TOMATOMA: A Novel Tomato Mutant Database Distributing Micro-Tom Mutant Collections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The tomato is an excellent model for studies of plants bearing berry-type fruits and for experimental studies of the Solanaceae family of plants due to its conserved genetic organization. In this study, a comprehensive mutant tomato population was generated in the background of Micro-Tom, a dwarf, rapid-growth variety. In this and previous studies, a family including 8,598 and 6,422 M(2) mutagenized lines was produced by ethylmethane sulfonate (EMS) mutagenesis and γ-ray irradiation, and this study developed and investigated these M(2) plants for alteration of visible phenotypes. A total of 9,183 independent M(2) families comprising 91,830 M(2) plants were inspected for phenotypic alteration, and 1,048 individual mutants were isolated. Subsequently, the observed mutant phenotypes were classified into 15 major categories and 48 subcategories. Overall, 1,819 phenotypic categories were found in 1,048 mutants. Of these mutants, 549 were pleiotropic, whereas 499 were non-pleiotropic. Multiple different mutant alleles per locus were found in the mutant libraries, suggesting that the mutagenized populations were nearly saturated. Additionally, genetic analysis of backcrosses indicated the successful inheritance of the mutations in BC(1)F(2) populations, confirming the reproducibility in the morphological phenotyping of the M(2) plants. To integrate and manage the visible phenotypes of mutants and other associated data, we developed the in silico database TOMATOMA, a relational system interfacing modules between mutant line names and phenotypic categories. TOMATOMA is a freely accessible database, and these mutant recourses are available through the TOMATOMA (http://tomatoma.nbrp.jp/index.jsp).
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.
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 it