Inference of subgenomes resulting from polyploid events using synteny based dynamic linking and maximum neighbourhood
Bibliographic record
Abstract
Polyploidy is common in flowering plants, resulting in extra sets of chromosomes, known as subgenomes. These events are widespread in plant evolution, making the assignment of synteny blocks to subgenomes challenging due to gene fractionation and gene order rearrangements. Current methods for subgenome identification are labor-intensive and require expertise, lacking automation. To address this challenge, we introduce the SyntenyLink algorithm, which automates subgenome reconstruction from synteny blocks. This algorithm considers differences in substitution and fractionation patterns in synteny blocks and maintains the continuity of gene order. SyntenyLink starts by identifying synteny blocks using BLASTP and DAGchainer, then Automatically partitions them into subgenomes by traversing the maximum weighted path on the "super-synteny graph". We validated the SyntenyLink algorithm using verified subgenomes of Brassica rapa, Brassica oleracea, Brassica nigra, Brassica napus, and Sinapis alba. The results demonstrate its effectiveness, especially for subgenome 1, with accuracy ranging from 82% to 88%. Subgenomes 2 and 3 showed slightly lower accuracy (60%-85%) due to their similar fractionation patterns. Furthermore, we applied SyntenyLink to separate the six subgenomes in Brassica juncea and Brassica carinata, illustrating its versatility. In summary, the SyntenyLink algorithm offers a powerful and automated approach for reconstructing subgenomes in complex polyploid genomes. This advancement has significant implications for studying the evolutionary history of flowering plants and other polyploid organisms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".