Development of genome-wide InDel markers and their integration with SSR, DArT and SNP markers in single barley map
Bibliographic record
Abstract
BACKGROUND: Development of molecular markers such as SSR (simple sequence repeat), DArT (diversity arrays technology) and SNP (single nucleotide polymorphism) is fundamental for linkage map construction and QTL mapping. However, DArT and SNP genotyping require special tools, and detection of SSR polymorphisms requires time-consuming polyacrylamide electrophoresis. Furthermore, many markers have been mapped in different populations such that their genetic positions are inconsistent. Recently, InDel (insertion and deletion) markers have become popular in genetic map construction and map-based cloning. RESULTS: Aligning genomic DNA sequences in two barley cultivars (Morex and Barke) identified 436,640 InDels. We designed 1140 InDel markers across the barley genome with an average genetic distance of 1 cM, each having a unique location in the barley genome. High-resolution melting (HRM) technology was used to genotype 55 InDel markers; those PCR amplicons with melting temperature differences >0.3 °C were ideal for HRM genotyping. The 1140 InDel markers together with 383 SSRs, 3909 gene-based SNPs and 1544 DArT markers were integrated into single barley genetic map according to their physical map positions. CONCLUSIONS: High-density InDel markers with specific genome locations were developed, with 6976 molecular markers (SSRs, DArTs, SNPs and InDels) integrated into single barley genetic map. HRM genotyping of the InDel markers each with single PCR band will facilitate quick map construction and gene fine-mapping.
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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 | 0.000 | 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".