Promoter Cre‐Specific Genotyping Assays for Authentication of Cre‐Driver Mouse Lines
Bibliographic record
Abstract
ABSTRACT The Cre‐LoxP system gene knockout (KO) technology provides cell‐ and time‐specificity of gene ablation to investigate cell‐autonomous gene function in vivo, and is paramount for understanding the function of genes involved in bone development, remodeling, and repair. This approach permits gene ablation in a cell‐ or tissue‐specific, differentiation stage‐specific, and inducible manner, thanks to the use of well‐chosen promoters that drive expression of the Cre recombinase in selected cells/tissues. The generation of these powerful tools has led to the expansion of Cre mouse lines available to the research community, which are often shared within and between laboratories. Although convenient and commonly used, genotyping these Cre lines with a generic set of primers that amplifies the Cre transgene does not distinguish between various Cre‐deleter lines. This practice poses the significant risk of mistakenly swapping Cre lineages, as laboratories often host and handle several lines at a time and utilize multiple lines per project. In line with the NIH‐led effort to promote authentication of biological reagents and increase scientific rigor, we report here strategies for designing appropriate sets of primers able to discriminate some of most widely used Cre‐deleter mouse lines in the field of bone biology, and the validation of 24 of them. © 2018 The Authors JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.015 |
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".