Microsatellite discovery from BAC end sequences and genetic mapping to anchor the soybean physical and genetic maps
Bibliographic record
Abstract
Whole-genome sequencing of the soybean (Glycine max (L.) Merr. 'Williams 82') has made it important to integrate its physical and genetic maps. To facilitate this integration of maps, we screened 3290 microsatellites (SSRs) identified from BAC end sequences of clones comprising the 'Williams 82' physical map. SSRs were screened against 3 mapping populations. We found the AAT and ACT motifs produced the greatest frequency of length polymorphisms, ranging from 17.2% to 32.3% and from 11.8% to 33.3%, respectively. Other useful motifs include the dinucleotide repeats AG, AT, and AG, with frequency of length polymorphisms ranging from 11.2% to 18.4% (AT), 12.4% to 20.6% (AG), and 11.3% to 16.4% (GT). Repeat lengths less than 16 bp were generally less useful than repeat lengths of 40-60 bp. Two hundred and sixty-five SSRs were genetically mapped in at least one population. Of the 265 mapped SSRs, 60 came from BAC singletons not yet placed into contigs of the physical map. One hundred and ten originated in BACs located in contigs for which no genetic map location was previously known. Ninety-five SSRs came from BACs within contigs for which one or more other BACs had already been mapped. For these fingerprinted contigs (FPC) a high percentage of the mapped markers showed inconsistent map locations. A strategy is introduced by which physical and genetic map inconsistencies can be resolved using the preliminary 4x assembly of the whole genome sequence of soybean.
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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) | 0.000 | 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".