codons

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'AnaCoDa' contains three currently unpublished models.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.

Cedric Landerer

AnaCoDa

Analysis of Codon Data under Stationarity using a Bayesian
Framework

Gabriel Hanas

Jeremy Rogers

Alex Cope

codons function

Codons — codons

Codons

codons: Codons

Description

Usage

Arguments

Value

See Also