Comprehensive post-genomic data analysis approaches integrating biochemical pathway maps.
Post-genomic era research is focusing on studies to attribute functions to genes and their encoded proteins, and to describe the regulatory networks controlling metabolic, protein synthesis and signal transduction pathways. To facilitate the analysis of experiments using post-genomic technologies, new concepts for linking the vast amount of raw data to a biological context have to be developed. Visual representations of pathways help biologists to understand the complex relationships between components of metabolic networks, and provide an invaluable resource for the integration of transcriptomics, proteomics and metabolomics data sets. Besides providing an overview of currently available bioinformatic tools for plant scientists, we introduce BioPathAt, a newly developed visual interface that allows the knowledge-based analysis of genome-scale data by integrating biochemical pathway maps (BioPathAtMAPS module) with a manually scrutinized gene-function database (BioPathAtDB) for the model plant Arabidopsis thaliana. In addition, we discuss approaches for generating a biochemical pathway knowledge database for A. thaliana that includes, in addition to accurate annotation, condensed experimental information regarding in vitro and in vivo gene/protein function.
Lange
B Markus
BM
Institute of Biological Chemistry and Center for Integrated Biotechnology, Washington State University, PO Box 646340, Pullman, WA 99164-6340, USA. lange-m@wsu.edu
Ghassemian
Majid
M
D019295:Computational Biology / Q000295:instrumentation* / Q000379:methods*; D030541:Databases, Genetic; D018745:Genome, Plant; D023281:Genomics; D010944:Plants / Q000235:genetics* / Q000378:metabolism*