Development and Transferability of Black and Red Raspberry Microsatellite Markers from Short-Read Sequences
Bibliographic record
Abstract
The advent of next-generation, or massively parallel sequencing technologies has been a boon to the cost-effective development of molecular markers, particularly in nonmodel species. Here, we demonstrate the efficiency of microsatellite or simple sequence repeat (SSR) marker development from short-read sequences in black and red raspberry ( Rubus occidentalis L. and R. idaeus L., respectively), compare transferability of markers across species, and test whether the rate of polymorphism in the recovered markers can be improved upon by how marker sequences are chosen. From 28,536,412 black raspberry reads and 27,430,159 reads in red raspberry, we identified more than 6000 SSR sequences in each species and selected 288 of these (144 from each species), for testing in black and red raspberry. A total of 166 SSR primer pairs were identified with informative polymorphism in one or both species. SSRs selected based on different percentages (90% to 97% as compared with ≥98%) of read cluster similarity did not differ in polymorphism rates from each other or from those originating from singletons. Efficiency of polymorphic SSR recovery was nearly twice as high in black raspberry from black raspberry-derived sequences as from red raspberry-derived sequences, while efficiency of polymorphic SSR recovery in red raspberry was unaffected by the source of the primer sequences. Development of SSR markers that are transferable between red and black raspberry for marker-assisted selection, evaluation of genome collinearity and to facilitate comparative studies in Rubus L. will be more efficient using SSR markers developed from black raspberry sequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".