S<scp>ub</scp>P<scp>haser</scp>: a robust allopolyploid subgenome phasing method based on subgenome‐specific <i>k</i>‐mers
Bibliographic record
Abstract
With advanced sequencing technology, dozens of complex polyploid plant genomes have been characterized. However, for many polyploid species, their diploid ancestors are unknown or extinct, making it impossible to unravel the subgenomes and genome evolution directly. We developed a novel subgenome-phasing algorithm, SubPhaser, specifically designed for a neoallopolyploid or a homoploid hybrid. SubPhaser first searches for the subgenome-specific sequence (k-mer), then assigns homoeologous chromosomes into subgenomes, and further provides tools to annotate and investigate specific sequences. SubPhaser works well on neoallopolyploids and homoploid hybrids containing subgenome-specific sequences like wheat, but fails on autopolyploids lacking subgenome-specific sequences like alfalfa, indicating that SubPhaser can phase neoallopolyploid/homoploid hybrids with high accuracy, sensitivity and performance. This highly accurate, highly sensitive, ancestral data free chromosome phasing algorithm, SubPhaser, offers significant application value for subgenome phasing in neoallopolyploids and homoploid hybrids, and for the subsequent exploration of genome evolution and related genetic/epigenetic mechanisms.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".