the primer... then what else is wrong with the sequence?

That is, using cutadapt to search through your reads, to find and remove the primers, is an additional form of quality control. That is, if you are unable to find the primers.. then chances are the reads are of low quality anyway and you might as well discard sequences from which you are unable to find the primers (e.g. --discard-untrimmed).

For example here are a couple forum threads you can read through:

q2-cutadapt output

Hi, Firstly, thank you to the moderators for always responding so promptly and being so helpful with queries. My question is in relation to the q2-cutadapt output (attached). I find that about 50% of reads from all my samples are removed after this step. If I understand correctly, this is because the primers were not detected in these removed reads. I work on 2 x 300bp (Illumina MiSeq V3) 16S reads. Is this 'normal' for Illumina reads or am I losing too much? The parameters I use are: qiime …

Cutadapt failing to remove all primers

Hi @Moon Add the flags --p-match-read-wildcards and --p-match-adapter-wildcards to your command. This will allow matching of IUPAC wild cards such as H, V, N, etc… This is why your primers are not currently being trimmed. So, adding these flags, at least the --p-match-adapter-wildcards flag should help. This also may explain why you end up with having so many sequences. The variety of alternative bases (due to the primer wild cards) create more variation in your sequence in the primer region.…

But there is no one "right way" to do things here. I just prefer to be as exacting, and retain the best quality data I can.