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R package for RIVER (RNA-Informed Variant Effect on Regulation)

Bioconductor version: Release (3.19)

An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples.

Author: Yungil Kim [aut, cre], Alexis Battle [aut]

Maintainer: Yungil Kim <ipw012 at>

Citation (from within R, enter citation("RIVER")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Reference Manual PDF


biocViews Bayesian, BiomedicalInformatics, Clustering, FunctionalGenomics, FunctionalPrediction, GeneExpression, GeneRegulation, GeneticVariability, Genetics, GenomicVariation, Regression, SNP, Software, SystemsBiology, Transcription, TranscriptomeVariant, Transcriptomics
Version 1.28.0
In Bioconductor since BioC 3.5 (R-3.4) (7 years)
License GPL (>= 2)
Depends R (>= 3.3.2)
Imports glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils
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Follow Installation instructions to use this package in your R session.

Source Package RIVER_1.28.0.tar.gz
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macOS Binary (x86_64) RIVER_1.28.0.tgz
macOS Binary (arm64) RIVER_1.28.0.tgz
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