Genome-wide identification and characterization of nucleotide binding site leucine-rich repeat genes in linseed reveal distinct patterns of gene structure
Bibliographic record
Abstract
Plants employ different disease-resistance genes to detect pathogens and to induce defense responses. The largest class of these genes encodes proteins with nucleotide binding site (NBS) and leucine-rich repeat (LRR) domains. To identify the putative NBS-LRR encoding genes from linseed, we analyzed the recently published linseed genome sequence and identified 147 NBS-LRR genes. The NBS domain was used for phylogeny construction and these genes were classified into two well-known families, non-TIR (CNL) and TIR related (TNL), and formed eight clades in the neighbor-joining bootstrap tree. Eight different gene structures were observed among these genes. An unusual domain arrangement was observed in the TNL family members, predominantly in the TNL-5 clade members belonging to class D. About 12% of the genes observed were linseed specific. The study indicated that the linseed genes probably have an ancient origin with few progenitor genes. Quantitative expression analysis of five genes showed inducible expression. The in silico expression evidence was obtained for a few of these genes, and the expression was not correlated with the presence of any particular regulatory element or with unusual domain arrangement in those genes. This study will help in understanding the evolution of these genes, the development of disease resistant varieties, and the mechanism of disease resistance in linseed.
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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 | 0.000 |
| 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) | 0.000 | 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".