Genomic resources of broomcorn millet: demonstration and application of a high-throughput BAC mapping pipeline
Bibliographic record
Abstract
BACKGROUND: With high-efficient water-use and drought tolerance, broomcorn millet has emerged as a candidate for food security. To promote its research process for molecular breeding and functional research, a comprehensive genome resource is of great importance. RESULTS: Herein, we constructed a BAC library for broomcorn millet, generated BAC end sequences based on the clone-array pooled shotgun sequencing strategy and Illumina sequencing technology, and integrated BAC clones into genome by a novel pipeline for BAC end profiling. The BAC library consisted of 76,023 clones with an average insert length of 123.48 Kb, covering about 9.9-fold of the 850 Mb genome. Of 9216 clones tested using our pipeline, 8262 clones were mapped on the broomcorn millet cultivar longmi4 genome. These mapped clones covered 308 of the 829 gaps left by the genome. To our knowledge, this is the only BAC resource for broomcorn millet. CONCLUSIONS: We constructed a high-quality BAC libraray for broomcorn millet and designed a novel pipeline for BAC end profiling. BAC clones can be browsed and obtained from our website ( http://eightstarsbio.com/gresource/JBrowse-1.16.5/index.html ). The high-quality BAC clones mapped on genome in this study will provide a powerful genomic resource for genome gap filling, complex segment sequencing, FISH, functional research and genetic engineering of broomcorn millet.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".