Gene targeting using a promoterless gene trap vector (“targeted trapping”) is an efficient method to mutate a large fraction of genes
Bibliographic record
Abstract
A powerful tool for postgenomic analysis of mammalian gene function is gene targeting in mouse ES cells. We report that homologous recombination using a promoterless gene trap vector ("targeting trapping") yields targeting frequencies averaging above 50%, a significant increase compared with current approaches. These high frequencies appear to be due to the stringency of selection with promoterless constructs, because most random insertions are silent and eliminated by drug selection. The promoterless design requires that the targeted gene be expressed in ES cells at levels exceeding a certain threshold (which we estimate to be approximately 1% of the transferrin receptor gene expression level, for the secretory trap vector used here). Analysis of 127 genes that had been trapped by random (nontargeted) gene trapping with the same vector shows that virtually all are expressed in ES cells above this threshold, suggesting that targeted and random trapping share similar requirements for expression levels. In a random sampling of 130 genes encoding secretory proteins, about half were expressed above threshold, suggesting that about half of all secretory genes are accessible by either targeted or random gene trapping. The simplicity and high efficiency of the method facilitate systematic targeting of a large fraction of the genome by individual investigators and large-scale consortia alike.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".