Dr. Nadya Doncheva - Network biology approaches for analysis and interpretation of high-throughput data - IPBS-Toulouse, Seminar Room

Le

A 11h00

IPBS-Toulouse, Seminar Room

31400 Toulouse

Nadya Doncheva

University of Copenhagen, Denmark

Network biology approaches for analysis and interpretation of high-throughput data

Networks are a powerful abstraction of biological systems and are useful for modeling and visualizing the complex interplay of the many proteins that make up a cell. As such, biological networks have become a popular tool for interpreting the long lists of proteins or genes identified by proteomics and other high-throughput technologies. This is only possible with the availability of protein network resources such as STRING and network visualization and analysis tools like Cytoscape. To include both resources in the same workflow and combine their strengths, we developed stringApp, a Cytoscape app that makes it easy to import protein–protein interaction networks from STRING into Cytoscape and integrate them with data from associated databases such as DISEASES, TISSUES, and COMPARTMENTS. In this talk, I will introduce the field of network biology and then focus on why and how we develop some of the mentioned resources. I will also present examples of how we apply these to interpret the results from omics studies or otherwise try to understand any biological process or disease.

Selected publications

  • Szklarczyk et al. (2023): The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51:D638-D646
  • Doncheva et al. (2022): Cytoscape stringApp 2.0: Analysis and visualization of heterogeneous biological networks. Journal of Proteome Research 22:637-646
  • Legeay et al. (2020): Visualize omics data on networks with Omics Visualizer, a Cytoscape App. F1000Research 9:157
  • Doncheva et al. (2019): Cytoscape stringApp: Network analysis and visualization of proteomics data. Journal of Proteome Research 18:623-632

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