Pinning down ploidy in paleopolyploid plants
Bibliographic record
Abstract
BACKGROUND: Fractionation is the genome-wide process of losing one gene per duplicate pair following whole genome multiplication (doubling, tripling, …). This is important in the evolution of plants over tens of millions of years, because of their repeated cycles of genome multiplication and fractionation. One type of evidence in the study of these processes is the frequency distribution of similarities between the two genes, over all the duplicate pairs in the genome. RESULTS: We study modeling and inference problems around the processes of fractionation and whole genome multiplication focusing first on the frequency distribution of similarities of duplicate pairs in the genome. Our birth-and-death model accounts for repeated duplication, triplication or other multiplication events, as well as fractionation rates among multiple progeny of a single gene specific to each event. It also has a biologically and combinatorially well-motivated way of handling the tendency for at least one sibling to survive fractionation. The method settles previously unexplored questions about the expected number of gene pairs tracing their ancestry back to each multiplication event. We exemplify the algebraic concepts inherent in our models and on Brassica rapa, whose evolutionary history is well-known. We demonstrate the quantitative analysis of high-similarity gene pairs and triples to confirm the known ploidies of events in the lineage of B. rapa. CONCLUSIONS: Our birth-and-death model accounts for the similarity distribution of paralogs in terms of multiple rounds of whole genome multiplication and fractionation. An analysis of high-similarity gene triples confirms the recent Brassica triplication.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".