Pseudofinder: Detection of Pseudogenes in Prokaryotic Genomes
Bibliographic record
Abstract
Prokaryotic genomes are usually densely packed with intact and functional genes. However, in certain contexts, such as after recent ecological shifts or extreme population bottlenecks, broken and nonfunctional gene fragments can quickly accumulate and form a substantial fraction of the genome. Identification of these broken genes, called pseudogenes, is a critical step for understanding the evolutionary forces acting upon, and the functional potential encoded within, prokaryotic genomes. Here, we present Pseudofinder, an open-source software dedicated to pseudogene identification and analysis in bacterial and archaeal genomes. We demonstrate that Pseudofinder's multi-pronged, reference-based approach can detect a wide variety of pseudogenes, including those that are highly degraded and typically missed by gene-calling pipelines, as well newly formed pseudogenes containing only one or a few inactivating mutations. Additionally, Pseudofinder can detect genes that lack inactivating substitutions but experiencing relaxed selection. Implementation of Pseudofinder in annotation pipelines will allow more precise estimations of the functional potential of sequenced microbes, while also generating new hypotheses related to the evolutionary dynamics of bacterial and archaeal genomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".