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miRge is a logical, ultrafast, small RNA-seq solution to process samples in a highly multiplexed fashion, resulting in dramatically decreased computational requirements when processing sets of samples in a single run.

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An overview of the approach implemented in miRge can be found at https://baraslab.github.io/miRge/.

Please cite https://www.ncbi.nlm.nih.gov/pubmed/26571139 when using miRge in publications, thanks!

Installation

Obtaining miRge:

miRge may be cloned from this repository by the following command:

git clone https://github.com/BarasLab/miRge.git

System/Perl/Python Dependencies:

All required libraries may be installed on a ubuntu variant with this command:

sudo apt-get update

sudo apt-get install libgd-graph-perl libhtml-table-perl python-setuptools python-dev unzip

Cutadapt:

pip install cutadapt>=1.8.1 or easy_install cutadapt>=1.8.1

OR if you lack root privs

pip install --user cutadapt>=1.8.1 or easy_install --user cutadapt>=1.8.1

Note: if you install it as a non-root user, you must ensure the install location is on your path, or specify the location of cutadapt with the --cutadapt argument. This is usually in ~/.local/bin/cutadapt, but may vary with your particular distribution. At this time, miRge has been tested with cutadapt versions 1.8.1 through 1.11. Future versions may break compatibility

Bowtie:

This version may not be the latest, visit the official Bowtie website for the latest version

wget http://sourceforge.net/projects/bowtie-bio/files/bowtie/1.1.1/bowtie-1.1.1-linux-x86_64.zip/download

It may be saved as 'download' or 'bowtie-xxx', so run 'unzip download.zip' to extract it to a given location

Sequence Libraries:

From within the directory miRge is installed in, run:

wget http://atlas.pathology.jhu.edu/baras/miRge/miRge.seqLibs.tar.gz

tar -zxvf miRge.seqLibs.tar.gz

User Manual

https://baraslab.github.io/miRge/miRge/miRge_help.html

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miRge is a logical, ultrafast, small RNA-seq solution to process samples in a highly multiplexed fashion, resulting in dramatically decreased computational requirements when processing sets of samples in a single run.

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