CoFGen - A data portal for COVID-19 Functional Genomics

Over the last two years, SARS-CoV-2 and COVID-19 were and still are the dominating topics in research focusing on health and medicine in general and genomics and functional genomics in specific. In functional genomics, which studies the interplay of genes, signaling pathways and gene products, research is focused on answering questions about the immune response to an infection with SARS-CoV-2 and finding explanations for differing disease courses and severity. During the course of the pandemic, German researchers alone published nearly 25 000 papers on COVID-19 and were one of the most active contributors to research about various topics related to the pandemic.

However, research data and findings are not yet bundled or centralized, and the data sharing culture is still under development. This is why we develop CoFGen, a data portal for research on functional genomics in COVID-19. CoFGen will enable researchers to answer questions about changed biological processes and mechanisms, e.g. regulations of signaling pathways, after an infection with SARS-CoV-2 and further centralize and democratize data storage, as well as the storage of analysis workflows.

Our goal is to collect single-cell and bulk RNA-sequencing datasets and analyses from German research groups, execute basic metadata analyses, and make data more easily available to other researchers focusing on different parts of the immune response to COVID-19. To achieve this, we will work closely together with DeCOI and the Lung Biological Network of the Human Cell Atlas, who are our initial data providers. Storage of datasets, corresponding analysis workflows, as well as data access will be managed by FASTGenomics, a collaborative research effort of Comma Soft AG in Bonn and the LIMES Institute from Bonn University.

Goals 

  • Collect information about raw data from German research groups working on functional genomics in SARS-CoV-2 infections
  • Collect processed data and workflows and make them available to other researchers
  • Analyse metadata across all collected and included studies and generate new knowledge about COVID-19 from CoFGen datasets