Large-Scale Screening for Targeted Knockouts in the <i>Caenorhabditis elegans</i> Genome
Bibliographic record
Abstract
The nematode Caenorhabditis elegans is a powerful model system to study contemporary biological problems. This system would be even more useful if we had mutations in all the genes of this multicellular metazoan. The combined efforts of the C. elegans Deletion Mutant Consortium and individuals within the worm community are moving us ever closer to this goal. At present, of the 20,377 protein-coding genes in this organism, 6764 genes with associated molecular lesions are either deletions or null mutations (WormBase WS220). Our three laboratories have contributed the majority of mutated genes, 6841 mutations in 6013 genes. The principal method we used to detect deletion mutations in the nematode utilizes polymerase chain reaction (PCR). More recently, we have used array comparative genome hybridization (aCGH) to detect deletions across the entire coding part of the genome and massively parallel short-read sequencing to identify nonsense, splicing, and missense defects in open reading frames. As deletion strains can be frozen and then thawed when needed, these strains will be an enduring community resource. Our combined molecular screening strategies have improved the overall throughput of our gene-knockout facilities and have broadened the types of mutations that we and others can identify. These multiple strategies should enable us to eventually identify a mutation in every gene in this multicellular organism. This knowledge will usher in a new age of metazoan genetics in which the contribution to any biological process can be assessed for all genes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".