RBioMIR - An R package for differential expression (DE) analysis of small RNA-Seq based miRNA profiling experiments.
When using this program please cite: Zhang J, Hadj-Moussa H, and Storey KB. (2016). Current progress of high-throughput microRNA differential expression analysis and random forest gene selection for model and non-model systems: an R implementation. Journal of Integrative Bioinformatics, 13(5), 306. PMID:28187420.
Download and install R and R Studio. To install the latest version of RBioMIR, please select the following commands (copy/paste) and run them in R Studio (make sure you are connected to the internet):
(1) Run the following command to install devtools (needed to run RBioMIR), otherwise skip this step:
install.packages("devtools")(2) Run the following command to install Bioconductor (needed to run RBioMIR), otherwise skip this step:
source("https://bioconductor.org/biocLite.R") biocLite() For help with installing Bioconductor, visit (https://www.bioconductor.org/install/)
(3) Run the following command to install RBioMIR:
devtools::install_github("jzhangc/git_RBioMIR/RBioMIR", repos = BiocInstaller::biocinstallRepos())(4) To set a working directory use, replace
"..." with your folder address: For Mac and Linux:
setwd("working directory")The source code and the R package can be downloaded through GitHub: https://github.com/jzhangc/git_RBioMIR.git.
To access the Unix Shell codes for initial data processing of small RNA-Seq raw reads, CLICK HERE!