[Rp] Reproducibility report: Identifying essential genes by mutagenesis
An R reproduction report about Bayesian identification of essential genes from mutagenesis data.
External publication. Published by its original venue; not published in our journal.
Can a tiny synthetic count dataset with known generating parameters expose posterior-calculation or convergence errors?
The evaluator supplies synthetic counts, computes a small exact or numerical posterior reference and independently assesses convergence; no new biological procedure is proposed.
What you could produce
- Versioned protocol, input and environment manifest, and independent per-case comparison table including uncertainty and incomplete cases.
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Limits to keep in view
- No research code was executed; no independent scientific verification has been performed.
- The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
- Published source metadata and a historical review do not establish compatibility, successful reproduction, operator independence or current scientific correctness.
- The language/runtime named by the upstream source is outside the current qualified Python-only pilot.
Source and permission context
Broman, Karl W.. [Rp] Reproducibility report: Identifying essential genes by mutagenesis. ReScience C 6(1), #12; 10.5281/zenodo.3959516.
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Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
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