Phylogenetic analyses of cyanobacterial genomes: Quantification of horizontal gene transfer events
Bibliographic record
Abstract
Using 1128 protein-coding gene families from 11 completely sequenced cyanobacterial genomes, we attempt to quantify horizontal gene transfer events within cyanobacteria, as well as between cyanobacteria and other phyla. A novel method of detecting and enumerating potential horizontal gene transfer events within a group of organisms based on analyses of "embedded quartets" allows us to identify phylogenetic signal consistent with a plurality of gene families, as well as to delineate cases of conflict to the plurality signal, which include horizontally transferred genes. To infer horizontal gene transfer events between cyanobacteria and other phyla, we added homologs from 168 available genomes. We screened phylogenetic trees reconstructed for each of these extended gene families for highly supported monophyly of cyanobacteria (or lack of it). Cyanobacterial genomes reveal a complex evolutionary history, which cannot be represented by a single strictly bifurcating tree for all genes or even most genes, although a single completely resolved phylogeny was recovered from the quartets' plurality signals. We find more conflicts within cyanobacteria than between cyanobacteria and other phyla. We also find that genes from all functional categories are subject to transfer. However, in interphylum as compared to intraphylum transfers, the proportion of metabolic (operational) gene transfers increases, while the proportion of informational gene transfers decreases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".