Detection of RNA Outlier Pipeline

The detection of RNA Outliers Pipeline (DROP) is an integrative workflow to detect aberrant expression, aberrant splicing, and mono-allelic expression from raw sequencing files.

The manuscript is available in Nature Protocols. SharedIt link.

What's new

Version 1.2.2 fixes some critical bugs that affected the performance of the aberrantExpression pipeline, and allows sample IDs to be numeric.

As of version 1.2.1 DROP has a new module that performs RNA-seq variant calling. The input are BAM files and the output either a single-sample or a multi-sample VCF file (option specified by the user) annotated with allele frequencies from gnomAD (if specified by the user). The sample annotation table does not need to be changed, but several new parameters in the config file have to be added and tuned. For more info, refer to the documentation.

Also, the integration of external split and non-split counts to detect aberrant splicing is now possible. Simply specify in a new column in the sample annotation the directory containing the counts. For more info, refer to the documentation.

Quickstart

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

mamba create -n drop_env -c conda-forge -c bioconda drop --override-channels

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, build hg19, Technical University of Munich:

• 269 strand specific fibroblasts, build hg19, Technical University of Munich:

• 49 tissues, each containing hundreds of samples, non-strand specific, build hg19, GTEx:

• 49 tissues, each containing hundreds of samples, non-strand specific, build hg38, GTEx:

• 139 strand specific fibroblasts, build hg19, Baylor College of Medicine:

• 125 strand specific blood, build hg19, Baylor College of Medicine:

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

Citation

If you use DROP in research, please cite our manuscript.

Furthermore, if you use the aberrant expression module, also cite OUTRIDER; if you use the aberrant splicing module, also cite FRASER; and if you use the MAE module, also cite the Kremer, Bader et al study and DESeq2.

For the complete set of tools used by DROP (e.g. for counting), see the manuscript.

Acknowledgements and Funding

The DROP team is composed of members from the Gagneur lab at the Department of Informatics and School of Medicine of the Technical University of Munich (TUM) and The German Human Genome-Phenome Archive (GHGA). The team has been funded by the German Bundesministerium für Bildung und Forschung (BMBF) through the e:Med Networking fonds AbCD-Net, Medical Informatics Initiative CORD-MI, and ERA PerMed project PerMiM. We would like to thank all the users for their feedback.