Little millet genome reveals evolutionary insights into tetraploid structure and genetic basis of micronutrient density
Bibliographic record
Abstract
Little millet is a hardy and nutrient-rich cereal which improves food and nutritional security in marginal environments. Despite its importance, genomic resources for this orphan crop have been limited. Here, we report a high quality, chromosome-scale genome assembly of little millet comprising 18 chromosomes and 59,045 genes. Eleven chromosomes are assembled from telomere to telomere, revealing an 850 Mb tetraploid genome that closely resembles broomcorn millet. Comparative analyses indicate early stages of diploidization, characterized by gene loss and subgenome-specific expression biases that vary across genes and tissues. Resequencing of 300 accessions uncovers extensive genetic diversity, including single-nucleotide polymorphism and structural variants. Genome-wide association studies identify genetic loci linked to grain micronutrient traits, including several associated with high iron content. These genomic and phenotypic resources provide a foundation for molecular breeding and marker-assisted selection, enabling the improvement of little millet as a climate-resilient crop to support global food and nutritional security.
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.
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 | 0.001 |
| 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) | 0.002 | 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".