Genetic Diversity Analysis with 454 Pyrosequencing and Genomic Reduction Confirmed the Eastern and Western Division in the Cultivated Barley Gene Pool
Bibliographic record
Abstract
Next-generation DNA sequencing (NGS) technologies can survey sequence variation on a genome-wide scale, but their utility for crop genetic diversity analysis is poorly known. Many challenges remain in their applications, including sampling complex genomes, identifying single nucleotide polymorphisms (SNPs), and analyzing missing data. This study presented a practical application of the Roche 454 GS FLX Titanium technology in combination with genomic reduction and an advanced bioinformatics tool to analyze the genetic relationships of 16 diverse barley (Hordeum vulgare L.) landraces. A full 454 run generated roughly 1.7 million sequence reads with a total length of 612 Mbp. Application of the computational pipeline called DIAL (de novo identification of alleles) identified 2578 contigs and 3980 SNPs. Sanger sequencing of four barley samples confirmed 85 of the 100 selected contigs and 288 of the 620 putative SNPs and identified 735 new SNPs and 39 new indels. Several diversity analyses revealed the eastern and western division in the barley samples. The division is compatible with those inferred with 156 microsatellite alleles of the same 16 samples and consistent with our current knowledge about cultivated barley. These results help to illustrate the utility of NGS technologies for crop diversity studies. The NGS application also provides a new informative set of genomic resources for barley research.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".