Summary Reliable and integrated data is a prerequisite for effective research on the recent COVID-19 pandemic. The CovidGraph project integrates and connects heterogeneous COVID-19 data in a knowledge graph, referred to as “CovidGraph”. It provides easy access to multiple data sources through a single point of entry and enables flexible data exploration. Availability and Implementation More information on CovidGraph is available from the project website: https://healthecco.org/covidgraph/. Source code and documentation are provided on GitHub: https://github.com/covidgraph. Supplementary information Supplementary data is available at Bioinformatics online.

HealthECCO is the driving force behind the COVID-19 knowledge graph spanning multiple biomedical data domains. One way to access CovidGraph is SemSpect, an interface designed for data exploration in graphs. To showcase the possibilities that arise from integrating a variety of COVID-19 related data sources over the last three years, we present three use cases from the (bio-)medical domain. Availability: The project is open source and freely available from: https://healthecco.org/covidgraph/. The source code and documentation are available on GitHub: https://github.com/covidgraph.

MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are involved in the modulation of the DNA-damage response (DDR) and upon exposure to ionizing radiation (IR), their expression fluctuates. In this study, we propose a workflow that enables the creation of regulatory networks by integrating transcriptomics data as well as regulatory data in order to better understand the interplay between genes, transcription factors (TFs), miRNAs, and lncRNAs in the cellular response to IR. We preprocessed and analyzed publicly available gene expression profiles and then applied our consensus and integration approach using open source data and tools. To exemplify the benefits of our proposed workflow, we identified a total of 32 differentially expressed transcripts corresponding to 20 unique differentially expressed genes (DEGs) and using these DEGs, we constructed a regulatory network consisting of 106 interactions and 100 nodes (11 DEGs, 78 miRNAs, 1 DEG acting as a TF, and 10 lncRNAs). Overrepresentation analyses (ORAs) furthermore linked our DEGs and miRNAs to annotations pertaining to the DDR and to IR. Our results show that MDM2 and E2F7 function as network hubs, and E2F7, miR-25-3p, let-7a-5p, and miR-497-5p are the four nodes with the highest betweenness centrality. In brief, our workflow, that is based on open source data and tools, and that generates a regulatory network, provides novel insights into the regulatory mechanisms involving miRNAs and lncRNAs in the cellular response to IR.