LinkedImm: a linked data graph database for integrating immunological data

BMC Bioinformatics

Shrikant Pawar¹  Syed Ahmad Chan Bukhari²  Jeff Mandell³  Kei-Hoi Cheung⁴  Steven H. Kleinstein⁵ 

[1] Department of Genetics, Yale School of Medicine, New Haven, CT, USA;Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, New York, NY, USA;Program in Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, CT, USA;Program in Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, CT, USA;Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA;Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA;Program in Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, CT, USA;Department of Pathology, Yale School of Medicine, New Haven, CT, USA;

关键词: Ontology;    Knowledgebase;    Graph database;    Immunology;    Influenza vaccine;   

DOI  :  10.1186/s12859-021-04031-9

来源: Springer

【 摘 要 】

BackgroundMany 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.ResultsWe 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.ConclusionWe 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.

【 授权许可】

CC BY