Major drop quality score with --trim adapters

[felipebatalini]

Why are my q scores dropping so much with --trim adapters?

We are using FLO-MIN114 and R10 chemistry for a cDNA library derived from human RNA.
We noticed a high percentage (>50%) of unusable reads detected by pychopper with the wf-transcriptome workflow, and then identified that it could be improved to <10% if we trimmed the adapters (therefore keeping primers).

However, I was surprised to see a significant drop in the quality scores when I turn on --trim adapters:
nextflow run epi2me-labs/wf-basecalling \ -profile singularity \ --sample_name $sample_name \ --input $pod5_dir \ --dorado_ext pod5 \ --basecaller_cfg [email protected] \ --qscore_filter 10 \ --basecaller_args "--trim adapters" \ --output_fmt fastq \ --out_dir $results_folder
While it makes sense for pychopper to work better with the primers present, I can't understand while the basecalling quality drops do much. In the example below, I demonstrate the different q scores from the same sample.

I appreciate any help to understand this!
Felipe