AnaCoDa: Analysis of Codon Data under Stationarity using a Bayesian Framework
Version 0.1.0

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) ), and ROC with phi (Wallace & Drummond (2013) ). In addition 'AnaCoDa' contains three currently unpublished models.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.

Getting started

Package details

AuthorCedric Landerer [aut, cre], Gabriel Hanas [ctb], Jeremy Rogers [ctb], Alex Cope [ctb]
Date of publication2018-01-11 12:01:10 UTC
MaintainerCedric Landerer <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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AnaCoDa documentation built on Jan. 11, 2018, 5:04 p.m.