Multi‐individual microsatellite identification: A multiple genome approach to microsatellite design (MiMi)
Bibliographic record
Abstract
Bespoke microsatellite marker panels are increasingly affordable and tractable to researchers and conservationists. The rate of microsatellite discovery is very high within a shotgun genomic data set, but extensive laboratory testing of markers is required for confirmation of amplification and polymorphism. By incorporating shotgun next-generation sequencing data sets from multiple individuals of the same species, we have developed a new method for the optimal design of microsatellite markers. This new tool allows us to increase the rate at which suitable candidate markers are selected by 58% in direct comparisons and facilitate an estimated 16% reduction in costs associated with producing a novel microsatellite panel. Our method enables the visualisation of each microsatellite locus in a multiple sequence alignment allowing several important quality checks to be made. Polymorphic loci can be identified and prioritised. Loci containing fragment-length-altering mutations in the flanking regions, which may invalidate assumptions regarding the model of evolution underlying variation at the microsatellite, can be avoided. Priming regions containing point mutations can be detected and avoided, helping to reduce sample-site-marker specificity arising from genetic isolation, and the likelihood of null alleles occurring. We demonstrate the utility of this new approach in two species: an echinoderm and a bird. Our method makes a valuable contribution towards minimising genotyping errors and reducing costs associated with developing a novel marker panel. The Python script to perform our method of multi-individual microsatellite identification (MiMi) is freely available from GitHub (https://github.com/graemefox/mimi).
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".