Faculty Opinions recommendation of Knocking out multigene redundancies via cycles of sexual assortment and fluorescence selection.
Bibliographic record
Abstract
Phenotypes that might otherwise reveal a gene's function can be obscured by genes with overlapping function. This phenomenon is best known within gene families, in which an important shared function may only be revealed by mutating all family members. Here we describe the 'green monster' technology that enables precise deletion of many genes. In this method, a population of deletion strains with each deletion marked by an inducible green fluorescent protein reporter gene, is subjected to repeated rounds of mating, meiosis and flow-cytometric enrichment. This results in the aggregation of multiple deletion loci in single cells. The green monster strategy is potentially applicable to assembling other engineered alterations in any species with sex or alternative means of allelic assortment. To test the technology, we generated a single broadly drug-sensitive strain of Saccharomyces cerevisiae bearing precise deletions of all 16 ATP-binding cassette transporters within clades associated with multidrug resistance. PMID: 21217751 Funding information This work was supported by: NCI NIH HHS, United States Grant ID: R21 CA130266-02 NCI NIH HHS, United States Grant ID: R21 CA130266-01A1 NCI NIH HHS, United States Grant ID: R21 CA130266 NCI NIH HHS, United States Grant ID: R21 CA130266 NHGRI NIH HHS, United States Grant ID: R01 HG003224-05 NHGRI NIH HHS, United States Grant ID: R01 HG003224-04 NHGRI NIH HHS, United States Grant ID: R01 HG003224-03 NHGRI NIH HHS, United States Grant ID: R01 HG003224-02 NHGRI NIH HHS, United States Grant ID: R01 HG003224-01A2 NHGRI NIH HHS, United States Grant ID: R01 HG003224 NHGRI NIH HHS, United States Grant ID: R01 HG003224 More Less keyboard_arrow_down
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.577 | 0.351 |
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".