Application of denaturing high-performance liquid chromatography for mapping of single nucleotide polymorphisms in barley (<i>Hordeum vulgare</i> L.)
Bibliographic record
Abstract
Recent advances in DNA sequence analysis and the establishment of high-throughput assays have provided the framework for large-scale discovery and analysis of DNA sequence variation. In this context, single nucleotide polymorphisms (SNPs) are of particular interest. To initiate a systematic approach to develop an SNP map of barley (Hordeum vulgare L.), we have employed denaturing high-performance liquid chromatography (DHPLC) to analyse segregating SNP patterns in a doubled-haploid (DH) mapping population. To this end, SNPs between the parental genotypes were identified using a direct sequencing approach. Once a SNP was established between the parents, the optimal melting temperature of the PCR fragment containing the SNP was predicted for its analysis by DHPLC. Following the detection of the optimal temperature, the DH lines were analysed for the presence of either of the alleles. To test the utility of the analysis, data from previously mapped RFLP markers from which these SNPs were derived were compared. Results from these experiments indicate that DHPLC can be efficiently employed in analysing SNPs on a high-throughput scale.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".