Pseudofinder: detection of pseudogenes in prokaryotic genomes
Bibliographic record
Abstract
Abstract Prokaryotic genomes are generally gene dense and encode relatively few pseudogenes, or nonfunctional/inactivated remnants of genes. However, in certain contexts, such as recent ecological shifts or extreme population bottlenecks (such as those experienced by symbionts and pathogens), pseudogenes can quickly accumulate and form a substantial fraction of the genome. Identification of pseudogenes is, thus, 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. With Pseudofinder’s multi-pronged, reference-based approach, we demonstrate its capacity to detect a wide variety of pseudogenes, including those that are highly degraded and typically missed by gene-calling pipelines, as well newly formed pseudogenes, which can have only one or a few inactivating mutations. Additionally, Pseudofinder can detect intact genes undergoing relaxed selection, which may indicate incipient pseudogene formation. Implementation of Pseudofinder in annotation pipelines will not only clarify the functional potential of sequenced microbes, but will also generate novel insights and hypotheses regarding the evolutionary dynamics of bacterial and archaeal genomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".