The Evolutionary Rates of Eukaryotic RNA Polymerases and of Their Transcription Factors Are Affected by the Level of Concerted Evolution of the Genes They Transcribe
Bibliographic record
Abstract
A defining characteristic of all eukaryotes is the presence of three RNA polymerases, each of which transcribes a particular subset of nuclear genes. RNA polymerase I transcribes rRNA genes; RNA polymerase II transcribes mRNA, miRNA, snRNA, and snoRNA genes; and RNA polymerase III transcribes 5S rRNA and tRNA genes. Here, we use the sequences of up to 25 Ascomycete species to show that the type of genes transcribed by each RNA polymerase affects their evolutionary rates and those of their transcription factors (TFs). The RNA polymerase subunits and TFs of genes whose promoters experience higher levels of concerted evolution evolve significantly faster than those experiencing lower levels of concerted evolution. The rates of evolution of RNA polymerase genes and their TFs are therefore not only the result of diverse selective constraints but are also influenced by the level of concerted evolution of the genes they transcribe.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".