The pandemic has shown that scientists across the globe are willing to join forces to better understand the virus and to develop new treatments and vaccines. Key scientific achievements in this respect – some of them already in the early months of the pandemic – include the identification and characterization of entry factors (Sungnak et al. 2020), receptors (Ziegler et al. 2020), immunological responses (Bernardes et al. 2020), and disease severity (Schulte-Schrepping et al., 2020; Aschenbrenner et al. 2021). These breakthroughs were possible because scientists from around the world shared and re-used large amounts of data and collaborated across labs e.g. within the Human Cell Atlas Lung Biological Network.
To facilitate this collaborative effort even further, FASTGenomics developed a new “Project Feature”. With this new feature, you can publicly share complete reseach projects. These projects include a description, all necessary data (in interoperable and re-usable formats) together with the associated analyses (with Scanpy, Seurat and more). All this comes in a fully virtualized and reproducible form and can, e.g., be published along with your paper. FASTGenomics projects thus enable the reproduction, reanalysis and re-use of your scientific results.
As first examples of these new interactive projects we highlight here several COVID-19 studies (more projects can be found on https://beta.fastgenomics.org):
- Schulte-Schrepping et al., 2020, Severe COVID-19 is marked by a dysregulated myeloid cell compartment [Project Page]
- Bernardes et al. 2020, Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19 [Project Page]
- Aschenbrenner et al. 2021, Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients [Project Page]
All projects, their data and analyses can be inspected without the need to register. This makes projects an ideal supplement to your paper. To explore the data, we offer cellxgene visualizations as well as Jupyter notebooks for your analyses in Python (e.g. with Scanpy) or R (Seurat). You also have the opportunity to publish shiny apps such as, e.g., Shiny-Seq by Sundararajan et al. (2019).[blog_2.jpg]
The successful collaboration and sharing of data has been facilitated by FASTGenomics – the data management and analytics platform for reproducible research. FASTGenomics is developed by Comma Soft in Bonn, Germany, who is a member of the HCA Lung Biological Network and Lead-Developer of the Human Lung Cell Atlas portal for the HCA project discovAIR.