PCR‐Based Diversity Estimates of Artificial and Environmental 18S rRNA Gene Libraries
Bibliographic record
Abstract
Environmental clone libraries constructed using small subunit ribosomal RNA (rRNA) or other gene-specific primers have become the standard molecular approach for identifying microorganisms directly from their environment. This technique includes an initial polymerase chain reaction (PCR) amplification step of a phylogenetically useful marker gene using universal primers. Although it is acknowledged that such primers introduce biases, there have been few studies if any to date systematically examining such bias in eukaryotic microbes. We investigated some implications of such bias by constructing clone libraries using several universal primer pairs targeting rRNA genes. Firstly, we constructed artificial libraries using a known mix of small cultured pelagic arctic algae with representatives from five major lineages and secondly we investigated environmental samples using several primer pairs. No primer pair retrieved all of the original algae in the artificial clone libraries and all showed a favorable bias toward the dinoflagellate Polarella glacialis and a bias against the prasinophyte Micromonas and a pennate diatom. Several other species were retrieved by only one primer pair tested. Despite this, sequences from nine environmental libraries were diverse and contained representatives from all major eukaryotic clades expected in marine samples. Further, libraries from the same sample grouped together using Bray-Curtis clustering, irrespective of primer pairs. We conclude that environmental PCR-based techniques are sufficient to compare samples, but the total diversity will probably always be underestimated and relative abundance estimates should be treated with caution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".