Using Phylogeny and a Conserved Genomic Neighborhood Analysis to Extract and Visualize Gene Sets Involved in Target Gene Function: The Case of [NiFe]-hydrogenase and Succinate Dehydrogenase
Bibliographic record
Abstract
Several enzymes have subunits that require the addition of cofactors or maturation of the active center, which is facilitated by other genes. Information on these functionally-related genes not only aids in the functional anal-ysis of target genes, but is also useful for heterologous expression. In the present study, we analyzed the homologs of a target gene and their relationships with adjacent genes within the genome by constructing clusters of neighboring genes, quantifying the number of clustered genes, and examining their conservation in a taxonomic clade of target gene homologs. [NiFe]-hydrogenase was selected as the target because of the availability of a concrete database for subsequent evaluations in our anal-ysis. The present results indicate that genes associated with target gene function were conserved according to the molecular phylogeny of the target gene. We subsequently introduced automated clustering of the phylogenetic tree clade of clustered genes and applied this method to large datasets not yet analyzed and our previous data. The results obtained suggest that this approach provides insights into a comprehensive set of genes involved in cellular functions, particularly when the genes being analyzed are complex and require maturation. The procedure developed herein also provided similar and reproducible results on previously analyzed succinate dehydrogenase, which was not arbitrary.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".