V-primer: software for the efficient design of genome-wide InDel and SNP markers from multi-sample variant call format (VCF) genotyping data
Bibliographic record
Abstract
DNA markers are indispensable tools in genetics and genomics research as well as in crop breeding, particularly for marker-assisted selection. Recent advances in next-generation sequencing technology have made it easier to obtain genome sequences for various crop species, enabling the large-scale identification of DNA polymorphisms among varieties, which in turn has made DNA marker design more accessible. However, existing primer design software is not suitable for designing many types of genome-wide DNA markers from next-generation sequencing data. Here, we describe the development of V-primer, high-throughput software for designing insertion/deletion, cleaved amplified polymorphic sequence, and single-nucleotide polymorphism (SNP) markers. We validated the applicability of these markers in different crops. In addition, we performed multiplex PCR targeted amplicon sequencing using SNP markers designed with V-primer. Our results demonstrate that V-primer facilitates the efficient and accurate design of primers and is thus a useful tool for genetics, genomics, and crop breeding. V-primer is freely available at https://github.com/ncod3/vprimer.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.030 |
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".