DNAMarkMaker: streamlining ARMS and CAPS marker development from resequencing data with NGS short reads
Bibliographic record
Abstract
DNA markers serve as essential tools in breeding selection and genetic analysis. However, developing DNA markers can be time-consuming and labor-intensive due to the need to identify polymorphisms between cultivars/lines and to design suitable primers. To address these challenges, we have developed DNAMarkMaker, a tool designed to automate the process of primer design for Amplification Refractory Mutation System (ARMS) and Cleaved Amplified Polymorphic Sequences (CAPS) markers, utilizing resequencing data. One key feature of DNAMarkMaker is its user-friendly graphical user interface (GUI), ensuring its accessibility and ease of use, even for researchers not well-versed in bioinformatics. We confirmed DNAMarkMaker's applicability by developing DNA markers for rice, potato, and turnip-each representing distinct genome structures: homozygous diploid, heterozygous autotetraploid, and heterozygous diploid, respectively. DNAMarkMaker will contribute to the rapid and efficient development of DNA markers, accelerating breeding and genetic analysis in various crops.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".