Do orthologous gene phylogenies really support tree-thinking?
Bibliographic record
Abstract
BACKGROUND: Since Darwin's Origin of Species, reconstructing the Tree of Life has been a goal of evolutionists, and tree-thinking has become a major concept of evolutionary biology. Practically, building the Tree of Life has proven to be tedious. Too few morphological characters are useful for conducting conclusive phylogenetic analyses at the highest taxonomic level. Consequently, molecular sequences (genes, proteins, and genomes) likely constitute the only useful characters for constructing a phylogeny of all life. For this reason, tree-makers expect a lot from gene comparisons. The simultaneous study of the largest number of molecular markers possible is sometimes considered to be one of the best solutions in reconstructing the genealogy of organisms. This conclusion is a direct consequence of tree-thinking: if gene inheritance conforms to a tree-like model of evolution, sampling more of these molecules will provide enough phylogenetic signal to build the Tree of Life. The selection of congruent markers is thus a fundamental step in simultaneous analysis of many genes. RESULTS: Heat map analyses were used to investigate the congruence of orthologues in four datasets (archaeal, bacterial, eukaryotic and alpha-proteobacterial). We conclude that we simply cannot determine if a large portion of the genes have a common history. In addition, none of these datasets can be considered free of lateral gene transfer. CONCLUSION: Our phylogenetic analyses do not support tree-thinking. These results have important conceptual and practical implications. We argue that representations other than a tree should be investigated in this case because a non-critical concatenation of markers could be highly misleading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".