Bibliographic record
Abstract
About 40% of the genes in the nematode Caenorhabditis elegans have homologs in humans. Based on the history of this model system, it is clear that the application of genetic methods to the study of this set of genes would provide important clues to their function in humans. To facilitate such genetic studies, we are engaged in a project to derive deletion alleles in every gene in this set. Our standard methods make use of nested PCR to hunt for animals in mutagenized populations that carry deletions at a given locus. The deletion bearing animals exist initially in mixed populations where the majority of the animals are wild type at the target. Therefore, the production of the PCR fragment representing the deletion allele competes with the production of the wild type fragment. The size of the deletion fragment relative to wild type determines whether it can compete to a level where it can be detected above the background. Using our standard conditions, we have found that when the deletion is <600 bp, the deletion fragment does not compete effectively with the production of the wild type fragment in PCR. Therefore, although our standard methods work well to detect mutants with deletions >600 bp, they do not work well to detect mutants with smaller deletions. Here we report a new strategy to detect small deletion alleles in complex DNA pools. Our new strategy is a modification of our standard PCR based screens. In the first round of the nested PCR, we include a third PCR primer between the two external primers. The presence of this third primer leads to the production of three fragments from wild type DNA. We configure the system so that two of these three fragments cannot serve as a template in the second round of the nested PCR. The addition of this third primer, therefore, handicaps the amplification from wild type template. On the other hand, the amplification of mutant fragments where the binding site for the third primer is deleted is unabated. Overall, we see at least a 500-fold increase in the sensitivity for small deletion fragments using our new method. Using this new method, we report the recovery of new deletion alleles within 12 C.elegans genes.
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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".