Construction, characterization, and preliminary BAC-end sequencing analysis of a bacterial artificial chromosome library of white clover (Trifolium repens L.)
Bibliographic record
Abstract
White clover (Trifolium repens L.) is a forage legume widely used in combination with grass in pastures because of its ability to fix nitrogen. We have constructed a bacterial artificial chromosome (BAC) library of an advanced breeding line of white clover. The library contains 37 248 clones with an average insert size of approximately 85 kb, representing an approximate 3-fold coverage of the white clover genome based on an estimated genome size of 960 Mb. The BAC library was pooled and screened by polymerase chain reaction (PCR) amplification using both white clover microsatellites and PCR-based markers derived from Medicago truncatula, resulting in an average of 6 hits per marker; this supports the estimated 3-fold genome coverage in this allotetraploid species. PCR-based screening of 766 clones with a multiplex set of chloroplast primers showed that only 0.5% of BAC clones contained chloroplast-derived inserts. The library was further evaluated by sequencing both ends of 724 of the clover BACs. These were analysed with respect to their sequence content and their homology to the contents of a range of plant gene, expressed sequence tag, and repeat element databases. Forty-three microsatellites were discovered in the BAC-end sequences (BESs) and investigated as potential genetic markers in white clover. The BESs were also compared with the partially sequenced genome of the model legume M. truncatula with the specific intention of identifying putative comparative-tile BACs, which represent potential regions of microsynteny between the 2 species; 14 such BACs were discovered. The results suggest that a large-scale BAC-end sequencing strategy has the potential to anchor a significant proportion of the genome of white clover onto the gene-space sequence of M. truncatula.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".