Sesame Genomic Web Resource (SesameGWR): a well-annotated data resource for transcriptomic signatures of abiotic and biotic stress responses in sesame (<i>Sesamum indicum</i> L.)
Bibliographic record
Abstract
Sesame (Sesamum indicum L.) is a globally cultivated oilseed crop renowned for its historical significance and widespread growth in tropical and subtropical regions. With notable nutritional and medicinal attributes, sesame has shown promising effects in combating malnutrition cancer, diabetes, and other diseases like cardiovascular problems. However, sesame production faces significant challenges from environmental threats such as charcoal rot, drought, salinity, and waterlogging stress, resulting in economic losses for farmers. The scarcity of information on stress-resistance genes and pathways exacerbates these challenges. Despite its immense importance, there is currently no platform available to provide comprehensive information on sesame, which significantly hinders the mining of various stress-associated genes and the molecular breeding of sesame. To address this gap, here a free, web-accessible, and user-friendly genomic web resource (SesameGWR, http://backlin.cabgrid.res.in/sesameGWR/) has been developed This platform provides key insights into differentially expressed genes, transcription factors, miRNAs, and molecular markers like simple sequence repeats, single nucleotide polymorphisms, and insertions and deletions associated with both biotic and abiotic stresses.. The functional genomics information and annotations embedded in this web resource were predicted through RNA-seq data analysis. Considering the impact of climate change and the nutritional and medicinal importance of sesame, this study is of utmost importance in understanding stress responses. SesameGWR will serve as a valuable tool for developing climate-resilient sesame varieties, thereby enhancing the productivity of this ancient oilseed crop.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.023 |
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".