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BMC Bioinformatics. 2014 Dec 10;15:386. doi: 10.1186/s12859-014-0386-y.

Finding gene regulatory network candidates using the gene expression knowledge base.

Author information

1
Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. aravind.venkatesan@ntnu.no.
2
Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. sushil.tripathi@ntnu.no.
3
Escuela Nacional de Sanidad, Instituto de Salud Carlos III, 28029, Madrid, Spain. asdgvarela@gmail.com.
4
Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. w.blonde@vumc.nl.
5
Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. astrid.lagreid@ntnu.no.
6
Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. vladimir.mironov@ntnu.no.
7
Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway. martin.kuiper@ntnu.no.

Abstract

BACKGROUND:

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.

RESULTS:

We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions.

CONCLUSIONS:

Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

PMID:
25490885
PMCID:
PMC4279962
DOI:
10.1186/s12859-014-0386-y
[Indexed for MEDLINE]
Free PMC Article

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