Soybean genomic survey: BAC-end sequences near RFLP and SSR markers
Bibliographic record
Abstract
We are building a framework physical infrastructure across the soybean genome by using SSR (simple sequence repeat) and RFLP (restriction fragment length polymorphism) markers to identify BACs (bacterial artificial chromosomes) from two soybean BAC libraries. The libraries were prepared from two genotypes, each digested with a different restriction enzyme. The BACs identified by each marker were grouped into contigs. We have obtained BAC- end sequence from BACs within each contig. The sequences were analyzed by the University of Minnesota Center for Computational Genomics and Bioinformatics using BLAST algorithms to search nucleotide and protein databases. The SSR-identified BACs had a higher percentage of significant BLAST hits than did the RFLP-identified BACs. This difference was due to a higher percentage of hits to repetitive-type sequences for the SSR-identified BACs that was offset in part, however, by a somewhat larger proportion of RFLP-identified significant hits with similarity to experimentally defined genes and soybean ESTs (expressed sequence tags). These genes represented a wide range of metabolic functions. In these analyses, only repetitive sequences from SSR-identified contigs appeared to be clustered. The BAC-end sequences also allowed us to identify microsynteny between soybean and the model plants Arabidopsis thaliana and Medicago truncatula. This map-based approach to genome sampling provides a means of assaying soybean genome structure and organization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".