reapR

reapR is an R package designed for the analysis of RNA sequencing (RNA-Seq) data, particularly for differential expression and gene set analysis. Developed by Lars Bolker, it is widely used in bioinformatics and computational biology communities.

The package allows users to perform various types of differential expression analyses using different statistical models, including negative binomial regression and edgeR. It also provides functions to fit linear models and to calculate empirical Bayes shrinkage. Additionally, reapR offers a range of pre-defined contrasts and enforcement of non-negative separation constraints.

One of the unique features of reapR is its ability to perform gene-set analysis through the use of the Fisher-Exact test or a similar alternative test to identify sets of genes that are overexpressed or knocked down in a particular condition. This enables researchers to follow the progression of biological pathways that may be relevant to the studied phenomenon.

Other notable functions in the package include Bayesian model averaging and posterior inference, the estimation of high-dimensional covariance matrices, and estimation of uniformized background counting matrices from DESeq2 outputs.

One of the package's main applications is in identifying target genes and biology pathways after microarray or RNA-Seq experiments in eukaryotic and prokaryotic organisms, including viruses.

reapR is built on top of the popular bioinformatics library BiocStyle, ensuring seamless integration with other R packages used in the field. While some features might be added or deprecated over the past years, its core functionality is well-rooted and widely adopted by the bioinformatics community.

reapR offers an R-based alternative to commonly used software and language in the bioinformatics domain. Moreover, given the extensive bioinformatical background and pertinent documentation, bioinformatics researchers could easily build upon or contribute more in the R programming foundation it follows.