Protocol to identify genes important for Candida albicans fitness in diverse environmental conditions using pooled bar-seq screening approach
Bibliographic record
Abstract
Identifying genes important for fitness in Candida albicans advances our understanding of this important pathogen of humans. Here, we present a functional genomics approach for assessing fitness through the quantification of strain-specific barcodes. We describe steps for library preparation, propagation of strains, genomic DNA extraction, amplification of barcodes, and sequencing. We then detail the computational analysis of data to determine effect size and statistical significance. For complete details on the use and execution of this protocol, please refer to Xiong et al. 1 • Quantifying strain-specific barcodes allows assessment of fitness in C. albicans • Steps for propagating mutant strains in a pooled format in different conditions • Steps for DNA extraction and PCR amplification of strain-specific barcodes • Steps for quantifying and analyzing strain abundance via next-generation sequencing Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Identifying genes important for fitness in Candida albicans advances our understanding of this important pathogen of humans. Here, we present a functional genomics approach for assessing fitness through the quantification of strain-specific barcodes. We describe steps for library preparation, propagation of strains, genomic DNA extraction, amplification of barcodes, and sequencing. We then detail the computational analysis of data to determine effect size and statistical significance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".