Using Graph Databases for Portraying and Analysing Biological and Biomedical Networks

Nowadays, huge amounts of data are generated experimentally in systems biology as well in clinics and other healthcare and medical institutions. This has resulted in the emergence of new concepts: big data and NoSQL databases that are becoming more popular and promising especially for analyzing complex interactions that exist in biological networks. These heterogeneous and voluminous data, which are usually semi-structured or unstructured, highly connected and unpredictable, need to be integrated and stored properly. With the growth of data size and data complexity, NoSQL databases have outperformed traditional relational databases for analysis, access and querying. Particularly, to represent various complex relationships among entities in physics, in biological, social and computer networks, graph-based databases are very suitable. These databases are appropriate to represent, store and query heavily interconnected data, especially for large-scale network data that exist in biology. This paper describes the biological and other networks in biomedicine and surveys the most widely used graph databases and software tools and packages and their properties. Additionally, the portraying, analyzing and querying of biological networks are described. The analysis of the biological networks results in gaining very significant insights into the relevant information for the biological processes, such as diseases, interactions and regulatory mechanisms that occur in biological networks, as well as studying their properties.