Detection of RNA Outlier Pipeline

The manuscript main file, supplementary figures and table can be found in the manuscript folder or in protocol exchange.

DROP documentation

Dependencies

Programming languages:

• python >= 3.6.7
  • pip >= 19.1
  • we recommend using a virtual environment e.g. anaconda
• R >= 3.5 (https://www.r-project.org/)

R packages

Bioconductor and base R packages need to be installed. The packages are listed in drop/requirementsR.txt. A script for installing these packages is provided. From the repository root just execute:

Rscript drop/installRPackages.R drop/requirementsR.txt

Other packages

• samtools >= 1.7 (https://www.htslib.org/download/)
• bcftools (newest) (https://github.com/samtools/bcftools)
• tabix (https://www.htslib.org/download/)
• GATK (https://software.broadinstitute.org/gatk/)
• graphviz (https://www.graphviz.org/)
• pandoc (https://pandoc.org/)

Installation

Make sure that all of the above listed dependencies are installed. Then install DROP from github using pip. For this you need to recursively clone the repository with all its submodules first.

git clone https://github.com/gagneurlab/drop.git --recurse-submodules

Install DROP (activate your python environment if you are using one)

# conda activate drop_env
cd drop
pip install .

Alternatively, you can also install it directly without cloning

pip install git+https://github.com/gagneurlab/drop.git

Installation time (including all dependencies): ~ 1h

Initialize a project

DROP projects are initialized in a separate directory dedicated to the analysis project. Calling the initialization command creates the necessary files.

cd <project/path>
drop init

Set up the demo project

First, install the drop module according to installation. Initialize the demo directory with a custom test project path. In the following we will use $HOME/drop_demo as <project/path>.

cd $HOME/drop_demo
drop demo

This command downloads the demo data, initializes DROP and adapts the config file paths to your current project directory. Now the pipeline is ready to be executed using snakemake.

snakemake -n # dryrun
snakemake

Once the pipeline has run through, you will find the output in the $HOME/drop_demo/Output. It will consist of processed data, results and HTML pages. In order to view the complete HTML summary, open $HOME/drop_demo/Output/htmlOutput/drop_demo_index.html in the browser.

Expected runtime: 30 min

Set up a custom project

Install the drop module according to installation and initialize the project in a custom project directory.

Prepare the input data

Create a sample annotation that contains the sample IDs, file locations and other information necessary for the pipeline. Edit the config file to set the correct file path of sample annotation and locations of non-sample specific input files. For these steps, please refer to the documentation.

Execute the pipeline

Once these files are set up, you can execute a dry run from your project directory

snakemake -n

This shows you the rules of all subworkflows. Omit -n if you are sure that you want you execute all printed rules. You can also invoke single workflows explicitly e.g. for aberrant splicing with

snakemake aberrantExpression -n