infoRNAseq: An R package to estimate transcriptome diversity and specialization as well as locus specificity for RNA-Seq data

If you have RNA-Seq data (counts from genes in different libraries), using this R package you could obtain a new perspective of your results by estimating the diversity of the sampled libraries, the specialization of each one of them as well as the specificity of each one of the genes.

The R package implements the methods in: 

Martinez O and Reyes-Valdes H. (2008). Defining diversity, specialization, and gene specificity in transcriptomes through information theory. PNAS, 105:28, pp. 9709–9714.

Those methods have been used or mentioned in more than 100 scientific papers https://scholar.google.com/scholar?oi=bibs&hl=es&cites=8465061442640969601&as_sdt=5

For example, we used the methods to demonstrate how cancer reduced transcriptome specialization https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0010398

Additionally to the R package, here you can download its manual, an extensive tutorial explaining its use in a complex factorial experiment and the R objects associated with those analyses.

You may be also interested in our R package Salsa (https://zenodo.org/uploads/7602359) which demonstrate the use of Standardized Expression Profiles in chili paper.

Files and their use

1. "infoRNAseq_1.0.tar.gz"  Contains the R package ready for installation; please see text file "_Readme_infoRNAseq.txt" (next item) for instructions of how to install.
2. "_Readme_infoRNAseq.txt" Text file with brief installation instructions.
3. "infoRNAseq-manual.pdf" The PDF manual for the package.
4. "infoRNAseqTut.pdf" A detailed tutorial demonstrating all the data and functions of the package in the analysis of a complex RNA-Seq experiment.
5. "ObjInBoxesStuff.RData" A binary R file containing all the objects created when analyzing the Capsicum data, which are described in the previous item, "infoRNAseqTut.pdf". You need to download this file and include it in your environment only if you want to follow step by step the analyses shown in the tutorial. If so, please download the file to the R working directory and load the objects with the R statement 

   load("ObjInBoxesStuff.RData")