Introduction

nf-core/diseasemodulediscovery is a bioinformatics pipeline for network medicine hypothesis generation, designed for identifying active/disease modules. Developed and maintained by the RePo4EU consortium, it aims to characterize the molecular mechanisms of diseases by analyzing the local neighborhood of disease-associated genes or proteins (seeds) within the interactome. This approach can help identify potential drug targets for drug repurposing.

• Module inference (all enabled by default):
  • DOMINO
  • DIAMOnD
  • ROBUST
  • ROBUST (bias aware)
  • 1st Neighbors
  • random walk with restart (RWR)
• Evaluation
  • Over-representation analysis (g:Profiler)
  • Functional coherence analysis (DIGEST)
  • Network topology analysis (graph-tool)
  • Overlaps between different disease modules
  • Seed set permutation-based evaluation (robustness and seed rediscovery, enabled by --run_seed_permutation)
  • Network permutation-based evaluation (robustness, enabled by --run_network_permutation)
• Export to the network medicine web visualization tool Drugst.One
• Drug prioritization using the API of Drugst.One
• Visualization of the module networks (graph-tool, pyvis)
• Annotation with biological data (targeting drugs, side effects, associated disorders, cellular localization) queried from NeDRexDB and conversion to BioPAX format.
• Result and evaluation summary (MultiQC)

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

[!WARNING] The pipeline is still under development. In order to run it, please use the option -r dev

Test your setup

bash nextflow run nf-core/diseasemodulediscovery \ -profile <docker/singularity>,test \ --outdir <OUTDIR>

This will run the pipeline with a small test dataset. Results will be saved to the specified <OUTDIR>. Use -profile to set whether docker or singularity should be used for software deployment.

Running the pipeline

Now, you can run the pipeline with your own data using:

bash nextflow run nf-core/diseasemodulediscovery \ -profile <docker/singularity> \ --seeds <SEED_FILE> \ --network <NETWORK_FILE> \ --outdir <OUTDIR>

This will run the pipeline based on the provided <SEED_FILE> and <NETWORK_FILE>. Results will be saved to the specified <OUTDIR>. Use -profile to set whether docker or singularity should be used for software deployment.

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/diseasemodulediscovery was originally written by the RePo4EU consortium.

We thank the following people for their extensive assistance in the development of this pipeline:

• Johannes Kersting (TUM)
• Lisa Spindler (TUM)
• Quirin Manz (TUM)
• Quim Aguirre (STALICLA)
• Chloé Bucheron (University Vienna)

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

If you want to include an additional module identification approach, please see this guide. For further information or help, don't hesitate to get in touch on the Slack #diseasemodulediscovery channel (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.