LinkedImm: a linked data graph database for integrating immunological data

• Main Authors: Syed Ahmad Chan Bukhari, Shrikant Pawar, Jeff Mandell, Steven H. Kleinstein, Kei-Hoi Cheung
• Format: Article
• Language: English
• Published: BMC 2021-08-01
• Series: BMC Bioinformatics
• Subjects: Ontology; Knowledgebase; Graph database; Immunology; Influenza vaccine
• Online Access: https://doi.org/10.1186/s12859-021-04031-9

Abstract Background Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. Results We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. Conclusion We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.