navigate diverse sets of omics data in a manner useful for systems biology. Proteins are often the biologically functional elements in cellular networks; thus, many types of data can be mapped to and through proteins as a common biological object. The lightweight data warehouse approach used for the MPD proved useful in practice, especially with large datasets as its simple design and schema allows greater flexibility to add new data types and to modify search and analysis capabilities. Similar lightweight approaches and schemas designed to optimize queries have been shown useful in integration of genomic data [63,64]. The main drawback of this approach is that the warehouse does not contain all the data. However, this is rarely a problem if the data are available in some other data resource optimized for that particular data type and if some upfront analysis of the user’s needs for query and analysis options is performed. For example, our use case analysis suggested that for microarray and mass spectrometry data, individual raw intensities, machine-specific parameters and most calculated numerical values were not required for general queries and analysis across the combined data as these values were only comparable between the particular analysis performed in one lab. As a result, most numerical values were not included in the MPD for the default search but are accessible for display via hyperlinks to our Protein Data Center or FTP site. However, if a new attribute appear or users request searches on a particular value omitted from the warehouse, adding it is a relatively simple matter of adding new data columns. For instance, in example II our combination and analysis of mass spectrometry and protein interaction data, we could include peptide counts directly in the MPD for immediate download instead of retrieving them from the ftp files. Of course, no one approach can be perfect, as in biology and research there always seems to be exceptions and new data and multiple approaches need to be accommodated. Efforts to standardize reporting requirements, vocabularies and develop common XML data formats for sharing data are welcome and can greatly ease the transfer and automated processing of a particular data type. However the current standards do not necessarily guarantee integration as the problems of reconciling gene and protein identifiers as well as differences in experimental methodology remain. We investigated and employed a few common data standards and ontologies in developing the References 1. Sheeley DM, Breen JJ, Old SE (2005) Building integrated approaches for the proteomics of complex, dynamic systems: NIH programs in technology and infrastructure development. J Proteome Res 4: 1114–1122. 2. 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Most useful was the Gene Ontology [67] which has been widely adopted to annotate and classify large scale results and can be used for searching and classification in the MPD. Here we have presented some unique examples to illustrate benefits, as well as the difficulties, associated with integration of a very diverse set of omics research data across different data types, laboratories and organisms. We illustrated with three examples how potential therapeutic and diagnostic targets can be identified from integrated data applying relatively simple and established tools and techniques. We continue to focus on data integration to allow biologists to find relevant data sets for further detailed analysis using the approaches and tools of their choice. In general the analysis of diverse omics data is an area of active research and a number of useful tools are under active development including cytoscape [28], bioconductor [68] and galaxy [69]. 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