How Sequence Context-Dependent Mutability Drives Mutation Rate Variation in the Genome
Bibliographic record
Abstract
The rate of mutations varies >100-fold across the genome, altering the rate of evolution, and susceptibility to genetic diseases. The strongest predictor of mutation rate is the sequence itself, varying 75-fold between trinucleotides. The fact that DNA sequence drives its own mutation rate raises a simple but important prediction; highly mutable sequences will mutate more frequently and eliminate themselves in favor of sequences with lower mutability, leading to a lower equilibrium mutation rate. However, purifying selection constrains changes in mutable sequences, causing higher rates of mutation. We conduct a simulation using real human mutation data to test if 1) DNA evolves to a low equilibrium mutation rate and 2) purifying selection causes a higher equilibrium mutation rate in the genome's most important regions. We explore how this simple process affects sequence evolution in the genome, and discuss the implications for modeling evolution and susceptibility to DNA damage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".