Séquences répétées des génomes de <i>Rhizobium</i> sp. NGR234 et <i>Sinorhizobium meliloti</i> : une analyse comparative par séquençage aléatoire
Bibliographic record
Abstract
Amongst prokaryotic genomes, those of nitrogen-fixing members of the Rhizobiaceae family are relatively large (69 Mb), often include mega-plasmids of 1.52 Mb, and contain numerous families of repeated DNA sequences. Although most essential nodulation and nitrogen fixation genes are well characterized, these represent only a small fraction of the DNA content. Little is known about the detailed structure of rhizobial genomes. With the development of sequencing techniques and new bio-informatic tools such studies become possible, however. Using the 2275 shot-gun sequences of ANU265 (a derivative of NGR234 cured of pNGR234a), we have identified numerous families of repeats. Amongst these, the 58-bp-long NGRREP-4 represents the third most abundant DNA sequence after the RIME1 and RIME2 repeats, all of which are also found in Sinorhizobium meliloti. Surprisingly, studies on the distribution of these elements showed that in proportion to its size, the chromosome of NGR234 carries many more RIME modules than pNGR234a or pNGR234b. Together with the presence in NGR234 and S. meliloti 1021 of an insertion sequence (IS) element more conserved than essential nodulation and nitrogen fixation genes, these results give new insights into the origin and evolution of rhizobial genomes.Key words: shot-gun, repeats, BIME.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".