Enabling low-cost and robust essentiality studies with high-throughput transposon mutagenesis (HTTM)
Bibliographic record
Abstract
Transposon-insertion sequencing (TIS) methods couple high density transposon mutagenesis with next-generation sequencing and are commonly used to identify essential or important genes in bacteria. However, this approach can be work-intensive and sometimes expensive depending on the selected protocol. The difficulty to process a high number of samples in parallel using standard TIS protocols often restricts the number of replicates that can be performed and limits the deployment of this technique to large-scale projects studying gene essentiality in various strains or growth conditions. Here, we report the development of a robust and inexpensive High-Throughput Transposon Mutagenesis (HTTM) protocol and validate the method using Escherichia coli strain BW25113, the parental strain of the KEIO collection. HTTM reliably provides high insertion densities with an average of one transposon every ≤20bp along with impressive reproducibility (Spearman correlation coefficients >0.94). A detailed protocol is available at protocol.io and a graphical version is also included with this article.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".