Analyses of transcriptome sequences reveal multiple ancient large‐scale duplication events in the ancestor of Sphagnopsida (Bryophyta)
Bibliographic record
Abstract
The goal of this research was to investigate whether there has been a whole-genome duplication (WGD) in the ancestry of Sphagnum (peatmoss) or the class Sphagnopsida, and to determine if the timing of any such duplication(s) and patterns of paralog retention could help explain the rapid radiation and current ecological dominance of peatmosses. RNA sequencing (RNA-seq) data were generated for nine taxa in Sphagnopsida (Bryophyta). Analyses of frequency plots for synonymous substitutions per synonymous site (Ks ) between paralogous gene pairs and reconciliation of 578 gene trees were conducted to assess evidence of large-scale or genome-wide duplication events in each transcriptome. Both Ks frequency plots and gene tree-based analyses indicate multiple duplication events in the history of the Sphagnopsida. The most recent WGD event predates divergence of Sphagnum from the two other genera of Sphagnopsida. Duplicate retention is highly variable across species, which might be best explained by local adaptation. Our analyses indicate that the last WGD could have been an important factor underlying the diversification of peatmosses and facilitated their rise to ecological dominance in peatlands. The timing of the duplication events and their significance in the evolutionary history of peat mosses are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".