est_optimal_codons | R Documentation |
est_optimal_codons
identifies optimal codons within each codon family
or amino acid group using binomial regression. Optimal codons are those whose
usage correlates positively with high gene expression or negatively with
codon usage bias (ENC), suggesting they are preferred for efficient translation.
est_optimal_codons(
cf,
codon_table = get_codon_table(),
level = "subfam",
gene_score = NULL,
fdr = 0.001
)
cf |
A matrix of codon frequencies as calculated by |
codon_table |
A codon table defining the genetic code, derived from
|
level |
Character string specifying the analysis level: "subfam" (default, analyzes codon subfamilies) or "amino_acid" (analyzes at amino acid level). |
gene_score |
A numeric vector of gene-level scores used to identify
optimal codons. Length must equal the number of rows in
If not provided, the negative of ENC values will be used (lower ENC = higher bias). |
fdr |
Numeric value specifying the false discovery rate threshold for determining statistical significance of codon optimality (default depends on method). |
A data.table containing the input codon table with additional columns indicating codon optimality status, statistical significance, and effect sizes from the regression analysis. The columns include single-letter abbreviation of the amino acid, three-letter abbreviation, codon, codon subfamily, regression coefficient, regression P-value, Benjamini and Hochberg corrected Q-value, and indication of whether the codon is optimal.
Presnyak V, Alhusaini N, Chen YH, Martin S, Morris N, Kline N, Olson S, Weinberg D, Baker KE, Graveley BR, et al. 2015. Codon optimality is a major determinant of mRNA stability. Cell 160:1111-1124.
# perform binomial regression for optimal codon estimation
cf_all <- count_codons(yeast_cds)
codons_opt <- est_optimal_codons(cf_all)
codons_opt <- codons_opt[optimal == TRUE]
codons_opt
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