Comparative Evaluation of Four Bacteria-Specific Primer Pairs for 16S rRNA Gene Surveys
Bibliographic record
Abstract
Bacterial taxonomic community analyses using PCR-amplification of the 16S rRNA gene and high-throughput sequencing has become a cornerstone in microbiology research. To reliably detect the members, or operational taxonomic units (OTUs), that make up bacterial communities, taxonomic surveys rely on the use of the most informative PCR primers to amplify the broad range of phylotypes present in up-to-date reference databases. However, primers specific for the domain Bacteria were often developed some time ago against database versions that are now out of date. Here we evaluated the performance of four bacterial primers on complex microbial communities of an explosives contaminated and non-contaminated forest soil and by in silico evaluation against the current SILVA123 database. Primer pair 341f/785r produced the highest number of bacterial OTUs, phylogenetic richness, Shannon diversity, low non-specificity and most reproducible results, followed by 967f/1391r and 799f/1193r. Primer pair 68f/518r showed overall low coverage and a bias towards Alphaproteobacteria. The primer pair 341f/785r showed also in silico the highest coverage of the domain Bacteria (96.1 %) with no obvious bias towards the majority of bacterial species. This suggests the high utility of primer pair 341f/785r for soil and plant-associated bacterial microbiome studies.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".