biochem4j: Integrated and extensible biochemical knowledge through graph databases

Swainston, Neil ORCID: 0000-0001-7020-1236, Batista-Navarro, Riza, Carbonell, Pablo, Dobson, Paul D, Dunstan, Mark, Jervis, Adrian J, Vinaixa, Maria, Williams, Alan R, Ananiadou, Sophia, Faulon, Jean-Loup
et al (show 4 more authors) (2017) biochem4j: Integrated and extensible biochemical knowledge through graph databases. PLOS ONE, 12 (7). e0179130-.

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Biologists and biochemists have at their disposal a number of excellent, publicly available data resources such as UniProt, KEGG, and NCBI Taxonomy, which catalogue biological entities. Despite the usefulness of these resources, they remain fundamentally unconnected. While links may appear between entries across these databases, users are typically only able to follow such links by manual browsing or through specialised workflows. Although many of the resources provide web-service interfaces for computational access, performing federated queries across databases remains a non-trivial but essential activity in interdisciplinary systems and synthetic biology programmes. What is needed are integrated repositories to catalogue both biological entities and–crucially–the relationships between them. Such a resource should be extensible, such that newly discovered relationships–for example, those between novel, synthetic enzymes and non-natural products–can be added over time. With the introduction of graph databases, the barrier to the rapid generation, extension and querying of such a resource has been lowered considerably. With a particular focus on metabolic engineering as an illustrative application domain, biochem4j, freely available at, is introduced to provide an integrated, queryable database that warehouses chemical, reaction, enzyme and taxonomic data from a range of reliable resources. The biochem4j framework establishes a starting point for the flexible integration and exploitation of an ever-wider range of biological data sources, from public databases to laboratory-specific experimental datasets, for the benefit of systems biologists, biosystems engineers and the wider community of molecular biologists and biological chemists.

Item Type: Article
Uncontrolled Keywords: Computational Biology, Internet, User-Computer Interface, Databases, Factual
Depositing User: Symplectic Admin
Date Deposited: 24 Jun 2020 10:37
Last Modified: 18 Jan 2023 23:48
DOI: 10.1371/journal.pone.0179130
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