Title:	Codon Usage Bias Analysis
Version:	1.1.0
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)
VignetteBuilder:	knitr
RoxygenNote:	7.3.2
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-12-07 07:31:04 UTC; mt1022
Author:	Hong Zhang [aut, cre], Mengyue Liu [aut], Bu Zi [aut]
Maintainer:	Hong Zhang <mt1022.dev@gmail.com>
Repository:	CRAN
Date/Publication:	2024-12-07 07:50:02 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

Quality control of CDS

Description

check_cds performs quality control of CDS sequences by filtering some peculiar sequences and optionally remove start or stop codons.

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

DNAStringSet of filtered (and trimmed) CDS sequences

Examples

# CDS sequence QC 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 codons

Description

codon_optimize takes a coding sequence (without stop codon) and replace each codon to the corresponding synonymous optimal codon.

Usage

codon_optimize(
  seq,
  optimal_codons,
  codon_table = get_codon_table(),
  level = "subfam"
)

Arguments

Value

a DNAString of the optimized coding sequence.

Examples

cf_all <- count_codons(yeast_cds)
optimal_codons <- est_optimal_codons(cf_all)
seq <- 'ATGCTACGA'
codon_optimize(seq, optimal_codons)

Count occurrences of different codons

Description

count_codons tabulates the occurrences of all the 64 codons in input CDSs

Usage

count_codons(seqs, ...)

Arguments

Value

matrix of codon (column) frequencies of each CDS (row).

Examples

# count codon occurrences
cf_all <- count_codons(yeast_cds)
dim(cf_all)
cf_all[1:5, 1:5]
count_codons(yeast_cds[1])

create custom codon table from a data frame

Description

create_codon_table creates codon table from data frame of aa to codon mapping.

Usage

create_codon_table(aa2codon)

Arguments

Value

a data.table with four columns: aa_code, amino_acid, codon, and subfam.

Examples

head(aa2codon)
create_codon_table(aa2codon = aa2codon)

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

data.table of optimal codons.

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)

Estimate optimal codons

Description

est_optimal_codons determine optimal codon of each codon family with binomial regression. Usage of optimal codons should correlate negatively with enc.

Usage

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

Arguments

Value

data.table of optimal codons.

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 RSCU

Description

est_rscu returns the RSCU value of codons

Usage

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

Arguments

Value

a data.table of codon info. RSCU values are reported in the last column.

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

# compute RSCU of all yeast genes
cf_all <- count_codons(yeast_cds)
est_rscu(cf_all)

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

Estimate tRNA weight w

Description

est_trna_weight compute the tRNA weight per codon for TAI calculation. This weight reflects relative tRNA availability for each codon.

Usage

est_trna_weight(
  trna_level,
  codon_table = get_codon_table(),
  s = list(WC = 0, IU = 0, IC = 0.4659, IA = 0.9075, GU = 0.7861, UG = 0.6295)
)

Arguments

Value

data.table of tRNA expression information.

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

# estimate codon tRNA weight for yeasts
est_trna_weight(yeast_trna_gcn)

Calculate CAI

Description

get_cai calculates Codon Adaptation Index (CAI) of each input CDS

Usage

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

Arguments

Value

a named vector of CAI values

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

# estimate CAI of 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)

get codon table by NCBI gene code ID

Description

get_codon_table creates a codon table based on the given id of genetic code in NCBI.

Usage

get_codon_table(gcid = "1")

Arguments

Value

a data.table with four columns: aa_code, amino_acid, codon, and subfam.

Examples

# Standard genetic code
get_codon_table()

# Vertebrate Mitochondrial genetic code
get_codon_table(gcid = '2')

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.

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.

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 ENC

Description

get_enc computes ENC of each CDS

Usage

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

Arguments

Value

vector of ENC values, sequence names are used as vector names

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

# estimate ENC of yeast genes
cf_all <- count_codons(yeast_cds)
enc <- get_enc(cf_all)
head(enc)
hist(enc)

Fraction of optimal codons (Fop)

Description

get_fop calculates the fraction of optimal codons (Fop) of each CDS.

Usage

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

Arguments

Value

a named vector of fop values.

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

# estimate Fop of yeast genes
cf_all <- count_codons(yeast_cds)
fop <- get_fop(cf_all)
head(fop)
hist(fop)

GC contents

Description

Calculate GC content of the whole sequences.

Usage

get_gc(cf)

Arguments

Value

a named vector of GC contents.

Examples

# estimate GC content of yeast genes
cf_all <- count_codons(yeast_cds)
gc <- get_gc(cf_all)
head(gc)
hist(gc)

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.

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.

Examples

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

Calculate TAI

Description

get_tai calculates tRNA Adaptation Index (TAI) of each CDS

Usage

get_tai(cf, trna_w)

Arguments

Value

a named vector of TAI values

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)
cf_all <- count_codons(yeast_cds)
tai <- get_tai(cf_all, 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_pairing show possible codon-anticodons pairings

Usage

plot_ca_pairing(codon_table = get_codon_table(), plot = TRUE)

Arguments

Value

a data.table of codon info and RSCU values

Examples

ctab <- get_codon_table(gcid = '2')
pairing <- plot_ca_pairing(ctab)
head(pairing)

Reverse complement

Description

rev_comp creates reverse complemented version of the input sequence

Usage

rev_comp(seqs)

Arguments

Value

reverse complemented input sequences as a DNAStringSet.

Examples

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

Convert CDS to codons

Description

seq_to_codons converts a coding sequence to a vector of codons

Usage

seq_to_codons(seq)

Arguments

Value

a character vector of codons

Examples

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

show available codon tables

Description

show_codon_tables print a table of available genetic code from NCBI through Biostrings::GENETIC_CODE_TABLE.

Usage

show_codon_tables()

Value

No return value (NULL). Available codon tables will be printed out directly.

Examples

# print available NCBI codon table IDs and descriptions.
show_codon_tables()

slide window interval generator

Description

slide generates a data.table with start, center, and end columns for a sliding window analysis.

Usage

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

Arguments

Value

data.table with start, center, and end columns

Examples

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)

sliding window of codons

Description

slide_codon generates a data.table with start, center, and end columns for a sliding window analysis of codons.

Usage

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

Arguments

Value

data.table with start, center, and end columns

Examples

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