Is a collection of models to analyze genome scale codon
data using a Bayesian framework. Provides visualization
routines and checkpointing for model fittings. Currently
published models to analyze gene data for selection on codon
usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
et al. (2015)
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)
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.
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)
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.
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)
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)
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)
fixed problem with getCSPEstimates where log scaling was falsely enabled
fixed problem where the grouplist was not stored by writeParameterObject
Added functions to calculate the Codon Adaptation Index, Effective Number of Codons and selection coefficients.
Allow to set initial phi values based on observed phi values stored in genome object.