Abstract
BackgroundSystems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.ResultsWe show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.ConclusionsOur study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0102-8) contains supplementary material, which is available to authorized users.
Highlights
A major effort in translational medicine is to understand the molecular basis of disease [1, 2]
Here we illustrate the use of a graph database for facilitating hypothesis generation on disease mechanisms
We describe four queries related to asthma, a complex respiratory disease whose symptoms include airway inflammation and remodelling
Summary
A major effort in translational medicine is to understand the molecular basis of disease [1, 2]. In order to build a higher level picture of the underlying processes involved in the disease pathology, it is necessary to integrate various classes of heterogeneous information, and to explore the complex relationships between entities such as diseases, candidate genes, proteins, interactions and pathways. Some candidate genes may encode large families of sequence-similar proteins, and may be Lysenko et al BioData Mining (2016) 9:23 involved in multiple protein-protein interactions Identification of these wider relationships between entities can help in providing biological context and in hypothesis generation on disease mechanisms. We describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data
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