There are a lot of different methods to analyze miRNA-Seq and RNA-Seq data. majority of them involved a lot of installations and human efforts. In order to resolve this issue many different analysis suits and software packages are available online. Today I am going to analyze data using miARma-Seq suit, which was published in nature scientific reports in May 2016 (Original Article). Other than miARma-Seq many other tools and analysis suits are available which are mentioned below:

  1. Tools gene expression analysis, like ExpressionPlot5, GENE-counter6, RobiNA7, TCW8, Grape RNA-Seq9 or MAP-RSeq10
  2. Tools focuses on the analysis of miRNA expression profiles, such as DSAP11, miRanalyzer12, miRExpress13, miRNAkey14, iMir15, CAP-miRSeq16, mirTools 2.017 or sRNAtoolbox18
  3. Tools implemented to perform both RNA-Seq and miRNA-Seq analysis, such as wapRNA19, eRNA20, BioVLAB-MMIA-NGS21 or Omics Pipe22
  4. Methods integrating several software enabling different type of NGS analyses are GALAXY (https://galaxyproject.org/), QuasR23, RAP24, Subread/edgeR25, ViennaNGS26 suite

I found miARma-Seq most convenient and easy to install among all available options, so, I am going to install it and apply it on my datasets. I am starting from completely scratch on Ubuntu OS, using amazon cloud EC2 cloud. First we have to install all pre-requisites but if you are using already working server may be many of them are already installed on the machine.

  • Install make using "sudo apt install make"
  • Install GCC compiler, I installed using following command:

sudo apt-get update && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \
sudo apt-get update && \
sudo apt-get install gcc-snapshot -y && \
sudo apt-get update && \
sudo apt-get install gcc-6 g++-6 -y && \
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-6 60 --slave /usr/bin/g++ g++ /usr/bin/g++-6 && \
sudo apt-get install gcc-4.8 g++-4.8 -y && \
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 60 --slave /usr/bin/g++ g++ /usr/bin/g++-4.8;

  • Test GCC installation by checking its version

gcc -v

GCC Installation

  • Install perl, I installed perl 5.6.1 but you can install the latest available version

wget http://www.cpan.org/src/5.0/perl-5.6.1.tar.gz
tar -zxvf perl-5.6.1.tar.gz
cd perl-5.6.1/
rm -f config.sh Policy.sh
sh Configure -de
make test
make install

  • Install R

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'
sudo apt-get update
sudo apt-get install r-base

  • Test R installation

sudo -i R


  • Install JAVA

sudo apt-get update
sudo apt-get install default-jre
sudo apt-get install default-jdk

  • Test JAVA installation

java -version

  • Install Bioconductor packages in R, you need to start R with administrative permissions or use local R but don't forget to add path iof local R in bashrc

sudo R

  • Install miARmaSeq suite 

mkdir NGS
cd NGS
curl -L -O https://bitbucket.org/cbbio/miarma/get/master.tar.bz2
tar -xjf master.tar.bz2
cd cbbio-miARma-*
ls -l

  • Install miARmaSeq Examples

curl -L -O https://sourceforge.net/projects/miarma/files/Examples/Examples_miARma_mRNAs.tar.bz2
tar -xjf Examples_miARma_mRNAs.tar.bz2


  • Test miARma

perl miARma Examples/basic_examples/mRNAs/1.Quality/1.Quality.ini --check
perl miARma Examples/basic_examples/mRNAs/1.Quality/1.Quality.ini

  • Download Genome or just give path of already downloaded genome in .ini files and matching .gtf files. I preferred to download genome from iGenome because they already have indexes of Bowtie1, Bowtie2, BWA and matching annotation files.

wget ftp://igenome:This email address is being protected from spambots. You need JavaScript enabled to view it./Homo_sapiens/UCSC/hg19/Homo_sapiens_UCSC_hg19.tar.gz
tar -zxvf Homo_sapiens_UCSC_hg19.tar.gz


You can find miARma manual on the following link for further details (Manual). You can use Bowtie1, Bowtie2, HiSAT and STAR for mapping using this suite and Edge R + NOISeq for differential expression analysis. It also provides functional annotation.

If you want to change any default commands of miARma e.g. I wanted to use star but because of RAM limitations I was unable to use it, so, I changed the command in /lib/miARma/Aligner.pm. You can modify the default commands, only if you know what you are doing.

I hope you will enjoy your analysis with miARma. Please feel free to comment your feedback.


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