Bioinformatics Study of Functional Associations Observed in Multiple Sources of Human Genome Data

High-throughput genome analysis techniques produce the ever increasing number of heterogeneous large-scale datasets. Studies of these mutually complementary sources of data promise insights into a global picture of the living cell. Here, we present a simple bioinformatics methodology for the analysis of multiple heterogeneous sources of 'omic' (genomic, proteomic, etc) data. We apply this methodology to study associations among four types of human 'omic' data: protein-protein interactions, gene expression, transcription factor binding sites, and functional pathways. The results of our study indicate that the proposed approach can be used to identify and rank statistically significant functional associations among genes. We show that combinations of multiple data types provide additional insights into the properties of functional pathways. The proposed methodology can also be used as a quantitative procedure for evaluating the quality of 'omic' datasets.