Genome-wide DNA polymorphisms in Kavuni, a traditional rice cultivar with nutritional and therapeutic properties
Bibliographic record
Abstract
Although rice genome was sequenced in the year 2002, efforts in resequencing the large number of available accessions, landraces, traditional cultivars, and improved varieties of this important food crop are limited. We have initiated resequencing of the traditional cultivars from India. Kavuni is an important traditional rice cultivar from South India that attracts premium price for its nutritional and therapeutic properties. Whole-genome sequencing of Kavuni using Illumina platform and SNPs analysis using Nipponbare reference genome identified 1 150 711 SNPs of which 377 381 SNPs were located in the genic regions. Non-synonymous SNPs (62 708) were distributed in 19 251 genes, and their number varied between 1 and 115 per gene. Large-effect DNA polymorphisms (7769) were present in 3475 genes. Pathway mapping of these polymorphisms revealed the involvement of genes related to carbohydrate metabolism, translation, protein-folding, and cell death. Analysis of the starch biosynthesis related genes revealed that the granule-bound starch synthase I gene had T/G SNPs at the first intron/exon junction and a two-nucleotide combination, which were reported to favour high amylose content and low glycemic index. The present study provided a valuable genomics resource to study the rice varieties with nutritional and medicinal properties.
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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 | 0.000 |
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".