Title:	Codon Usage Bias Analysis
Version:	1.2.0
Description:	A suite of functions for rapid and flexible analysis of codon usage bias. It provides in-depth analysis at the codon level, including relative synonymous codon usage (RSCU), tRNA weight calculations, machine learning predictions for optimal or preferred codons, and visualization of codon-anticodon pairing. Additionally, it can calculate various gene- specific codon indices such as codon adaptation index (CAI), effective number of codons (ENC), fraction of optimal codons (Fop), tRNA adaptation index (tAI), mean codon stabilization coefficients (CSCg), and GC contents (GC/GC3s/GC4d). It also supports both standard and non-standard genetic code tables found in NCBI, as well as custom genetic code tables.
License:	MIT + file LICENSE
URL:	https://github.com/mt1022/cubar, https://mt1022.github.io/cubar/
BugReports:	https://github.com/mt1022/cubar/issues
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	bzip2
Imports:	Biostrings (≥ 2.60.0), IRanges (≥ 2.34.0), data.table (≥ 1.14.0), ggplot2 (≥ 3.3.5), rlang (≥ 0.4.11)
Depends:	R (≥ 4.1.0)
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), reticulate
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-08-01 13:26:15 UTC; mt1022
Author:	Hong Zhang [aut, cre], Mengyue Liu [aut], Bu Zi [aut]
Maintainer:	Hong Zhang <mt1022.dev@gmail.com>
Repository:	CRAN
Date/Publication:	2025-08-19 07:30:02 UTC

amino acids to codons

Description

A data.frame of mapping from amino acids to codons

Usage

aa2codon

Format

a data.frame with two columns: amino_acid, and codon.

amino_acid

amino acid corresponding to the codon

codon

codon identity

Source

It is actually the standard genetic code.

Examples

aa2codon

Generate codon-anticodon pairing relationship

Description

ca_pairs show possible codon-anticodons pairings

Usage

ca_pairs(codon_table = get_codon_table(), domain = "Eukarya", plot = FALSE)

Arguments

Value

a data.table of codon-anticodon pairing information. The columns represent the pairing type, codon, corresponding anticodon, and the encoded amino acid when the argument "plot" is FALSE.

Examples

# get possible codon and anticodon pairings for the vertebrate mitochondrial genetic code
ctab <- get_codon_table(gcid = '2')
pairing <- ca_pairs(ctab)
head(pairing)

Quality control and preprocessing of coding sequences

Description

check_cds performs comprehensive quality control on coding sequences (CDS) by filtering sequences based on various criteria and optionally removing start or stop codons. This function ensures that sequences meet the requirements for downstream codon usage analysis.

Usage

check_cds(
  seqs,
  codon_table = get_codon_table(),
  min_len = 6,
  check_len = TRUE,
  check_start = TRUE,
  check_stop = TRUE,
  check_istop = TRUE,
  rm_start = TRUE,
  rm_stop = TRUE,
  start_codons = c("ATG")
)

Arguments

Value

A DNAStringSet containing filtered and optionally trimmed CDS sequences that pass all quality control checks.

Examples

# Perform CDS sequence quality control for a sample of yeast genes
s <- head(yeast_cds, 10)
print(s)
check_cds(s)

Differential codon usage analysis

Description

codon_diff takes two set of coding sequences and perform differential codon usage analysis.

Usage

codon_diff(seqs1, seqs2, codon_table = get_codon_table())

Arguments

Value

a data.table of the differential codon usage analysis. Global tests examine wthether a codon is used differently relative to all the other codons. Family tests examine whether a codon is used differently relative to other codons that encode the same amino acid. Subfamily tests examine whether a codon is used differently relative to other synonymous codons that share the same first two nucleotides. Odds ratio > 1 suggests a codon is used at higher frequency in seqs1 than in seqs2.

Examples

yeast_exp_sorted <- yeast_exp[order(yeast_exp$fpkm),]
seqs1 <- yeast_cds[names(yeast_cds) %in% head(yeast_exp_sorted$gene_id, 1000)]
seqs2 <- yeast_cds[names(yeast_cds) %in% tail(yeast_exp_sorted$gene_id, 1000)]
cudiff <- codon_diff(seqs1, seqs2)

Optimize codon usage in coding sequences

Description

codon_optimize redesigns a coding sequence by replacing each codon with its optimal synonymous alternative, while maintaining the same amino acid sequence. This function supports multiple optimization methods and is useful for improving protein expression in heterologous systems.

Usage

codon_optimize(
  seq,
  optimal_codons = optimal_codons,
  cf = NULL,
  codon_table = get_codon_table(),
  level = "subfam",
  method = "naive",
  num_sequences = 1,
  organism = NULL,
  envname = "cubar_env",
  attention_type = "original_full",
  deterministic = TRUE,
  temperature = 0.2,
  top_p = 0.95,
  match_protein = FALSE,
  spliceai = FALSE
)

Arguments

Value

The return type depends on parameters:

• Single DNAString: When num_sequences=1 and spliceai=FALSE

• DNAStringSet: When num_sequences>1 and spliceai=FALSE

• data.table: When spliceai=TRUE (includes sequences and splice predictions)

References

Fallahpour A, Gureghian V, Filion GJ, Lindner AB, Pandi A. CodonTransformer: a multispecies codon optimizer using context-aware neural networks. Nat Commun. 2025 Apr 3;16(1):3205.

Jaganathan K, Panagiotopoulou S K, McRae J F, et al. Predicting splicing from primary sequence with deep learning. Cell, 2019, 176(3): 535-548.e24.

Examples

cf_all <- count_codons(yeast_cds)
optimal_codons <- est_optimal_codons(cf_all)
seq <- 'ATGCTACGA'
# method "naive":
codon_optimize(seq, optimal_codons)
# method "IDT":
codon_optimize(seq, cf = cf_all, method = "IDT")
codon_optimize(seq, cf = cf_all, method = "IDT", num_sequences = 10)

# # The following examples requires pre-installation of python package SpliceAI or Codon
#  # Transformer. see the codon optimization vignette for further details.
# seq_opt <- codon_optimize(seq, method = "CodonTransformer",
#     organism = "Saccharomyces cerevisiae")
# seqs_opt <- codon_optimize(seq, method = "CodonTransformer",
#     organism = "Saccharomyces cerevisiae", num_sequences = 10,
#     deterministic =FALSE, temperature = 0.4)
# seqs_opt <- codon_optimize(seq, cf = cf_all, method = "IDT",
#     num_sequences = 10, spliceai = TRUE)
# seq_opt <- codon_optimize(seq, method = "CodonTransformer",
#     organism = "Saccharomyces cerevisiae", spliceai = TRUE)

Count codon frequencies in coding sequences

Description

count_codons tabulates the frequency of all 64 possible codons across input coding sequences. This function provides the foundation for most codon usage bias analyses in the cubar package.

Usage

count_codons(seqs, ...)

Arguments

Value

A matrix where rows represent individual CDS sequences and columns represent the 64 possible codons. Each cell contains the frequency count of the corresponding codon in the respective sequence.

Examples

# Count codon frequencies across all yeast CDS sequences
cf_all <- count_codons(yeast_cds)
dim(cf_all)
cf_all[1:5, 1:5]

# Count codons for a single sequence
count_codons(yeast_cds[1])

Create custom codon table from amino acid-codon mapping

Description

create_codon_table generates a codon table from a user-defined data frame that maps codons to their corresponding amino acids. This function enables analysis of non-standard or artificial genetic codes not available in the NCBI genetic code collection.

Usage

create_codon_table(aa2codon)

Arguments

Value

A data.table with four columns:

• aa_code: Single-letter amino acid code

• amino_acid: Three-letter amino acid abbreviation

• codon: Three-nucleotide codon sequence

• subfam: Codon subfamily identifier (amino_acid_XY format)

Examples

# View the example amino acid to codon mapping
head(aa2codon)

# Create a custom codon table
custom_table <- create_codon_table(aa2codon = aa2codon)
head(custom_table)

Estimate Amino Acid Usage Frequencies of CDSs.

Description

Estimate Amino Acid Usage Frequencies of CDSs.

Usage

est_aau(cf, codon_table = get_codon_table())

Arguments

Value

a data.table with amino acid frequencies of CDSs. The columns include three-letter abbreviation of the amino acid, single-letter abbreviation, usage frequency of the amino acid in all sequences, and usage frequency proportion.

Examples

# estimate amino acid frequencies of yeast genes
cf_all <- count_codons(yeast_cds)
aau <- est_aau(cf_all)
print(aau)

Estimate Codon Stabilization Coefficient

Description

get_csc calculate codon occurrence to mRNA stability correlation coefficients (Default to Pearson's).

Usage

est_csc(
  seqs,
  half_life,
  codon_table = get_codon_table(),
  cor_method = "pearson"
)

Arguments

Value

a data.table of codons and their CSCs. The columns include codon, codon stability coefficient, and correlation P-value.

References

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.

Examples

# estimate yeast mRNA CSC
est_csc(yeast_cds, yeast_half_life)

Identify optimal codons using statistical modeling

Description

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.

Usage

est_optimal_codons(
  cf,
  codon_table = get_codon_table(),
  level = "subfam",
  gene_score = NULL,
  fdr = 0.001
)

Arguments

Value

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.

References

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.

Examples

# 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

Estimate Relative Synonymous Codon Usage (RSCU)

Description

est_rscu calculates the Relative Synonymous Codon Usage (RSCU) values for codons, which quantify the bias in synonymous codon usage. RSCU values indicate whether a codon is used more (>1) or less (<1) frequently than expected under uniform usage within its synonymous group.

Usage

est_rscu(
  cf,
  weight = 1,
  pseudo_cnt = 1,
  codon_table = get_codon_table(),
  level = "subfam",
  incl_stop = FALSE
)

Arguments

Value

A data.table containing the codon table with additional columns for RSCU analysis: usage frequency counts (cts), frequency proportions (prop), CAI weights (w_cai), and RSCU values (rscu). The table includes amino acid codes, full amino acid names, codons, and subfamily classifications.

References

Sharp PM, Tuohy TM, Mosurski KR. 1986. Codon usage in yeast: cluster analysis clearly differentiates highly and lowly expressed genes. Nucleic Acids Res 14:5125-5143.

Examples

# Calculate RSCU for all yeast genes
cf_all <- count_codons(yeast_cds)
rscu_all <- est_rscu(cf_all)
head(rscu_all)

# Calculate RSCU for highly expressed genes (top 500)
heg <- head(yeast_exp[order(-yeast_exp$fpkm), ], n = 500)
cf_heg <- count_codons(yeast_cds[heg$gene_id])
rscu_heg <- est_rscu(cf_heg)
head(rscu_heg)

Estimate tRNA weights for TAI calculation

Description

est_trna_weight calculates tRNA weights for each codon based on tRNA availability and codon-anticodon pairing efficiency. These weights are used in tRNA Adaptation Index (TAI) calculations and reflect how well each codon is supported by the cellular tRNA pool.

Usage

est_trna_weight(
  trna_level,
  codon_table = get_codon_table(),
  domain = "Eukarya",
  s = NULL
)

Arguments

Value

A data.table containing comprehensive tRNA weight information with columns:

• aa_code: Single-letter amino acid code

• amino_acid: Three-letter amino acid abbreviation

• codon: Codon sequence

• subfam: Codon subfamily identifier

• anticodon: Corresponding anticodon sequence

• trna_id: tRNA identifier (amino_acid-anticodon)

• ac_level: tRNA abundance level

• W: Absolute adaptiveness value

• w: Relative adaptiveness (normalized weight for TAI)

References

dos Reis M, Savva R, Wernisch L. 2004. Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res 32:5036-5044.

Sabi R, Tuller T. 2014. Modelling the efficiency of codon-tRNA interactions based on codon usage bias. DNA Res 21:511-526.

Examples

# Calculate tRNA weights for yeast using gene copy numbers
yeast_trna_w <- est_trna_weight(yeast_trna_gcn)
head(yeast_trna_w)

# View the weight distribution
hist(yeast_trna_w$w, main = "Distribution of tRNA weights")

Extract tRNA gene copy numbers from nature tRNA sequences

Description

extract_trna_gcn processes tRNA sequence data from GtRNADB to extract gene copy numbers for each tRNA type. This information is essential for calculating tRNA availability weights used in TAI analysis.

Usage

extract_trna_gcn(trna_seq)

Arguments

Value

A named table of tRNA gene copy numbers. Names are in the format "AminoAcid-Anticodon" (e.g., "Ala-AGC") and values represent the count of genes encoding each tRNA type. Initiator tRNAs (iMet, fMet) and undetermined tRNAs (Und-NNN) are automatically excluded as they serve specialized functions in translation initiation.

Examples

# Extract tRNA gene copy numbers for yeast
trna_gcn <- extract_trna_gcn(yeast_trna)
head(trna_gcn)

# View the distribution of tRNA gene copies
hist(trna_gcn, main = "Distribution of tRNA gene copy numbers")

Amino Acid Usage

Description

Calculate Amino Acid Usage Frequencies of each CDS.

Usage

get_aau(cf, codon_table = get_codon_table())

Arguments

Value

a matrix of amino acid frequencies for each CDS. Each row corresponds to a sequence, and each column represents an amino acid.

Examples

# estimate amino acid frequencies of yeast CDSs
cf_all <- count_codons(yeast_cds)
aau_gene <- get_aau(cf_all)
head(aau_gene)

Calculate Codon Adaptation Index (CAI)

Description

get_cai calculates the Codon Adaptation Index (CAI) for each input coding sequence. CAI measures how similar the codon usage of a gene is to that of highly expressed genes, serving as an indicator of translational efficiency. Higher CAI values suggest better adaptation to the translational machinery.

Usage

get_cai(cf, rscu, level = "subfam")

Arguments

Value

A named numeric vector of CAI values ranging from 0 to 1. Names correspond to sequence identifiers from the input matrix. Values closer to 1 indicate higher similarity to highly expressed genes.

References

Sharp PM, Li WH. 1987. The codon Adaptation Index–a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:1281-1295.

Examples

# Calculate CAI for yeast genes based on RSCU of highly expressed genes
heg <- head(yeast_exp[order(-yeast_exp$fpkm), ], n = 500)
cf_all <- count_codons(yeast_cds)
cf_heg <- cf_all[heg$gene_id, ]
rscu_heg <- est_rscu(cf_heg)
cai <- get_cai(cf_all, rscu_heg)
head(cai)
hist(cai, main = "Distribution of CAI values")

Retrieve codon table by NCBI genetic code ID

Description

get_codon_table creates a standardized codon table based on genetic codes cataloged by NCBI. This function provides the mapping between codons and amino acids for different organisms and organelles, which is essential for accurate codon usage analysis.

Usage

get_codon_table(gcid = "1")

Arguments

Value

A data.table with four columns:

• aa_code: Single-letter amino acid code

• amino_acid: Three-letter amino acid abbreviation

• codon: Three-nucleotide codon sequence

• subfam: Codon subfamily identifier (amino_acid_XY format)

Examples

# Standard genetic code (used by most organisms)
standard_code <- get_codon_table()
head(standard_code)

# Vertebrate mitochondrial genetic code
mito_code <- get_codon_table(gcid = '2')
head(mito_code)

Mean Codon Stabilization Coefficients

Description

get_cscg calculates Mean Codon Stabilization Coefficients of each CDS.

Usage

get_cscg(cf, csc)

Arguments

Value

a named vector of cscg values. The names of the elements correspond to the sequence names.

References

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.

Examples

# estimate CSCg of yeast genes
yeast_csc <- est_csc(yeast_cds, yeast_half_life)
cf_all <- count_codons(yeast_cds)
cscg <- get_cscg(cf_all, csc = yeast_csc)
head(cscg)
hist(cscg)

Deviation from Proportionality

Description

get_dp calculates Deviation from Proportionality of each CDS.

Usage

get_dp(
  cf,
  host_weights,
  codon_table = get_codon_table(),
  level = "subfam",
  missing_action = "ignore"
)

Arguments

Value

a named vector of dp values. The names of the elements correspond to the sequence names.

References

Chen F, Wu P, Deng S, Zhang H, Hou Y, Hu Z, Zhang J, Chen X, Yang JR. 2020. Dissimilation of synonymous codon usage bias in virus-host coevolution due to translational selection. Nat Ecol Evol 4:589-600.

Examples

# estimate DP of yeast genes
cf_all <- count_codons(yeast_cds)
trna_weight <- est_trna_weight(yeast_trna_gcn)
trna_weight <- setNames(trna_weight$w, trna_weight$codon)
dp <- get_dp(cf_all, host_weights = trna_weight)
head(dp)
hist(dp)

Calculate effective number of codons (ENC)

Description

get_enc computes the effective number of codons (ENC) for each coding sequence, which quantifies the degree of codon usage bias. Lower ENC values indicate stronger bias (fewer codons are used), while higher values indicate more uniform codon usage.

Usage

get_enc(cf, codon_table = get_codon_table(), level = "subfam")

Arguments

Value

A named numeric vector of ENC values. Names correspond to sequence identifiers from the input matrix. ENC values typically range from 20 (maximum bias) to 61 (uniform usage).

References

Wright F. 1990. The 'effective number of codons' used in a gene. Gene 87:23-29.

Sun X, Yang Q, Xia X. 2013. An improved implementation of effective number of codons (NC). Mol Biol Evol 30:191-196.

Examples

# Calculate ENC for yeast genes
cf_all <- count_codons(yeast_cds)
enc <- get_enc(cf_all)
head(enc)
hist(enc, main = "Distribution of ENC values")

Calculate fraction of optimal codons (Fop)

Description

get_fop calculates the fraction of optimal codons (Fop) for each coding sequence, which represents the proportion of codons that are considered optimal for translation efficiency. Higher Fop values suggest stronger selection for optimal codon usage.

Usage

get_fop(cf, op = NULL, codon_table = get_codon_table(), ...)

Arguments

Value

A named numeric vector of Fop values (ranging from 0 to 1). Names correspond to sequence identifiers from the input matrix. Higher values indicate greater usage of optimal codons.

References

Ikemura T. 1981. Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system. J Mol Biol 151:389-409.

Examples

# Calculate Fop for yeast genes (optimal codons determined automatically)
cf_all <- count_codons(yeast_cds)
fop <- get_fop(cf_all)
head(fop)
hist(fop, main = "Distribution of Fop values")

Calculate GC content of coding sequences

Description

get_gc calculates the overall GC content (percentage of guanine and cytosine nucleotides) for each coding sequence.

Usage

get_gc(cf)

Arguments

Value

A named numeric vector of GC content values (ranging from 0 to 1). Names correspond to sequence identifiers from the input matrix.

Examples

# Calculate GC content for yeast genes
cf_all <- count_codons(yeast_cds)
gc <- get_gc(cf_all)
head(gc)
hist(gc, main = "Distribution of GC content")

GC contents at synonymous 3rd codon positions

Description

Calculate GC content at synonymous 3rd codon positions.

Usage

get_gc3s(cf, codon_table = get_codon_table(), level = "subfam")

Arguments

Value

a named vector of GC3s values. The names of the elements correspond to the sequence names.

References

Peden JF. 2000. Analysis of codon usage.

Examples

# estimate GC3s of yeast genes
cf_all <- count_codons(yeast_cds)
gc3s <- get_gc3s(cf_all)
head(gc3s)
hist(gc3s)

GC contents at 4-fold degenerate sites

Description

Calculate GC content at synonymous position of codons (using four-fold degenerate sites only).

Usage

get_gc4d(cf, codon_table = get_codon_table(), level = "subfam")

Arguments

Value

a named vector of GC4d values. The names of the elements correspond to the sequence names.

Examples

# estimate GC4d of yeast genes
cf_all <- count_codons(yeast_cds)
gc4d <- get_gc4d(cf_all)
head(gc4d)
hist(gc4d)

Calculate tRNA Adaptation Index (TAI)

Description

get_tai calculates the tRNA Adaptation Index (TAI) for each coding sequence, which measures how well codon usage matches tRNA availability in the cell. Higher TAI values indicate better adaptation to the tRNA pool, suggesting more efficient translation.

Usage

get_tai(cf, trna_w, w_format = "cubar")

Arguments

Value

A named numeric vector of TAI values. Names correspond to sequence identifiers from the input matrix. Values range from 0 to 1, with higher values indicating better adaptation to tRNA availability. It should be noted that the tAI package does not include codons that correspond to methionine in its calculation. In contrast, cubar requires removing the start codon and takes into account codons encoding methionine within the sequence. Correspondingly, cubar excludes initiator tRNA (iMet in eukaryotes or fMet in prokaryotes), and focuses on the anticodons that correspond to regular methionine.

References

dos Reis M, Savva R, Wernisch L. 2004. Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res 32:5036-5044.

Examples

# calculate TAI of yeast genes based on genomic tRNA copy numbers
w <- est_trna_weight(yeast_trna_gcn)
# note: check_istop is suppressed to facilitate package development.
#   We suggest enable this option for real sequence analyses.
yeast_cds_qc <- check_cds(yeast_cds, check_istop = FALSE)
cf <- count_codons(yeast_cds_qc)
tai <- get_tai(cf, w)
head(tai)
hist(tai)

human mitochondrial CDS sequences

Description

CDSs of 13 protein-coding genes in the human mitochondrial genome extracted from ENSEMBL Biomart

Usage

human_mt

Format

a DNAStringSet of 13 sequences

Source

<https://www.ensembl.org/index.html>

Examples

head(human_mt)

Plot codon-anticodon pairing relationship

Description

plot_ca_pairs show possible codon-anticodons pairings

Usage

plot_ca_pairs(codon_table = get_codon_table(), pairs = pairs)

Arguments

Value

a plot on possible codon-anticodons pairings

Examples

# plot possible codon and anticodon pairings for the vertebrate mitochondrial genetic code
ctab <- get_codon_table(gcid = '2')
pairs <- ca_pairs(ctab, plot = TRUE)
plot_ca_pairs(ctab, pairs)

# plot possible codon and anticodon pairings for the standard genetic code in bacteria
plot_ca_pairs(pairs = ca_pairs(domain = "Bacteria", plot = TRUE))

Generate reverse complement sequences

Description

rev_comp generates the reverse complement of input DNA sequences. This is commonly used for analyzing complementary strands or anticodon sequences.

Usage

rev_comp(seqs)

Arguments

Value

A DNAStringSet object containing the reverse complemented sequences.

Examples

# Reverse complement of codons
rev_comp(Biostrings::DNAStringSet(c('TAA', 'TAG')))

Convert a coding sequence to a codon vector

Description

seq_to_codons converts a coding sequence (CDS) into a vector of codons by splitting the sequence into non-overlapping triplets starting from the first position.

Usage

seq_to_codons(seq)

Arguments

Value

A character vector where each element represents a codon (3-nucleotide sequence).

Examples

# Convert a CDS sequence to a sequence of codons
seq_to_codons('ATGTGGTAG')
seq_to_codons(yeast_cds[[1]])

Display available genetic code tables

Description

show_codon_tables displays a formatted list of all genetic code tables available from NCBI, showing their ID numbers and descriptive names. This function helps users identify the appropriate genetic code ID to use with get_codon_table().

Usage

show_codon_tables()

Value

No return value (called for side effects). The function prints a formatted table of available genetic codes to the console, with each line showing the numeric ID and corresponding organism/organelle description.

Examples

# Display all available NCBI genetic code tables
show_codon_tables()

Generate sliding window intervals

Description

slide creates a data.table defining sliding window positions for analyzing sequences or data along a continuous range. This function provides the foundation for positional analyses of codon usage patterns within genes.

Usage

slide(from, to, step = 1, before = 0, after = 0)

Arguments

Value

A data.table with three columns:

• start: Start position of each window

• center: Center position of each window

• end: End position of each window

Examples

# Create sliding windows with step size 2 and window size 3
slide(1, 10, step = 2, before = 1, after = 1)

apply a cub index to a sliding window

Description

slide_apply applies a function to a sliding window of codons.

Usage

slide_apply(seq, .f, step = 1, before = 0, after = 0, ...)

Arguments

Value

data.table with start, center, end, and codon usage index columns

Examples

slide_apply(yeast_cds[[1]], get_enc, step = 1, before = 10, after = 10)

Generate sliding windows for codon-level analysis

Description

slide_codon creates sliding window intervals specifically designed for codon-based analysis of DNA sequences. This function automatically handles codon boundaries and is useful for studying positional effects in codon usage within genes.

Usage

slide_codon(seq, step = 1, before = 0, after = 0)

Arguments

Value

A data.table with three columns containing nucleotide positions:

• start: Start nucleotide position of each window

• center: Center nucleotide position of each window

• end: End nucleotide position of each window

Examples

# Create sliding windows for codon analysis
x <- Biostrings::DNAString('ATCTACATAGCTACGTAGCTCGATGCTAGCATGCATCGTACGATCGTCGATCGTAG')
slide_codon(x, step = 3, before = 1, after = 1)

plot sliding window codon usage

Description

slide_plot visualizes codon usage in sliding window.

Usage

slide_plot(windt, index_name = "Index")

Arguments

Value

ggplot2 plot.

Examples

sw <- slide_apply(yeast_cds[[1]], get_enc, step = 1, before = 10, after = 10)
slide_plot(sw)

yeast CDS sequences

Description

CDSs of all protein-coding genes in Saccharomyces_cerevisiae

Usage

yeast_cds

Format

a DNAStringSet of 6600 sequences

Source

<https://ftp.ensembl.org/pub/release-107/fasta/saccharomyces_cerevisiae/cds/Saccharomyces_cerevisiae.R64-1-1.cds.all.fa.gz>

Examples

head(yeast_cds)

yeast mRNA expression levels

Description

Yeast mRNA FPKM determined from rRNA-depleted (RiboZero) total RNA-Seq libraries. RUN1_0_WT and RUN2_0_WT (0 min after RNA Pol II repression) were averaged and used here.

Usage

yeast_exp

Format

a data.frame with 6717 rows and three columns:

gene_id

gene ID

gene_name

gene name

fpkm

mRNA expression level in Fragments per kilobase per million reads

Source

<https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57385>

References

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.

Examples

head(yeast_exp)

Half life of yeast mRNAs

Description

Half life of yeast mRNAs in Saccharomyces_cerevisiae calculated from rRNA-deleted total RNAs by Presnyak et al.

Usage

yeast_half_life

Format

a data.frame with 3888 rows and three columns:

gene_id

gene id

gene_name

gene name

half_life

mRNA half life in minutes

Source

<https://doi.org/10.1016/j.cell.2015.02.029>

References

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.

Examples

head(yeast_half_life)

yeast tRNA sequences

Description

Yeast tRNA sequences obtained from gtRNAdb.

Usage

yeast_trna

Format

a RNAStringSet with a length of 275.

Source

<http://gtrnadb.ucsc.edu/genomes/eukaryota/Scere3/sacCer3-mature-tRNAs.fa>

References

Chan PP, Lowe TM. 2016. GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic Acids Res 44:D184-189.

Examples

yeast_trna

yeast tRNA gene copy numbers (GCN)

Description

Yeast tRNA gene copy numbers (GCN) by anticodon obtained from gtRNAdb.

Usage

yeast_trna_gcn

Format

a named vector with a length of 41. Value names are anticodons.

Source

<http://gtrnadb.ucsc.edu/genomes/eukaryota/Scere3/sacCer3-mature-tRNAs.fa>

References

Chan PP, Lowe TM. 2016. GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic Acids Res 44:D184-189.

Examples

yeast_trna_gcn