A public gene trap resource for mouse functional genomics
Bibliographic record
Abstract
Gene trapping is a high-throughput approach that can be used to introduce insertional mutations across the genome in mouse embryonic stem (ES) cells. Gene trap vectors simultaneously mutate and report the expression of the endogenous gene at the site of insertion and provide a DNA tag for the rapid identification of the disrupted gene. The generation of mutant mice from a large collection of ES cell lines carrying gene trap insertions could be applied to large-scale functional analysis of the ∼ 30,000 mammalian genes. The overall impact of gene trap resources will rest on the fraction of the genome that is accessible with this technology, the efficiency relative to other competing technologies and the availability of such a resource to the academic community. Lexicon Genetics, a US-based biotechnology company, was the first to implement a genome-wide gene trapping program 1 and has developed OmniBank ( http://www.lexicon-genetics.com ), the largest library of mutant ES cell lines. A parallel effort was initiated in the public domain by several academic groups in the International Gene Trap Consortium (IGTC; http://www.igtc.ca ). The recent release of OmniBank sequence tags to GenBank 2 has made it possible to compare the size and efficiency of the existing gene trap libraries.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.066 |
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".