Finding gene regulatory network candidates using the gene expression knowledge base

BMC Bioinformatics

Aravind Venkatesan¹  Sushil Tripathi²  Alejandro Sanz de Galdeano³  Ward Blondé¹  Astrid Lægreid²  Vladimir Mironov¹  Martin Kuiper¹ 

[1] Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway

[2] Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

[3] Escuela Nacional de Sanidad, Instituto de Salud Carlos III, Madrid, 28029, Spain

关键词: Gastrin biology;    Hypothesis assessment;    Target gene interaction;    Transcription factor;    Protein-protein interaction;    Transcription regulation;    Gene expression;    Network extension;    SPARQL;    RDF;    Semantic Web;    Semantic Systems Biology;    Knowledge representation;    Knowledge management;   

Others  :  1084513
DOI  :  10.1186/s12859-014-0386-y

 received in 2014-04-01, accepted in 2014-11-14,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Venkatesan et al.; licensee BioMed Central.

【 图 表 】

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