AnaCoDa

Analysis of Codon Data under Stationarity using a Bayesian Framework

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 In addition 'AnaCoDa' contains three currently unpublished models.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.

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Description file content

Package
AnaCoDa
Type
Package
Title
Analysis of Codon Data under Stationarity using a Bayesian Framework
Version
0.1.1
Date
2018-02-12
Author
Cedric Landerer [aut, cre], Gabriel Hanas [ctb], Jeremy Rogers [ctb], Alex Cope [ctb]
Maintainer
Cedric Landerer
URL
NeedsCompilation
yes
Depends
R (>= 3.3.0), Rcpp (>= 0.11.3), methods
Suggests
Hmisc, VGAM, coda, testthat
RcppModules
Test_mod, Trace_mod, CovarianceMatrix_mod, MCMCAlgorithm_mod, Model_mod, Parameter_mod, Genome_mod, Gene_mod, SequenceSummary_mod
Description
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 In addition 'AnaCoDa' contains three currently unpublished models.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.
License
GPL (>= 2)
Imports
LinkingTo
Rcpp
LazyLoad
yes
LazyData
yes
RoxygenNote
6.0.1
Packaged
2018-02-12 19:33:29 UTC; clandere
Repository
CRAN
Date/Publication
2018-02-12 23:14:54 UTC

install.packages('AnaCoDa')

0.1.1

6 months ago

https://github.com/clandere/AnaCoDa

Cedric Landerer

GPL (>= 2)

Depends on

R (>= 3.3.0), Rcpp (>= 0.11.3), methods

Suggests

Hmisc, VGAM, coda, testthat

Discussions