Scratching the surface of the rare biosphere with ribosomal sequence tag primers
Bibliographic record
Abstract
Increasingly large datasets of 16S rRNA gene sequences reveal new information about the extent of microbial diversity and the surprising extent of the rare biosphere. Currently, many of the largest datasets are represented by short and variable ribosomal sequence tags (RSTs) that are limited in their ability to accurately assign sequences to broad-scale phylogenetic trees. In this study, we selected 30 rare RSTs from existing sequence datasets and designed primers to amplify c. 1400 bases of the 16S rRNA gene to determine whether these sequences were represented by existing databases or if they might reveal new lineages within the Bacteria. Approximately one-third of the RST primers successfully amplified longer portions of these low-abundance 16S rRNA genes in a specific manner. Subsequent phylogenetic analysis demonstrated that most of these sequences were (1) distantly related to existing cultivated microorganisms and (2) closely related to uncultivated clone sequences that were recently deposited in GenBank. The presence of so many recently collected 16S rRNA gene reference sequences in existing databases suggests that progress is being made quickly towards a microbial census, one which has begun scratching the surface of the 'rare biosphere'.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".