Analysis of Codon Data under Stationarity using a Bayesian Framework
Amino Acid to codon set
Plots ACF for codon specific parameter traces
Autocorrelation function for the likelihood or posterior trace
Add gene observed synthesis rates
Amino acids
Calculates the marginal log-likelihood for a set of parameters
calculates the synonymous codon usage order (SCUO)
Codons
translates codon to amino acid
Convergence Test
Find and return list of optimal codons
fixDEta
fixDM
fixSphi
Take the geometric mean of a vector
getAdaptiveWidth
Calculate the Codon Adaptation Index
Calculate the CAI codon weigths for a reference genome
Get Codon Counts For all Amino Acids
Get Codon Counts For a specific Amino Acid
getCodonSpecificPosteriorMeanForCodon
getCodonSpecificPosteriorVarianceForCodon
getCodonSpecificQuantilesForCodon
Return Codon Specific Paramters (or write to csv) estimates as data.fr...
getEstimatedMixtureAssignmentForGene
getEstimatedMixtureAssignmentProbabilitiesForGene
Returns the estimated phi posterior for a gene
getGroupList
getLogLikelihoodTrace
getLogPosteriorMean
getLogPosteriorTrace
Returns mixture assignment estimates for each gene
Gene Names of Genome
Calculate the Effective Number of Codons
Calculate the Effective Number of Codons for each Amino Acid
getNoiseOffsetPosteriorMean
getNoiseOffsetVariance
Get gene observed synthesis rates
getSamples
Calculate Selection coefficients
getStdDevSynthesisRatePosteriorMean
getStdDevSynthesisRateVariance
getStepsToAdapt
getSynthesisRate
getSynthesisRatePosteriorMeanForGene
getSynthesisRatePosteriorVarianceForGene
getThinning
extracts an object of traces from a parameter object.
getTraceObject
Initialize Covariance Matrices
Genome Initialization
Initialize MCMC
Model Initialization
Initialize Parameter
initializeSynthesisRateByGenome
initializeSynthesisRateByList
initializeSynthesisRateByRandom
initMutationCategories
initSelectionCategories
Length of Genome
Load MCMC Object
Load Parameter Object
Plot Model Object
Plot Parameter
Plot MCMC algorithm
Plot Model Object
Plot Parameter
Plot Trace Object
Plot Acceptance ratios
Plot Codon Specific Parameter
readPhiValue
Run MCMC
setAdaptiveWidth
setGroupList
setRestartFileSettings
Set Restart Settings
setSamples
setStepsToAdapt
setThinning
simulateGenome
Summary of Genome
Write MCMC Object
Write Parameter Object to a File
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) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data.