Signature-tagged Mutagenesis to Characterize Genes Through Competitive Selection of Bar-coded Genome Libraries
Bibliographic record
Abstract
The availability of collections of genome-wide deletion mutants greatly accelerates systematic analyses of gene function. However, each of the thousands of genes that comprise a genome must be phenotyped individually unless they can be assayed in parallel and subsequently deconvolved. To this end, unique molecular identifiers have been developed for a variety of microbes. Specifically, the addition of DNA "tags," or "bar codes," to each mutant allows all mutants in a collection to be pooled and phenotyped in parallel, greatly increasing experimental throughput. In this chapter, we provide an overview of current methodologies used to create such tagged mutant collections and outline how they can be applied to understand gene function, gene-gene interactions, and drug-gene interactions. Finally, we present a methodology that uses universal TagModules, capable of bar coding a wide range of microorganisms, and demonstrate its reduction to practice by creating tagged mutant collections in the pathogenic yeast Candida albicans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".