Integrating Transcriptome and Metabolome for Seed Quality Improvement in Common Bean
Bibliographic record
Abstract
Common bean ( Phaseolus vulgaris L.) is a cornerstone protein and micronutrient source worldwide, yet improving its multifaceted seed quality traits has outpaced the capabilities of conventional breeding; to address this gap, we synthesize advances from integrating transcriptome and metabolome datasets for trait dissection. We first delineate nutritional attributes-protein, starch, and micronutrients-alongside anti-nutritional and functional metabolites such as tannins, phytates, polyphenols, and processing-related traits including cooking time, texture, flavor; we then review gene-expression dynamics during seed maturation, regulatory networks controlling nutrient deposition, and key transcription factors, in parallel with metabolomic profiles of primary and secondary metabolites and their environmental modulation; subsequently, we detail integrative strategies that correlate expression patterns with metabolite accumulation, employ network and pathway-level models, and nominate candidate biomarkers linked to quality; a comparative case study synthesizes landmark multi-omics investigations, highlighting recurrent pathways-cell-wall remodeling, amino-acid biosynthesis, phenylpropanoid metabolism-while distilling methodological lessons on sampling windows, normalization and batch control, and integration models that bolster interpretability; finally, we examine technical hurdles in data variability and metabolite identification, biological complexities from genotype × environment interactions, and opportunities for machine learning-driven predictive modeling. In summary, integrative omics has matured into a practical toolkit for prioritizing biomarkers and causal pathways that can accelerate marker-assisted and genomic selection; we anticipate near-term translation of multi-omics signals into breeding pipelines via robust, ML-enabled prediction and decision support, and a shift toward pan-omics and precision breeding to sustainably elevate common-bean seed quality under diverse environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".