Codon Usage Bias Fits
Codon Bias Usage Fits
Codon Usage Bias Fits for Observed ORFs and Expression
Codon Usage Bias Approximation for ORFs without Expression
Codon Usage Bias Prediction for Observed ORFs
Predictive X-Y Plot
Plot Binning Results
Plot Fitted Models
Cedric Plot Utilities
Cedric IO Utilities
Cedric Convergence Utilities
All Cedric Internal Functions
Initial Generic Functions of Codon Usage Bias Fits
Fit Multinomial Model (Generic)
Initialization of Phi (Generic)
Simulate ORFs and Expression Data
Function for Synonymous Codon Usage Order (SCUO) Index
Generate Randomized SCUO Index
Function for Codon Adaptation Index (CAI)
Function for Selection on Codon Usage (SCU)
Input and Output Utility
Generating Data Structure
Convert Data Frame to Other Formats
Rearrange Data Structure by ORF Names
The Asymmetric Laplace Distribution
Mixed Normal Optimization
Functions for Printing Objects According to Classes
Data Formats
Default Controlling Options
Datasets for Demonstrations
Yassour 2009 Yeast Experiment Dataset
Posterior Results of Yassour 2009 Yeast Experiment Dataset
All Internal Functions
Estimating mutation and selection coefficients on synonymous codon bias usage based on models of ribosome overhead cost (ROC). Multinomial logistic regression and Markov Chain Monte Carlo are used to estimate and predict protein production rates with/without the presence of expressions and measurement errors. Work flows with examples for simulation, estimation and prediction processes are also provided with parallelization speedup. The whole framework is tested with yeast genome and gene expression data of Yassour, et al. (2009) <doi:10.1073/pnas.0812841106>.