Effect of the assignment of ancestral CpG state on the estimation of nucleotide substitution rates in mammals
Bibliographic record
Abstract
BACKGROUND: Molecular evolutionary studies in mammals often estimate nucleotide substitution rates within and outside CpG dinucleotides separately. Frequently, in alignments of two sequences, the division of sites into CpG and non-CpG classes is based simply on the presence or absence of a CpG dinucleotide in either sequence, a procedure that we refer to as CpG/non-CpG assignment. Although it likely that this procedure is biased, it is generally assumed that the bias is negligible if species are very closely related. RESULTS: Using simulations of DNA sequence evolution we show that assignment of the ancestral CpG state based on the simple presence/absence of the CpG dinucleotide can seriously bias estimates of the substitution rate, because many true non-CpG changes are misassigned as CpG. Paradoxically, this bias is most severe between closely related species, because a minimum of two substitutions are required to misassign a true ancestral CpG site as non-CpG whereas only a single substitution is required to misassign a true ancestral non-CpG site as CpG in a two branch tree. We also show that CpG misassignment bias differentially affects fourfold degenerate and noncoding sites due to differences in base composition such that fourfold degenerate sites can appear to be evolving more slowly than noncoding sites. We demonstrate that the effects predicted by our simulations occur in a real evolutionary setting by comparing substitution rates estimated from human-chimp coding and intronic sequence using CpG/non-CpG assignment with estimates derived from a method that is largely free from bias. CONCLUSION: Our study demonstrates that a common method of assigning sites into CpG and non CpG classes in pairwise alignments is seriously biased and recommends against the adoption of ad hoc methods of ancestral state assignment.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".