A Nextflow Pipeline for Network-Based Disease Module Identification and Drug Repurposing

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d298415e263">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, evaluation, and subsequent drug prioritization utilizing the workflow system Nextflow. Our pipeline automatically deploys software dependencies using Docker, making installation easy. It prepares the inputs for and runs six popular module detection tools, including DIAMOnD, DOMINO, and ROBUST. The generated outputs are annotated with biological context information, converted into a unified BioPAX format, and extensively evaluated. The latter includes assessing the biological relevance based on overrepresentation analysis and the tool 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.