Mapping <i>Drosophila</i> mutations with molecularly defined <i>P</i> element insertions
Bibliographic record
Abstract
The isolation of chemically induced mutations in forward genetic screens is one of the hallmarks of Drosophila genetics. However, mapping the corresponding loci and identifying the molecular lesions associated with these mutations are often difficult and labor-intensive. Two mapping methods are most often used in flies: meiotic recombination mapping with marked chromosomes and deficiency mapping. The availability of the fly genome sequence allows the establishment and usage of molecular markers. Single-nucleotide polymorphisms have therefore recently been used to map several genes. Here we show that thousands of molecularly mapped P element insertions in fly strains that are publicly available provide a powerful alternative method to single-nucleotide polymorphism mapping. We present a strategy that allows mapping of lethal mutations, as well as viable mutations with visible phenotypes, with minimal resources. The most important unknown in using recombination rates to map at high resolution is how accurately recombination data correlate with molecular maps in small intervals. We therefore surveyed distortions of recombination rates in intervals <500 kb. We document the extent of distortions between the recombination and molecular maps and describe the required steps to map with an accuracy of <50 kb. Finally, we describe a recently developed method to determine molecular lesions in 50-kb intervals by using a heteroduplex DNA mutation detection system. Our data show that this mapping approach is inexpensive, efficient, and precise, and that it significantly broadens the application of P elements in Drosophila.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".