Next-generation sequencing based identification of novel resistance genes in wild tomato species
Bibliographic record
Abstract
Wild relatives of tomato harbour a wealth of disease resistance genes that have been largely untapped by modern breeding, mainly because the traditional map-based cloning approach is too slow and expensive for systematic gene discovery across multiple species. This research applied whole-genome sequencing on the Illumina NovaSeq platform to 36 accessions from four wild Solanum species (S. pimpinellifolium, S. habrochaites, S. pennellii, and S. chilense) and four cultivated tomato references at British Columbia Agricultural University and Prairie Agricultural University, Canada, during 2022-2023. A total of 1,490 NBS-LRR (nucleotide-binding site leucine-rich repeat) resistance gene candidates were identified, of which 137 were novel sequences absent from the cultivated tomato reference genome SL4.0. S. pimpinellifolium contributed the largest number of novel candidates (42), concentrated in five gene clusters on chromosomes 2, 4, 6, 9, and 11. Expression analysis by RT-qPCR confirmed that 28 of the 137 novel genes were upregulated in response to Phytophthora infestans challenge. Functional markers were developed for the five largest gene clusters to enable marker-assisted introgression into cultivated backgrounds. These results expand the catalogue of available resistance genes for tomato improvement and demonstrate the efficiency of NGS-based approaches for mining wild germplasm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".