Validation of a universal set of primers to study animal‐associated microeukaryotic communities
Bibliographic record
Abstract
The application of metabarcoding to study animal-associated microeukaryotes has been restricted because the universal barcode used to study microeukaryotic ecology and distribution in the environment, the Small Subunit of the Ribosomal RNA gene (18S rRNA), is also present in the host. As a result, when host-associated microbial eukaryotes are analysed by metabarcoding, the reads tend to be dominated by host sequences. We have done an in silico validation against the SILVA 18S rRNA database of a non-metazoan primer set (primers that are biased against the metazoan 18S rRNA) that recovers only 2.6% of all the metazoan sequences, while recovering most of the other eukaryotes (80.4%). Among metazoans, the non-metazoan primers are predicted to amplify 74% of Porifera sequences, 4% of Ctenophora, and 15% of Cnidaria, while amplifying almost no sequences within Bilateria. In vivo, these non-metazoan primers reduce significantly the animal signal from coral and human samples, and when compared against universal primers provide at worst a 2-fold decrease in the number of metazoan reads and at best a 2800-fold decrease. This easy, inexpensive, and near-universal method for the study of animal-associated microeukaryotes diversity will contribute to a better understanding of the microbiome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".