Rapid Microsatellite Development for Water Striders by Next-Generation Sequencing
Bibliographic record
Abstract
Water striders have become a model system for studies of sexual conflict and coevolution, but progress is currently limited by a lack of genetic resources. Next-generation sequencing technologies offer the potential for rapid and cost-effective development of molecular markers and hold particular promise for model organisms in ecology for which no reference genome exists. We used Roche 454 sequencing of genomic DNA to identify microsatellite loci for the water strider Gerris incognitus. A modest sequencing volume generated 182,912 reads, of which 30,820 (16.8%) contained microsatellite repeats. We selected 23 loci for primer development, based on criteria that maximized the likelihood of amplifying polymorphic loci, and tested them in G. incognitus and the related species G. buenoi. Of the 16 amplifying loci, 10 yielded reliable amplification and detectable polymorphism, with an average of 6.1 alleles per locus (range: 2-12). These markers should facilitate new avenues of study, including postcopulatory sexual selection, population genetic structure, phylogeography, and sexual coevolution, for a key taxon in studies of mating conflict. The current study demonstrates an effective method for microsatellite development and shows that light sequencing of genomic DNA can provide numerous and highly variable markers.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".