rsubioinfogroup/drop repository overview

⁠🧬 RSU-DROP

Dockerized environment for the DROP framework (Detection of RNA Outlier Pipeline) for RNA-seq based rare disease and aberrant event analysis.

This image provides a portable and reproducible environment for running:

• Aberrant Expression (OUTRIDER)
• Aberrant Splicing (FRASER)
• Monoallelic Expression (MAE)
• RNA variant-related workflows

The container is designed for:

• local execution
• HPC environments
• Azure
• DNAnexus
• cloud and shared compute systems

⁠📦 Docker Image

docker pull rsubioinfogroup/drop:latest

⁠🚀 Run the Container

Run using a mounted persistent project directory:

docker run --rm -it \
  -v /path/to/drop-project:/drop/analysis \
  rsubioinfogroup/drop:latest

Windows example:

docker run --rm -it ^
  -v C:/Users/YourName/drop-project:/drop/analysis ^
  rsubioinfogroup/drop:latest

⁠📂 Example Project Structure

analysis/
├── Data/
├── Output/
├── config.yaml
├── Scripts/
├── .drop/
└── Snakefile

⁠⚙️ Initialize a Demo Project

Inside the container:

drop demo

This downloads lightweight demo data and creates a complete runnable DROP project.

⁠▶️ Run the Full Workflow

snakemake --cores 8

⁠▶️ Run Individual Modules

⁠Aberrant Expression (OUTRIDER)

snakemake aberrantExpression --cores 8

⁠Aberrant Splicing (FRASER)

snakemake aberrantSplicing --cores 8

⁠Monoallelic Expression (MAE)

snakemake monoallelicExpression --cores 8

⁠RNA Variant Calling

snakemake rnaVariantCalling --cores 8

⁠🧠 Features

• Preconfigured DROP environment
• Conda-based reproducible dependency management
• Snakemake workflow execution
• Non-root execution user
• Persistent mounted project support
• Incremental rerun support
• Compatible with local, HPC, and cloud storage systems
• Suitable for large-scale RNA-seq workflows

⁠📊 Included Software

• DROP
• OUTRIDER
• FRASER
• GATK4
• STAR
• samtools
• bcftools
• Snakemake
• R / Bioconductor ecosystem

⁠🔄 Incremental Workflow Execution

To support incremental reruns and avoid recomputing previous samples, preserve:

Output/processed_data
Output/processed_results
.drop

between executions.

This allows adding new samples without recalculating existing intermediate results.

⁠☁️ Cloud / HPC Compatibility

The container itself is stateless.

Input data and workflow outputs should be mounted from:

• local disks
• shared HPC storage
• Azure storage
• DNAnexus project mounts
• network-attached storage systems

This enables reproducible execution across heterogeneous compute environments.

⁠🧾 Example Usage with Persistent Storage

docker run --rm -it \
  -v /data/drop-project:/drop/analysis \
  rsubioinfogroup/drop:latest

⁠🔬 Source

Original DROP framework:

https://github.com/gagneurlab/drop⁠

Containerized and adapted for reproducible deployment workflows.