SCAR Marker for the A Genome of Bananas (Musa spp. L.) Supports Lack of Differentiation between the A and B Genomes
Bibliographic record
Abstract
Bananas (Musa spp. L.) are grouped on the basis of their genomic origins in relation to Musa acuminata (A genome) and M. balbisiana (B genome). The two ancestral wild seeded diploid species evolved in vastly different geographical areas and contributed several agronomic traits towards the present genetic composition of cultivated bananas. Most cultivated bananas are triploid (AAA, AAB and ABB), some are diploid (AA, BB and AB) and a few are tetraploids (AAAA, AAAB, AABB and ABBB). Limitations on the correct identification of the A and B genomes in Musa have generated need for the development of new and more reliable techniques. Distinguishing the A and the B genome remains practically and theoretically important for banana breeders. The aim of the research was to develop a DNA based A genome specific marker for the identification of the A genome in bananas. A putative marker (600 bp) specific to the A genome was identified by Random Amplified Polymorphic DNA (RAPD) technique. A sequence characterised amplified region (SCAR) marker was developed from the RAPD amplicon. The SCAR primers annealed a 500 bp fragment specific to the A genome in a sample of 22 randomly selected homo- and heterogenomic A genome containing accessions representing different genome combinations. The 500 bp SCAR marker is useful for the identification of the A genome. However an additional 700 bp fragment annealed in all M. balbisiana genotypes and in five of the eight heterogenomic accessions, suggesting lack of differentiation between the A and B genome. This study has provided a 500 bp A genome SCAR marker and recent evidence that the A and B genomes of banana may not be as differentiated as previously considered.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".