Evaluation of rice and sugarcane SSR markers for phylogenetic and genetic diversity analyses in bambooIHBT Publication No. 0732.
Bibliographic record
Abstract
Simple sequence repeat (SSR) markers are valuable tools for many purposes such as phylogenetic, fingerprinting, and molecular breeding studies. However, only a few SSR markers are known and available in bamboo species of the tropics (Bambusa spp.). Considering that grass genomes have co-evolved and share large-scale synteny, theoretically it should be possible to use the genome sequence based SSR markers of field crops such as rice (Oryza sativa) and sugarcane (Saccharum spp.) for genome analysis in bamboo. To test this, 98 mapped SSR primers representing 12 linkage groups of rice and 20 EST-derived sugarcane SSR primers were evaluated for transferability to 23 bamboo species. Of the tested markers, 44 (44.9%) rice and 15 (75%) sugarcane SSR primers showed repeatable amplification in at least one species of bamboo and thus were successfully utilized for phylogenetic and genetic diversity analyses. Transferred SSR primers revealed complex amplification patterns in bamboo, with an average of 9.62 fragments per primer, indicating a high level of polyploidy and genetic variability in bamboo. Forty-two of these primers (34 rice and 8 sugarcane SSR primers) detected an average of 2.12 unique fragments per primer and thus could be exploited for species identification. Six bamboo SSR primers exhibited cross transferability, to varying degrees, to different bamboo species. The genetic similarity coefficient indicated a high level of divergence at the species level (73%). However, a relatively low level of diversity was observed within species (25% in 20 accessions of Dendrocalamus hamiltonii). Further, cluster analysis revealed that the major grouping was in accordance with the taxonomical classification of bamboo. Thus, the rice and sugarcane SSRs can be utilized for phylogenetic and genetic diversity studies in bamboo.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".