AnaCoDa

• AnaCoDa is a collection of codon models.
• the release version can be obtained from

Installing AnaCoDa

AnaCoDa can be installed via CRAN.

install.packages("AnaCoDa")

AnaCoDa can also be compiled directly from source obtained through Github using the bash commands The output zipped tarball can then be installed within R using the following command.

R CMD build RibModelFramework .
R

Once in R, user can call

install.packages(<tar.gz file produced from build>,repos=NULL,type="source")

An alternative, if user is able to use sudo, is

sudo R CMD build RibModelFramework .
sudo R CMD INSTALL <tar.gz file produced from build>

Examples: Running models

Example 1: Using codon data in the form of CDS in fasta format with one mixture (ROC)

The following example illustrates how you would estimates parameters under the ROC model of a given set of protein coding genes, assuming the same mutation and selection regime for all genes.

genome <- initializeGenomeObject(file = "genome.fasta")
parameter <- initializeParameterObject(genome = genome, sphi = 1, num.mixtures = 1, gene.assignment = rep(1, length(genome)))
mcmc <- initializeMCMCObject(samples = 5000, thinning = 10, adaptive.width=50)
model <- initializeModelObject(parameter = parameter, model = "ROC")
runMCMC(mcmc = mcmc, genome = genome, model = model)

Example 2: Using codon data in the form of CDS in fasta format with one mixture (FONSE)

The following example illustrates how you would estimates parameters under the FONSE model of a given set of protein coding genes, assuming the same mutation and selection regime for all genes. Note that this model is not published and is still under beta testing.

genome <- initializeGenomeObject(file = "genome.fasta")
parameter <- initializeParameterObject(genome = genome, sphi = 1, num.mixtures = 1, gene.assignment = rep(1, length(genome)))
mcmc <- initializeMCMCObject(samples = 5000, thinning = 10, adaptive.width=50)
model <- initializeModelObject(parameter = parameter, model = "FONSE")
runMCMC(mcmc = mcmc, genome = genome, model = model)

Example 3: Using codon data in the form of Ribosome footprints with one mixture (PA)

The following example illustrates how you would estimates parameters under the PA model of a given set of protein coding genes, assuming the same mutation and selection regime for all genes. Note that this model (and PANSE) is not published and is still under beta testing.

genome <- initializeGenomeObject(file = "rfpcounts.tsv", fasta = FALSE)
parameter <- initializeParameterObject(genome = genome, sphi = 1, num.mixtures = 1, gene.assignment = rep(1, length(genome)))
mcmc <- initializeMCMCObject(samples = 5000, thinning = 10, adaptive.width=50)
model <- initializeModelObject(parameter = parameter, model = "PA")
runMCMC(mcmc = mcmc, genome = genome, model = model)

Examples: Advanced examples

• As the above examples illustrated the commonalities in the way all models are called. The following example will use the default ROC model for illustration purposes

Example 4

• multiple mixture distributions with genes being initially randomly assigned to a mixture distribution. The mixture assignment of each gene will be estimated. As the below example shows, only arguments passed to the parameter object have to be adjusted to reflect a change in the number of assumed mixture distributions.

genome <- initializeGenomeObject(file = "genome.fasta")
parameter <- initializeParameterObject(genome = genome, sphi = c(1,2,3), num.mixtures = 3, gene.assignment = sample(1:3, length(genome), replace=TRUE))
mcmc <- initializeMCMCObject(samples = 5000, thinning = 10, adaptive.width=50)
model <- initializeModelObject(parameter = parameter, model = "ROC")
runMCMC(mcmc = mcmc, genome = genome, model = model)

Example 5

• This example is based on the previous one, but instead of estimating the assignemnt of each gene to one of the three mixture distributions, we will fix the mixture assignemt to the initial assignment

genome <- initializeGenomeObject(file = "genome.fasta")
parameter <- initializeParameterObject(genome = genome, sphi = c(1,2,3), num.mixtures = 3, gene.assignment = sample(1:3, length(genome), replace=TRUE))
mcmc <- initializeMCMCObject(samples = 5000, thinning = 10, adaptive.width=50, est.mix = FALSE)
model <- initializeModelObject(parameter = parameter, model = "ROC")
runMCMC(mcmc = mcmc, genome = genome, model = model)