This morning I was trouble shooting an upload issue on Ombre CSV data — the reason was “they changed the format again!“. While triaging the issues I saw a lot of counts of “1” in the sample that I was working with. A count of 1 means that only one unit of bacteria was detected. Most microbiologists would deem that to be unreliable, the bacteria may not actually be present, i.e. a “Ghost Bacteria Identification”.
As a result, I look at the 16s tests that has been uploaded to compute the percentages of ghosts in samples.
| 16s Test from | Average | Lowest Rate | Highest Rate | Bacteria Reported |
| Biomesight | 22.1% | 0% | 35.3% | 611 |
| Thryve | 28.8% | 0% | 41.1% | 694 |
| Medivere | 20.5% | 19% | 22.3% | 756 |
| BiomeSightRdp | 11% | 1.9% | 20.0% | 476 |
| CerbaLab | 13.9% | 0% | 24% | Over 600 |
| SequentiaBiotech | 1.4% | 0% | 5% | 313 |
| CosmosId | 0.01% | 0% | 0.28% | 463 |
The numbers above suggests that reporting on ghosts results in more bacteria reports — which is a good marketing strategy. It is a questionable service to the consumers.
For myself, for my offline research database, I will be excluding counts of “1”. I may also offer an option to remove them on the upload page in the future. This is not a significant issue with shotgun reports.
“Buyer beware,” or caveat emptor
From Perplexity (Click to get sources):
In 16S microbiome sequencing, counts of “1” (single read assigned to a taxon in a sample) are generally not considered reliable for determining the true presence of that organism. Here’s why:
- Low-abundance signals (especially a single read) can easily result from sequencing errors, index hopping, cross-contamination, or misclassification in the bioinformatic pipeline.
- Studies show that only OTUs (Operational Taxonomic Units) with higher counts (usually >10 reads, and especially >1% relative abundance) are consistently detected with high reliability and quantification accuracy.
- Single-read taxa are much more likely to be false positives or background noise. They typically do not pass statistical filtering thresholds used in rigorous microbiome analysis.
- Many pipelines recommend removing OTUs present in very low abundances (often <10 reads or <0.1–1% relative abundance) for reliable interpretation.
Summary:
- Counts of “1” should be viewed as unreliable noise and not taken as meaningful evidence of that organism’s presence in your microbiome sample.
- Reliable detection begins at much higher read counts and relative abundances, with reproducibility improving rapidly as counts increase.
Best practices:
- Filter out taxa with extremely low counts for clinical or quantitative interpretation.
- Use statistical and bioinformatic guidelines to set raw count and relative abundance thresholds for reporting results.
If you see a taxon with just one assigned read in your 16S data, consider it an artifact rather than true biological detection unless verified by other means.