Towards a mutation in every gene in Caenorhabditis elegans
Bibliographic record
Abstract
The combined efforts of the Caenorhabditis elegans Knockout Consortium and individuals within the worm community are moving us closer to the goal of identifying mutations in every gene in the nematode C. elegans. At present, we count about 7000 deletion alleles that fall within 5500 genes. The principal method used to detect deletion mutations in the nematode utilizes polymerase chain reaction (PCR). More recently, the Moerman group has incorporated array comparative genome hybridization (aCGH) to detect deletions across the entire coding genome. Other methods used to detect mutant alleles in C. elegans include targeting induced local lesion in genomes (TILLING), transposon tagging, using either Tc1 or Mos1 and resequencing. These combined strategies have improved the overall throughput of the gene-knockout labs, and have broadened the types of mutations that we, and others, can identify. In this review, we will discuss these different approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".