Development of a platform for the exploration of the CHRIS data via multi-level network medicine
General data
| Credits (SWS) | 12 (10 SWS) |
| Module level | Master |
| Language | German/English |
| Total hours | 360 h |
| Weekly time slot | 2-3 days/week |
| Block part in semester break | to be determined |
Time schedule of the internship
| Feb - Mar 2024 | Kickoff meeting and assignment of projects and teams |
| Apr - Jul 2024 | Division of the project work, interim presentations |
| Jul - Aug 2024 | Blockpart for finalizing the project work, writing the report, and preparing the final presentation |
Requirements and prior knowledge
Bachelor's degree in Bioinformatics, in particular successful completion of the GoBi module. Good Python programming skills. Interest in data visualization and network medicine. Previous experience in software development is an advantage, but not a must.
Population-based epidemiological cohorts such as the Cooperative Health Research in South Tyrol (CHRIS) study [1] provide an extensive overview of the health status of the general population, effectively capturing the standard physiological range and a spectrum of pathological states, unlike disease-focused studies. In particular, integrating molecular profiling data (genomics and metabolomics) with clinical and lifestyle data enables the identification of physiological changes potentially leading to complications or indicating the prevalence or early signs of diseases.
Contrasting with hypothesis-driven disease-focused studies, population-based cohorts offer versatility in hypothesis testing and generation, based on statistically significant data associations. Network-based approaches are particularly fitting for this purpose. With multi-omics integration tools [2] and graph-based machine-learning techniques for disease association identification [3], this type of explorative analysis can be made open to biomedical researchers who do not necessarily have experience with data analysis or machine learning.
In response to these advancements and the availability of the CHRIS study, we are currently developing a platform for dynamic exploration of epidemiological population cohorts via multi-level network medicine. Intricate association scores among genetic variants, metabolites, and additional meta information will be computed and a multi-omics network will be constructed to identify clinically significant subgroups and to outline the distinctions between groups based on various conditions typically found in a healthy cohort.
Throughout this internship, our objective is to develop a first prototype of the DyHealthNet platform. The first step involves constructing a static multi-omics network using simulated data, where complex association scores between various node types will be computed. To augment the data available in CHRIS, integration of data from external public sources, like OMIM [4] for genetic variant interpretation and STRING [5] for mapping gene-gene interactions, is planned. This static multi-omics network will be made available for explorative analysis through a graphical interface, employing Django for backend development and a JavaScript framework such as AngularJS for the frontend. The platform will enable users to browse the multi-omics network across multiple data types, and to discover relevant associations via filtering and network analysis methods, like node centrality analysis.
Literature
- Pattaro, C., Gögele, M., Mascalzoni, D., Melotti, R., Schwienbacher, C., De Grandi, A., Foco, L., D’Elia, Y., Linder, B., Fuchsberger, C., Minelli, C., Egger, C., Kofink, L. S., Zanigni, S., Schäfer, T., Facheris, M. F., Smárason, S. V., Rossini, A., Hicks, A. A., … Pramstaller, P. P. (2015). The Cooperative Health Research in South Tyrol (Chris) study: Rationale, objectives, and preliminary results. Journal of Translational Medicine, 13, 348. https://doi.org/10.1186/s12967-015-0704-9
- Hawe, J. S., Theis, F. J., & Heinig, M. (2019). Inferring interaction networks from multi-omics data. Frontiers in Genetics, 10. https://www.frontiersin.org/articles/10.3389/fgene.2019.00535
- Wang, T., Shao, W., Huang, Z., Tang, H., Zhang, J., Ding, Z., & Huang, K. (2021). MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nature Communications, 12(1), 3445. https://doi.org/10.1038/s41467-021-23774-w
- Amberger, J. S., Bocchini, C. A., Scott, A. F., & Hamosh, A. (2019). OMIM.org: Leveraging knowledge across phenotype-gene relationships. Nucleic Acids Research, 47(D1), D1038–D1043. https://doi.org/10.1093/nar/gky1151
- Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., & Mering, C. von. (2019). STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613. doi.org/10.1093/nar/gky1131