nickhsmith / drop

Pipeline to find aberrant events in RNA-Seq data, useful for diagnosis of rare disorders

Detection of RNA Outlier Pipeline

The manuscript is now available in Nature Protocols. SharedIt link.

Quickstart

DROP is available on bioconda. We recommend using a dedicated conda environment. (installation time: ~ 10min)

conda install -c conda-forge -c bioconda drop

Test installation with demo project

mkdir ~/drop_demo
cd ~/drop_demo
drop demo

The pipeline can be run using snakemake commands

snakemake -n # dryrun
snakemake --cores 1

Expected runtime: 25 min

For more information on different installation options, refer to the documentation

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. The requirements are described in 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 and specify the number of cores with --cores if you are sure that you want you execute all printed rules. You can also invoke single workflows explicitly e.g. for aberrant expression with:

snakemake aberrantExpression --cores 10

Datasets

The following publicly-available datasets of gene counts can be used as controls. Please cite as instructed for each dataset.

• 154 non-strand specific fibroblasts, Technical University of Munich:

• 269 strand specific fibroblasts, Technical University of Munich:

• 139 strand specific fibroblasts, Baylor College of Medicine:

• 125 strand specific blood, Baylor College of Medicine:

If you want to contribute with your own count matrices, please contact us: yepez at in.tum.de