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birte (version 1.8.1)

Bayesian Inference of Regulatory Influence on Expression (biRte)

Description

Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. biRte uses regulatory networks of TFs, miRNAs and possibly other factors, together with mRNA, miRNA and other available expression data to predict the relative influence of a regulator on the expression of its target genes. Inference is done in a Bayesian modeling framework using Markov-Chain-Monte-Carlo. A special feature is the possibility for follow-up network reverse engineering between active regulators.

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Version

Version

1.8.1

License

GPL (>= 2)

Maintainer

Holger Froehlich

Last Published

February 15th, 2017

Functions in birte (1.8.1)

birtePredict

Prediction of gene expression via biRte.
EColiOxygen

Example data set from E. Coli to sample TF activities.
suggestThreshold

Automatically suggest suitable threshold for marginal regulator activities.
proposeInteractions

Propose possible regulator-regulator interactions that could be worthwhile to be tested into the biRte model.
simulateData

Simulate expression data.
humanNetworkSimul

Subset of regulator-target gene network for human
simplify

Simplify regulator-target gene network via clustering.
birteRun

Main interface for Bayesian Inference of Regulatory Influence on Expression (biRte).
birteFitRidge

Fit ridge regression model given a defined set of active regulators.
estimateNetwork

Estimate network between active regulators using Nested Effects Models (NEMs).
limmaAnalysis

Simple limma analysis on expression data with one contrast.
getPotentialSwaps

plotConvergence

Plot the marginal log-likelihood of the model along MCMC samples (after thinning).
EColiNetwork

Example TF-target graph from Regulon DB.
TFexpr

Transcription factor expression values for the aerobic-anaerobic growth experiment.