Transcription-induced mutation and ancient gBGC shape the evolution of human transcriptional start sites
Bibliographic record
Abstract
ABSTRACT In the human genome, mutation rates vary along genes, yet how their fine-scale structure contributes to gene evolution has remained largely unexplored. Here, we map inherited mutations at single-nucleotide resolution and uncover a striking hypermutation peak at transcription start sites (TSSs). This pattern is observed both in population polymorphisms and in de novo mutations from parent-offspring trios, and is dependent on transcriptional activity in testes. We also find similar hypermutation peaks at TSSs used to produce long noncoding RNAs and at intergenic RNA Polymerase II pause sites. In addition, we identify distinct mutational signatures at exon-intron boundaries and in introns. By comparing the current nucleotide content to predicted equilibrium levels, inferred from mutation and fixation rates, and by analyzing derived ancestral frequency spectrums, we detect signals that are compatible with a low level of ongoing background GC-biased gene conversion (gBGC) throughout the region and ancestral elevated gBGC activity downstream from the TSS. Using a forward-in-time simulation algorithm, we show that the current nucleotide composition surrounding the TSS of protein-coding genes is best explained by local mutation biases coupled to ongoing and ancestral patterns of gene conversions. Our simulations allow us to infer several features of these gBGC events. Overall, these findings indicate that the nucleotide composition around TSSs is largely shaped by non-adaptive forces, particularly mutation bias and gBGC.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".