The Challenge of Choosing a Negative Control: A Behavioural and Morphological Characterization of Various <i>unc-43</i> LOF Alleles in the Model Organism <i>C. elegans</i>
Bibliographic record
Abstract
ABSTRACT Loss-of-Function (LOF) analyses have been pivotal in the discovery of genetic function in biological processes. However, phenotypic variation can occur between different supposedly LOF alleles of the same gene. We investigated the body of research surrounding the nematode ortholog of the Calcium/Calmodulin-dependent protein Kinase II (CAMKII), unc-43 , because of its relevance in a large range of biological functions. Our analysis shows that published findings on unc-43 function were obtained from studies using numerous loss-of-function alleles, leading to potential challenges in aligning research findings. We investigated the similarity of these putative loss-of-function (LOF) mutations by using the Multi-Worm Tracker (Swierczek et al., 2011) to phenotype nine LOF alleles across 27 phenotypes, spanning morphology, baseline locomotion and habituation learning and memory. Interestingly, our study reveals significant differences in phenotypes for these LOF alleles. From our data, we have identified the three putative LOF alleles with the most severe and similar phenotypes propose for two of them ( n498n1186 and js125) to be regarded as reference alleles to streamline future research on unc-43 function. This data will help future researchers optimise their research of unc-43 function and determine how to choose a strain/strains to use as a negative control.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".