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

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            Abstract

            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.

            Content

            Author and article information

            Conference
            RExPO24 Conference
            REPO4EU
            2 May 2024
            Affiliations
            [1 ] Technical University of Munich, Munich (Germany);
            [2 ] STALICLA SL, Barcelona (Spain);
            [3 ] University Vienna, Vienna (Austria);
            [4 ] University of Hamburg, Hamburg (Germany);
            Author notes
            Author information
            https://orcid.org/0000-0002-3882-0093
            https://orcid.org/0000-0002-5706-2718
            https://orcid.org/0000-0002-1971-1350
            https://orcid.org/0009-0004-0699-6475
            https://orcid.org/0009-0007-3502-3033
            https://orcid.org/0009-0007-4106-4659
            https://orcid.org/0000-0002-6171-1215
            https://orcid.org/0000-0002-3466-6535
            https://orcid.org/0000-0002-0941-4168
            Article
            10.58647/REXPO.24000043.v1
            0b5b747f-1f56-415a-a166-98e05430fd54

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO24
            3
            Munich, Germany
            3-5 July 2024
            History
            : 2 May 2024
            Funding
            Funded by: funder-id , European Union;
            Award ID: 101057619
            Categories

            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Bioinformatics & Computational biology
            disease modules,drug repurposing,pipeline,Nextflow,BioPAX,Docker,automatization,reproducibility

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