Pattern and Rate of Indel Evolution Inferred from Whole Chloroplast Intergenic Regions in Sugarcane, Maize and Rice
Bibliographic record
Abstract
Microstructural changes such as insertions and deletions (=indels) are a major driving force in the evolution of non-coding DNA sequences. To better understand the mechanisms by which indel mutations arise, as well as the molecular evolution of non-coding regions, the number and pattern of indels and nucleotide substitutions were compared in the whole chloroplast genomes. Comparisons were made for a total of over 38 kb non-coding DNA sequences from 126 intergenic regions in two data sets representing species with different divergence times: sugarcane and maize and Oryza sativa var. indica and japonica. The main findings of this study are: (i) Approximately half of all indels are single nucleotide indels. This observation agrees with previous studies in various organisms. (ii) The distribution and number of indels was different between two data sets, and different patterns were observed for tandem repeat and non-repeat indels. (iii) Distribution pattern of tandem repeat indels showed statistically significant bias towards A/T-rich. (iv) The rate of indel mutation was estimated to be approximately 0.8 +/- 0.04 x 10(-9) per site per year, which was similar to previous estimates in other organisms. (v) The frequencies of nucleotide substitutions and indels were significantly lower in inverted repeat (IR).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".