vhcub {vhcub}

vhcub: A package to analysis the co-adaptation of codon usage between a virus and its host.

Description

vhcub can calculate various codon usage bias measurements as; effective number of codons (ENc), codon adaptation index (CAI), relative codon deoptimization index (RCDI), similarity index (SiD), synonymous codon usage eorderliness (SCUO) and, relative synonymous codon usage (RSCU). Also, it provides a statistical dinucleotide over- and underrepresentation with three different models. Implement several methods for visualization of codon usage as ENc.GC3plot and PR2.plot.

vhcub functions

fasta.read: read fasta format files and convert it to data.frame.

GC.content: calculates overall GC content as well as GC at first, second, and third codon positions.

RSCU.values: measure the Relative Synonymous Codon Usage (RSCU) of DNA sequence.

SCUO.values: measure the Synonymous Codon Usage Eorderliness (SCUO) of DNA sequence.

RCDI.values: measure the Relative Codon Deoptimization Index (RCDI) of DNA sequence.

CAI.values: measure the Codon Adaptation Index (CAI) Sharp and Li (1987), of DNA sequence.

ENc.values: measure the Effective Number of Codons (ENc) of DNA sequence. Using its modified version.

dinuc.syncodon: measure of statistical dinucleotide over- and underrepresentation; by allows for random sequence generation by shuffling (with/without replacement) of synonymous codons.

dinuc.codon: measure of statistical dinucleotide over- and underrepresentation; by allows for random sequence generation by shuffling (with/without replacement) of codons.

dinuc.base: measure of statistical dinucleotide over- and underrepresentation; by allows for random sequence generation by shuffling (with/without replacement) of all bases in the sequence.

ENc.GC3plot: make an ENc-GC3 scatterplot. Where the y-axis represents the ENc values and the x-axis represents the GC3 content. The red fitting line shows the expected ENc values when codon usage bias affected solely by GC3.

PR2.plot: make a Parity rule 2 (PR2) plot, where the AT-bias [A3/(A3 +T3)] at the third codon position of the four-codon amino acids of entire genes is the ordinate and the GC-bias [G3/(G3 +C3)] is the abscissa. The center of the plot, where both coordinates are 0.5, is where A = U and G = C (PR2), with no bias between the influence of the mutation and selection rates.

Author(s)

Ali Mostafa Anwar [e-mail] and Mohmed Soudy [e-mail]

Examples

# read DNA from fasta files
fasta <- fasta.read("virus.fasta", "host.fasta")
fasta.v <- fasta[[1]]
fasta.h <- fasta[[2]]
# calculate GC content
gc.df <- GC.content(fasta.v)
# measure of statistical dinucleotide over- and underrepresentation
syncodon <- dinuc.syncodon(fasta.v,permutations=10)
base <- dinuc.base(fasta.v,permutations=10)
codon <- dinuc.codon(fasta.v,permutations=10)
# calculate ENc
enc.df <- ENc.values(fasta.v)
enc.df.h <- ENc.values(fasta.h)
# calculate SCUO and CAI
SCUO.df <- SCUO.values(fasta.v)
cai.df <- CAI.values(fasta.v,enc.df.h, fasta.h)
# calculate RSCU
RSCU.H <- RSCU.values(fasta.h)
RSCU.V <- RSCU.values(fasta.v)
# calculate SiD
SiD <- SiD.value(RSCU.H,RSCU.V)
# calculate RCDI
rcdi.df <- RCDI.values(fasta.v,fasta.h, enc.df.h)
# plot ENc.GC3plot
ENc.GC3plot(enc.df,gc.df)
# plot PR2.plot
PR2.plot(fasta.v)