Retro-recombination screening of a mouse embryonic stem cell genomic library
Bibliographic record
Abstract
Targeted gene disruption is an important tool in molecular medicine, allowing for the generation of animal models of human disease. Conventional methods of targeting vector (TV) construction are difficult and represent a rate limiting step in any targeting experiment. We previously demonstrated that bacteriophage are capable of acting as TVs directly, obviating the requirement for 'rolling out' plasmids from primary phage clones and thus eliminating an additional, time consuming step. We have also developed methods which facilitate the construction of TVs using recombination. In this approach, modification cassettes and point mutations are shuttled to specific sites in phage TVs using phage-plasmid recombination. Here, we report a further improvement in TV generation using a recombination screening-based approach deemed 'retro-recombination screening' (RRS). We demonstrate that phage vectors containing specific genomic clones can be genetically isolated from a lambdaTK embryonic stem cell genomic library using a cycle of integrative recombination and condensation. By introducing the gam gene of bacteriophage lambda into the probe plasmid it is possible to select for positive clones which have excised the plasmid, thus returning to their native conformation following purification from the library. Rapid clone isolation using the RRS protocol provides another method by which the time required for TV construction may be further reduced.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".