Data integration for plant genomics-exemplars from the integration of Arabidopsis thaliana databases

• Published in: Briefings in bioinformatics Vol. 10; no. 6; pp. 676 - 693
• Main Authors: Lysenko, Atem, Hindle, Matthew Morritt, Taubert, Jan, Saqi, Mansoor, Rawlings, Christopher John
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-11-2009; Oxford Publishing Limited (England)
• Subjects: Arabidopsis - genetics; Arabidopsis Proteins - genetics; Bioinformatics; Botany; Chromosome Mapping - methods; Database Management Systems; Databases, Genetic; Flowers & plants; Genome, Plant - genetics; Information Storage and Retrieval - methods; Protein Interaction Mapping - methods; Proteins; Systems Integration; database comparison; systems biology; metabolic networks; protein interaction networks; Ondex; plant genomics; data integration; graph based analysis
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbp047

The development of a systems based approach to problems in plant sciences requires integration of existing information resources. However, the available information is currently often incomplete and dispersed across many sources and the syntactic and semantic heterogeneity of the data is a challenge for integration. In this article, we discuss strategies for data integration and we use a graph based integration method (Ondex) to illustrate some of these challenges with reference to two example problems concerning integration of (i) metabolic pathway and (ii) protein interaction data for Arabidopsis thaliana. We quantify the degree of overlap for three commonly used pathway and protein interaction information sources. For pathways, we find that the AraCyc database contains the widest coverage of enzyme reactions and for protein interactions we find that the IntAct database provides the largest unique contribution to the integrated dataset. For both examples, however, we observe a relatively small amount of data common to all three sources. Analysis and visual exploration of the integrated networks was used to identify a number of practical issues relating to the interpretation of these datasets. We demonstrate the utility of these approaches to the analysis of groups of coexpressed genes from an individual microarray experiment, in the context of pathway information and for the combination of coexpression data with an integrated protein interaction network.