An improved suppression subtractive hybridization technique to develop species-specific repetitive sequences from Erianthus arundinaceus (Saccharum complex)
Bibliographic record
Abstract
BACKGROUND: Sugarcane has recently attracted increased attention for its potential as a source of bioethanol and methane. However, a narrow genetic base has limited germplasm enhancement of sugarcane. Erianthus arundinaceus is an important wild genetic resource that has many excellent traits for improving cultivated sugarcane via wide hybridization. Species-specific repetitive sequences are useful for identifying genome components and investigating chromosome inheritance in noblization between sugarcane and E. arundinaceus. Here, suppression subtractive hybridization (SSH) targeting E. arundinaceus-specific repetitive sequences was performed. The five critical components of the SSH reaction system, including enzyme digestion of genomic DNA (gDNA), adapters, digested gDNA concentrations, primer concentrations, and LA Taq polymerase concentrations, were improved using a stepwise optimization method to establish a SSH system suitable for obtaining E. arundinaceus-specific gDNA fragments. RESULTS: Specificity of up to 85.42% was confirmed for the SSH method as measured by reverse dot blot (RDB) of an E. arundinaceus subtractive library. Furthermore, various repetitive sequences were obtained from the E. arundinaceus subtractive library via fluorescence in situ hybridization (FISH), including subtelomeric and centromeric regions. EaCEN2-166F/R and EaSUB1-127F/R primers were then designed as species-specific markers to accurately validate E. arundinaceus authenticity. CONCLUSIONS: This is the first report that E. arundinaceus-specific repetitive sequences were obtained via an improved SSH method. These results suggested that this novel SSH system could facilitate screening of species-specific repetitive sequences for species identification and provide a basis for development of similar applications for other plant species.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".