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.

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Conference

Publisher: REPO4EU

Publication date (Electronic, pub): 19 July 2024

Affiliations

[1 ] Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Munich, Germany ( https://ror.org/02kkvpp62)

[2 ] Discovery and Data Science (DDS) Unit, STALICLA SL, Barcelona, Spain;

[3 ] Max Perutz Labs, Vienna BioCenter Campus, Department of Structural and Computational Biology, University of Vienna, Vienna, Austria ( https://ror.org/03prydq77)

[4 ] Ludwig Boltzmann Gesellschaft, Institute for Network Medicine, Vienna, Austria ( https://ror.org/01v1jam04)

[5 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany ( https://ror.org/00g30e956)

Author notes

[* ]Email: johannes.kersting@ 123456tum .

Author information

Johannes Kersting https://orcid.org/0000-0002-3882-0093

Quirin Manz https://orcid.org/0000-0002-5706-2718

Joaquim Aguirre-Plans https://orcid.org/0000-0002-1971-1350

Chloé Bucheron https://orcid.org/0009-0004-0699-6475

Lisa Spindler https://orcid.org/0009-0007-3502-3033

Tanja Pock https://orcid.org/0009-0007-4106-4659

Fernando Delgado-Chaves https://orcid.org/0000-0002-6171-1215

Cristian Nogales https://orcid.org/0000-0001-7535-0417

Jan Baumbach https://orcid.org/0000-0002-0282-0462

Jörg Menche https://orcid.org/0000-0002-1583-6404

Emre Guney https://orcid.org/0000-0002-3466-6535

Markus List https://orcid.org/0000-0002-0941-4168

Article

DOI: 10.58647/REXPO.24011

SO-VID: 41319ae3-83d4-4f35-b345-6ada123fb68e

License:

Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

Conference name: RExPO24

Conference number: 3

Conference location: Munich, Germany

Conference date: 3-5 July 2024

History

Product

REPO4EU

Funding

Funded by: funder-id http://dx.doi.org/10.13039/100018696, HORIZON EUROPE Health;

Award ID: 101057619

References

Levi Hagai, Elkon Ran, Shamir Ron. DOMINO: a network‐based active module identification algorithm with reduced rate of false calls. Molecular Systems Biology. Vol. 17(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Ghiassian Susan Dina, Menche Jörg, Barabási Albert-László. A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome. PLOS Computational Biology. Vol. 11(4)2015. Public Library of Science (PLoS). [Cross Ref]
Bernett Judith, Krupke Dominik, Sadegh Sepideh, Baumbach Jan, Fekete Sándor P, Kacprowski Tim, List Markus, Blumenthal David B. Robust disease module mining via enumeration of diverse prize-collecting Steiner trees. Bioinformatics. Vol. 38(6):1600–1606. 2022. Oxford University Press (OUP). [Cross Ref]
Di Tommaso Paolo, Chatzou Maria, Floden Evan W, Barja Pablo Prieto, Palumbo Emilio, Notredame Cedric. Nextflow enables reproducible computational workflows. Nature Biotechnology. Vol. 35(4):316–319. 2017. Springer Science and Business Media LLC. [Cross Ref]

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Comment on this article

[1] Levi Hagai, Elkon Ran, Shamir Ron. DOMINO: a network‐based active module identification algorithm with reduced rate of false calls. Molecular Systems Biology. Vol. 17(1)2021. Springer Science and Business Media LLC. [Cross Ref]

[2] Ghiassian Susan Dina, Menche Jörg, Barabási Albert-László. A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome. PLOS Computational Biology. Vol. 11(4)2015. Public Library of Science (PLoS). [Cross Ref]

[3] Bernett Judith, Krupke Dominik, Sadegh Sepideh, Baumbach Jan, Fekete Sándor P, Kacprowski Tim, List Markus, Blumenthal David B. Robust disease module mining via enumeration of diverse prize-collecting Steiner trees. Bioinformatics. Vol. 38(6):1600–1606. 2022. Oxford University Press (OUP). [Cross Ref]

[4] Di Tommaso Paolo, Chatzou Maria, Floden Evan W, Barja Pablo Prieto, Palumbo Emilio, Notredame Cedric. Nextflow enables reproducible computational workflows. Nature Biotechnology. Vol. 35(4):316–319. 2017. Springer Science and Business Media LLC. [Cross Ref]