The BioGateway Resource

A Semantic Systems Biology Database

BioGateway consists of a graph-based database built on Semantic Web principles, a SPARQL endpoint allowing users to query it, and a Cytoscape app which integrates the query functionality directly into your network building workflow.

What is BioGateway?

BioGateway is an initiative that enables a Semantic Systems Biology approach. It provides an entry point to access a data warehouse where biological data is gathered in the form of triples (using RDF). The systems can be queried using SPARQL. The BioGateway system can also be explored using the SPARQL browser. With this browser, SPARQL results can be visually seen as a network of resources.


The Cytoscape App

We have developed an app for Cytoscape to allow you to directly integrate the power of our Semantic Knowledge Base into your network building workflow. With the Query Builder tool, you can formulate the topology of what you are looking for, and it will generate the SPARQL query for you.

The query result can then be imported directly into the Cytoscape network you are building – without having to deal with result file formats, incompatible column standards or identifiers.


The BioGateway Database

Unified Identifiers

Every entity in BioGateway has a unique identifier URI across all datasets – allowing queries across data from different sources.

High-confidence Data

The data in BioGateway is a combination of the most trusted datasets from UniProt, IntAct and other curated sources.

Semantic Web Technologies

BioGateway combines Systems Biology and Semantic Web technologies for more effective modelling of biological systems.

Explorative Data

In addition to Transcription factor – Target gene network connections obtained from Curated resources, BioGateway also returns TF-TGs obtained through text mining and allows a validity check in the original abstract.


Data Types

BioGateway contains several datasets, in the form of bridge graphs, from various sources. Each of these bridge graphs has a set of relation types, describing different types of relationships between entities. The different graphs, the most important relation types in them, and their meaning is described below.



Protein → Taxon

Refers to the taxon that the subject protein is part of.


Protein → Disease

Refers to the diseases that the subject protein is involved in.



Protein → Protein

This relation is based on Protein-Protein Interactions (PPIs) from the IntAct dataset, and refers to curated direct relationships between the subject protein and any other protein.



Protein → GO Term

Refers to the GO Terms that the subject protein is annotated with.

This also includes parent GO Terms.



Gene → Taxon

Annotates the taxon that the subject gene is part of.


Gene → Protein

Annotates the proteins that the subject gene is encoding.

Note: some genes encode multiple proteins.

TF2TG (unpublished)

Is related to

Protein → Gene

Refers to the interaction between a Transcription Factor and a Target Gene. Usually this interaction results in a regulation of the target gene. BioGateway contains TF2TG relations both from curated resources (TRRUST, TFactS, HTRIdb, SIGNOR) and from a text mining effort carried out between CNIO/BSC, Spain, and NTNU (to be published soon).

Contact Us

Please let us know if you have any questions, or if something is not working correctly for you.