Development of soybean aphid genomic SSR markers using next generation sequencing
Bibliographic record
Abstract
Simple sequence repeats (SSRs) or microsatellites are very useful molecular markers, owing to their locus-specific codominant and multiallelic nature, high abundance in the genome, and high rates of transferability across species. The soybean aphid (Aphis glycines Matsumura) has become the most damaging insect pest of soybean (Glycine max (L.) Merr.) in North America, since it was first found in the Midwest of the United States in 2000. Biotypes of the soybean aphid capable of colonizing newly developed aphid-resistant soybean cultivars have been recently discovered. Genetic resources, including molecular markers, to study soybean aphids are severely lacking. Recently developed next generation sequencing platforms offer opportunities for high-throughput and inexpensive genome sequencing and rapid marker development. The objectives of this study were (i) to develop and characterize genomic SSR markers from soybean aphid genomic sequences generated by next generation sequencing technology and (ii) to evaluate the utility of the SSRs for genetic diversity or relationship analyses. In total 128 SSR primer pairs were designed from sequences generated by Illumina GAII from a reduced representation library of A. glycines. Nearly 94% (120) of the primer pairs amplified SSR alleles of expected size and 24 SSR loci were polymorphic among three aphid samples from three populations. The polymorphic SSRs were successfully used to differentiate among 24 soybean aphids from Ohio and South Dakota. Sequencing of PCR products of two SSR markers from four aphid samples revealed that the allelic polymorphism was due to variation in the SSR repeats among the aphids. These markers should be particularly useful for genetic differentiation among aphids collected from soybean fields at different localities and regions. These SSR markers provide the soybean aphid research community with the first set of PCR-based codominant markers developed from the genomic sequences of A. glycines.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".