Codon Bias and Base Composition Are Poor Indicators of Horizontally Transferred Genes
Bibliographic record
Abstract
Horizontal gene transfer is now recognized as an important mechanism of evolution. Several methods to detect horizontally transferred genes have been suggested. These methods are based on either nucleotide composition or the failure to find a similar gene in closely related species. Genes that evolve vertically between closely related species can be divided into those that retain homologous chromosomal positions (positional orthologs) and those that do not. By comparing open reading frames in the Escherichia coli and Salmonella typhi genomes, we identified 2,728 positional orthologs since these species split 100 MYA. A group of 1,144 novel E. coli genes were unusually diverged from their S. typhi counterparts. These novel genes included those that had been horizontally transferred into E. coli, as well as members of gene pairs that had been rearranged or deleted. Positional orthologs were used to investigate compositional methods of identifying horizontally transferred genes. A large number of E. coli genes with normal nucleotide composition have no apparent ortholog in S. typhi, and many genes of atypical composition do, in fact, have positional orthologs. A phylogenetic approach was employed to confirm selected examples of horizontal transmission among the novel groups of genes. Our analysis of 80 E. coli genes determined that a number of genes previously classified as horizontally transferred based on base composition and codon bias were native, and genes previously classified as native appeared to be horizontally transferred. Hence, atypical nucleotide composition alone is not a reliable indicator of horizontal transmission.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".