Bioinformatics for Microarrays

Microarrays (or biochips) is perhaps one of the most exciting developments in bioinformatics research. The emerging biochip technology has made it possible to simultaneously study expression (activity level) of thousands of genes or proteins in a single experiment in the laboratory. However, in order to extract relevant biological knowledge from the biochip experimental data, it is critical not only to analyze the experimental data, but also to cross-reference and correlate these large volumes of data with information available in external biological databases accessible online.

We describe a comprehensive system for knowledge management in bioinformatics called e2e in which data generated by the biochip experiments can be analyzed for emerging patterns among groups of genes with additional insights from related analyses like pathway scores, sequence similarity, literature text summarization, etc. To the biologist or biological applications, e2e exposes a common semantic view of inter-relationship among biological concepts in the form of an XML representation called eXpressML. Internally, e2e can use any data integration solution (like DiscoveryLink, Kleisli or natively XML-based) to retrieve data and return results corresponding to the semantic view. We have implemented an e2e prototype that demonstrates our framework by allowing a biologist to analyze her gene expression data in GEML or from a public site like Stanford, and discover knowledge through operations like querying on relevant annotated data represented in eXpressML using pathways data from KEGG, publication data from Medline and protein data from SWISS-PROT.