A diversity of uncharacterized reverse transcriptases in bacteria
Bibliographic record
Abstract
Retroelements are usually considered to be eukaryotic elements because of the large number and variety in eukaryotic genomes. By comparison, reverse transcriptases (RTs) are rare in bacteria, with only three characterized classes: retrons, group II introns and diversity-generating retroelements (DGRs). Here, we present the results of a bioinformatic survey that aims to define the landscape of RTs across eubacterial, archaeal and phage genomes. We identify and categorize 1021 RTs, of which the majority are group II introns (73%). Surprisingly, a plethora of novel RTs are found that do not belong to characterized classes. The RTs have 11 domain architectures and are classified into 20 groupings based on sequence similarity, phylogenetic analyses and open reading frame domain structures. Interestingly, group II introns are the only bacterial RTs to exhibit clear evidence for independent mobility, while five other groups have putative functions in defense against phage infection or promotion of phage infection. These examples suggest that additional beneficial functions will be discovered among uncharacterized RTs. The study lays the groundwork for experimental characterization of these highly diverse sequences and has implications for the evolution of retroelements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".