A Nextflow Pipeline for Network-Based Disease Module Identification and Validation

Disease modules provide unique insights into the mechanisms of complex diseases and lay the foundation for mechanistic drug repurposing. Algorithms for their identification leverage biological networks to extend an initial set of disease-associated genes (seeds) into subnetworks reflecting biological processes likely to be integral components of the investigated disease. These subnetworks can unveil causal pathways and provide drug repurposing efforts with promising new targets for therapeutics.Various computational methods have been developed for disease module identification. Since these methods differ in their modeling assumptions and techniques, evaluating various tools across different parameters to optimize for a specific use case is advisable. However, this can be tedious since the individual tools require specific installation and input preparation procedures. Moreover, identifying the best modules is not straightforward and requires topological and biological validation strategies.To mitigate this, we developed a comprehensive pipeline for disease module identification and validation utilizing the workflow software Nextflow. Our pipeline automatically deploys software dependencies using Docker, making installation easy. It prepares the inputs for and runs five popular module detection tools. The generated outputs are annotated with drug-repurposing relevant information, converted into a unified BioPAX format, and extensively validated. This includes assessing the biological relevance based on overrepresentation analysis and the dedicated software DIGEST, as well as robustness and consistency analyses.With our contribution, we allow the community to systematically compare different approaches for disease module discovery, thus contributing to robustness and reproducibility in systems and network medicine.