DNA Asymmetric Strand Bias Affects the Amino Acid Composition of Mitochondrial Proteins
Bibliographic record
Abstract
Variations in GC content between genomes have been extensively documented. Genomes with comparable GC contents can, however, still differ in the apportionment of the G and C nucleotides between the two DNA strands. This asymmetric strand bias is known as GC skew. Here, we have investigated the impact of differences in nucleotide skew on the amino acid composition of the encoded proteins. We compared orthologous genes between animal mitochondrial genomes that show large differences in GC and AT skews. Specifically, we compared the mitochondrial genomes of mammals, which are characterized by a negative GC skew and a positive AT skew, to those of flatworms, which show the opposite skews for both GC and AT base pairs. We found that the mammalian proteins are highly enriched in amino acids encoded by CA-rich codons (as predicted by their negative GC and positive AT skews), whereas their flatworm orthologs were enriched in amino acids encoded by GT-rich codons (also as predicted from their skews). We found that these differences in mitochondrial strand asymmetry (measured as GC and AT skews) can have very large, predictable effects on the composition of the encoded proteins.
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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".