Hi @lca123,
This is the scary sort of thing that happens all the time whether we are looking or not. Now you've just had the opportunity to experience these errors first-hand!
An additional possibility is sequence error in the barcodes causing one barcode to be misread as another.
You have a few choices:
- ignore and move on. It is unlikely that you can really thoroughly remove these errors from your real data, but the read counts are so low that these inherent errors likely will have no impact (incidentally, this is another reason why we use abundance filters to exclude samples with low read counts — and similarly another reason why negative controls will often register a few reads, such as the negative control that I see in your run).
- You could use these fake samples as quasi-negative controls and try to see if there are trends in what ASVs/taxa are observed, and potentially use that to filter your real samples if these appear to be obvious contaminants and not cross-contaminants.
- You can examine the barcode PHRED scores to see if there are any trends, e.g., lower quality scores as described in this paper that you can use as a threshold to exclude low-quality reads (not currently supported in QIIME 2).