The option to analyze RNA-Seq data using DESeq2 within Geneious is available in R10.1 and later. To do this on a Linux computer, you will first need to install R and the DESeq2 package, following the instructions below.
This is only necessary for Linux systems, as Geneious will automatically install R on Windows and MacOS systems the first time DESeq2 is run. Note that Geneious 2023.1.1 or newer is required for DESeq2 integration.
Once R has been installed, you will be able to run the DESeq2 method to Compare Expression Levels within Geneious like any other Geneious operation. You may need to specify the R executable location in the DESeq2 options, for example, if R is not installed on PATH.
Note that the installation instructions for R on Ubuntu, Centos or Red Hat Enterprise Linux require sudo or root access. If you do not have root access, you can install R from source in your home directory.
Installing R
Install R version 4.3 or greater. For more information, or more detailed instructions for select Linux distributions, refer to the R Project website.
R installation on Ubuntu:
- On the terminal, enter the following commands to install R and its dependencies:
$ sudo apt-get update
$ sudo apt-get install libcurl4-openssl-dev libxml2-dev libssl-dev
$ sudo apt-get install r-base r-base-dev
- You can now proceed with Installing DESeq2 (below)
R installation on Centos or Red Hat Enterprise Linux:
- On the terminal, enter the following commands to install Rs dependencies:
$ sudo yum install curl
$ sudo yum install libcurl libcurl-devel
$ sudo yum install libxml2 libxml2-devel
- Now install R with the following command:
$ sudo yum install R
- You can now proceed with DESeq2 installation
R installation from source in your home directory:
You should only install R from source in your home directory if you do not have root access.
-
Download the R source archive from the R project website
-
On the terminal, go to the directory where you downloaded the the source
-
Extract the archive:
$ tar -xvzf R-4.3.Y.tar.gz
(Replace 4.3.Y with the version you are installing)
- Enter the source directory:
$ cd R-4.3.Y
- Run the following commands to build and install R:
$ ./configure --prefix=$HOME/R
$ ./configure --prefix=$HOME/R
Note that if the first command fails because of missing libraries, it may still be possible to install these dependencies without root access.
This will install R in your home directory under $HOME/R/bin
.
You can add this directory to your PATH variable or run R from the terminal with the command ~/R/bin/R
.
You can now proceed with DESeq2 installation.
Installing DESeq2
Once you have installed R, you must install Bioconductor, then use Bioconductor to install DESeq2.
From the terminal, launch R normally using the command: R
- Install Bioconductor:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
- Install DESeq2:
BiocManager::install("DESeq2")
Note that you will be asked to install any dependent packages that are not already installed. This step can take some time: wait for it to finish before proceeding
- Quit R by entering
CTRL-D
. You will be asked whether you'd like to save your workspace image; you may answer 'n' (answer 'y' if you'd like to use R from the terminal in the future and wish to save the history of the commands you used).
If you are on a version of R that does not install DESeq2 version 1.14.1 by default you may experience problems. You can install version 1.14.1 by performing the following steps:
- Download DESeq2 1.14.1 from https://www.bioconductor.org/packages/3.4/bioc/src/contrib/DESeq2_1.14.1.tar.gz to your computer, for example, /home/geneious/Downloads
- Run R
-
Remove any other installed DESeq2 versions with remove.packages("DESeq2").
Note: this will remove all installed DESeq2 versions - To install DESeq2 1.14.1, type install.packages("/path/to/DESeq2") which in this example would be install.packages("/home/geneious/Downloads/DESeq2_1.14.1.tar.gz") and wait for the process to complete
- Once it has completed exit R. You should now be able to run DESeq2 from within Geneious using the R path you used in step 4.