Genome-wide DNA polymorphism in the <i>indica</i> rice varieties RGD-7S and Taifeng B as revealed by whole genome re-sequencing
Bibliographic record
Abstract
Next-generation sequencing technologies provide opportunities to further understand genetic variation, even within closely related cultivars. We performed whole genome resequencing of two elite indica rice varieties, RGD-7S and Taifeng B, whose F1 progeny showed hybrid weakness and hybrid vigor when grown in the early- and late-cropping seasons, respectively. Approximately 150 million 100-bp pair-end reads were generated, which covered ∼86% of the rice (Oryza sativa L. japonica 'Nipponbare') reference genome. A total of 2,758,740 polymorphic sites including 2,408,845 SNPs and 349,895 InDels were detected in RGD-7S and Taifeng B, respectively. Applying stringent parameters, we identified 961,791 SNPs and 46,640 InDels between RGD-7S and Taifeng B (RGD-7S/Taifeng B). The density of DNA polymorphisms was 256.8 SNPs and 12.5 InDels per 100 kb for RGD-7S/Taifeng B. Copy number variations (CNVs) were also investigated. In RGD-7S, 1989 of 2727 CNVs were overlapped in 218 genes, and 1231 of 2010 CNVs were annotated in 175 genes in Taifeng B. In addition, we verified a subset of InDels in the interval of hybrid weakness genes, Hw3 and Hw4, and obtained some polymorphic InDel markers, which will provide a sound foundation for cloning hybrid weakness genes. Analysis of genomic variations will also contribute to understanding the genetic basis of hybrid weakness and heterosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".