Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenic mechanisms from multiple perspectives.We analyzed a Huntington's disease transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in Huntington's disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation.In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

Aix Marseille Univ INSERM MMG Marseille France

Barcelona Supercomputing Center Barcelona Spain

CNRS Marseille France

Department of Bioinformatics BiGCaT NUTRIM MHeNs Maastricht University Maastricht The Netherlands

Department of Biostatistics and Medical Informatics School of Medicine Acibadem Mehmet Ali Aydinlar University Istanbul Turkey

Department of Human Genetics Leiden University Medical Center Leiden The Netherlands

Institute of Biology and Medical Genetics 1st Faculty of Medicine Charles University Prague Czech Republic

Maastricht Centre for Systems Biology Maastricht University Maastricht The Netherlands

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