vhcub

Virus-Host Codon Usage Co-Adaptation Analysis

vhcub is an R package to analyze the co-adaptation of codon usage between a virus and its host.

Installation

1- Using devtools:

R if (!requireNamespace("devtools", quietly=TRUE)){ install.packages("devtools")} devtools::install_github('AliYoussef96/vhcub')

2- vhcub was developed using R and available on CRAN:

R install.packages("vhcub")

3- If 1 and 2 installation instructions failed, please try:

```R if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("coRdon") BiocManager::install("Biostrings")

install.packages("vhcub") ```

The following measures are implemented in the package

• ENC, effective number of codons (Novembre, 2002),
• SCUO, synonymous codon usage orderliness (Wan et al., 2004),
• Codon Adaptation Index, CAI (Sharp and Li, 1987),
• Relative Codon Deoptimization Index, RCDI (Puigbò et al, 2010),
• Relative Synonymous Codon Usage, RSCU (Sharp and Li, 1987),
• Also, it provides a statistical dinucleotide over- and underrepresentation with three different models.

Usage

Using vhcub to study the CUB of a virus, its host and the co-adaptation between them is straightforward.

For example;

coding sequences for both Escherichia virus T4 and its host Escherichia coli were downloaded in fasta format from the NCBI database.

```R

read virus and host fasta files

fasta <- fasta.read("EscherichiavirusT4.fasta","Escherichiacoli.fasta") fasta.virus <- fasta[[1]] fasta.host <- fasta[[2]]

Calculate the GC overall all content as well as GC at first, second and third codon positions for the virus

gc.df <- GC.content(fasta.virus)

Calculate zscore using syncodon model for statistical dinucleotide over- and underrepresentation

syncodon <- dinuc.syncodon(fasta.virus,permutations=100)

Calculate zscore using base model for statistical dinucleotide over- and underrepresentation

base <- dinuc.base(fasta.virus,permutations=100)

Calculate zscore using codon model for statistical dinucleotide over- and underrepresentation

codon <- dinuc.codon(fasta.virus,permutations=100)

Calculate ENc values for the virus and its host

enc.df.virus <- ENc.values(fasta.virus) enc.df.host <- ENc.values(fasta.host)

Calculate SCUO values for the virus

scuo.df <- SCUO.values(fasta.virus)

Calculate CAI values for the virus using the host sequences as a reference genes set

cai.df <- CAI.values(fasta.virus, enc.df.host, fasta.host, genetic.code="11")

Calculate RSCU values for the virus and its host

rscu.virus <- RSCU.values(fasta.virus) rscu.host <- RSCU.values(fasta.host)

Calculate SiD value for the virus

SiD <- SiD.value(rscu.host,rscu.virus)

Calculate RCDI values for the virus

rcdi.df <- RCDI.values(fasta.virus,fasta.host, enc.df.host) ```

Furthermore, vhcub uses ggplot to visualize two important plots named ENc-GC3 plot and PR2plot, which help to explain what are the factors influence a virus's evolution concerning its CUB.

```R ENc.GC3plot(enc.df.virus,gc.df)

PR2.plot function need fasta sequences inputs that just only include the four-fold codons

PR2.plot(fasta.virus) ```

Contribution Guidelines

Contributions to the package are welcome

For bugs and suggestions, the most effective way is by raising an issue on the github issue tracker. Github allows you to classify your issues so that we know if it is a bug report, feature request or feedback to the authors.

If you wish to contribute some changes to the code then you should submit a pull request How to create a Pull Request? documentation on pull requests

Citation

Anwar AM, Soudy M and Mohamed R. vhcub: Virus-host codon usage co-adaptation analysis [version 1; peer review: 2 approved]. F1000Research 2019, 8:2137 (https://doi.org/10.12688/f1000research.21763.1)