An Improved Implementation of Effective Number of Codons (Nc)
Bibliographic record
Abstract
The effective number of codons (N(c)) is a widely used index for characterizing codon usage bias because it does not require a set of reference genes as does codon adaptation index (CAI) and because of the freely available computational tools such as CodonW. However, N(c), as originally formulated has many problems. For example, it can have values far greater than the number of sense codons; it treats a 6-fold compound codon family as a single-codon family although it is made of a 2-fold and a 4-fold codon family that can be under dramatically different selection for codon usage bias; the existing implementations do not handle all different genetic codes; it is often biased by codon families with a small number of codons. We developed a new N(c) that has a number of advantages over the original N(c). Its maximum value equals the number of sense codons when all synonymous codons are used equally, and its minimum value equals the number of codon families when exactly one codon is used in each synonymous codon family. It handles all known genetic codes. It breaks the compound codon families (e.g., those involving amino acids coded by six synonymous codons) into 2-fold and 4-fold codon families. It reduces the effect of codon families with few codons by introducing pseudocount and weighted averages. The new N(c) has significantly improved correlation with CAI than the original N(c) from CodonW based on protein-coding genes from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Bacillus subtilis, Micrococcus luteus, and Mycoplasma genitalium. It also correlates better with protein abundance data from the yeast than the original N(c).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".