Submitted to: Methods in Molecular Biology
Publication Type: Book / chapter
Publication Acceptance Date: 3/6/2006
Publication Date: 12/1/2007
Citation: Wise, R.P., Caldo, R.A., Hong, L., Shen, L., Dickerson, J.A. 2007. BarleyBase/PLEXdb: A Unified Expression Profiling Database for Plants and Plant Pathogens. In: David Edwards editor. Plant Bioinformatics: Methods and Protocols. Secaucus, N.J. Springer. p. 347-363. Interpretive Summary: Microarray analysis commonly consists of a data-driven exploratory approach that relies on searching for differentially expressed or co-regulated genes. Subsequently, the investigator is left with the task of looking for connections between the genes that showed interesting activity, by searching through annotation from BLAST hits, specialized genome databases, protein information, and pathway links. This process is very labor intensive and time consuming as well as risking missing subtle linkages due to the sheer amount of information available. In addition, each database has a different format, making it difficult for the user to perform uniform data retrieval. PLEXdb automates this procedure for the biologist by extracting key information such as physical location, functionality, potential pathways, homologs in related species, and grouping the diverse information in meaningful ways. PLEXdb is an essential public resource that will serve as a bridge from sequence to trait ontology through transcript (and eventually protein and metabolite) profiling. The underlying premise is that these molecular level phenotypes manifest as traits plant breeders select for and will lead to improvements in food and nutrition.
Technical Abstract: BarleyBase (http://barleybase.org/) and its successor, PLEXdb (http://plexdb.org/), are public resources for large-scale gene expression analysis for plants and plant pathogens. BarleyBase/PLEXdb provides a unified web interface to support the functional interpretation of highly parallel microarray experiments integrated with traditional structural genomics and phenotypic data. Users can perform hypothesis-building queries from multiple interlinked resources, e.g., a particular gene, a protein class, EST entries, physical or genetic map position - all coupled to highly parallel gene expression for a variety of crop and model plant species from a large array of experimental or field conditions. Array data are interlinked to analytical and biological functions (e.g., Gene and Plant Ontologies, BLAST, spliced alignment, multiple alignment, regulatory motif identification, and expression analysis), allowing members of the community to access and analyze comparative expression experiments in conjunction with their own data.