BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph
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1.
Université Libre de Bruxelles
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2.
Vrije Universiteit Brussel
Description
BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies.
Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions.
All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories.
BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8.
We provide BOCK 2.0 in three formats:
- GraphML (Graph Markup Language): a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).
- XML (Extensible Markup Language): a text-encoding system that is human-readable and compatible with many systems.
- Neo4J import files: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.
Notes
This work was supported by the European Regional Development Fund (ERDF) and the Brussels-Capital Region-Innoviris within the framework of the Operational Programme 2014-2020 through the ERDF-2020 project ICITY-RDI.BRU [27.002.53.01.4524], an F.N.R.S-F.R.S CDR [35276964], an Innoviris Joint R\&D project Genome4Brussels [2020 RDIR 55b], a Research Foundation-Flanders (F.W.O.) Infrastructure project associated with ELIXIR Belgium [I002819N], the Imagica2 IRP project by the Vrije Universiteit Brussel [IRP8b] and by TAILOR, a project funded by the EU Horizon 2020 research and innovation program [952215]. The authors also thank Foundation 101 Genomes (f101g.org) for fruitful collaboration, creative exchange and scientific support. This work was supported by the Fonds de la Recherche Scientifique (F.R.S-F.N.R.S) with the Fund for Research Training in Industry and Agriculture [40021542].