Data merging for integrated microarray and proteomic analysis.
The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.
Waters
Katrina M
KM
Computational Sciences & Mathematics Division, Pacific Northwest National Laboratory, Mail Stop P7-56 Box 999, Richland WA 99352, USA.
Pounds
Joel G
JG
Thrall
Brian D
BD
D000818:Animals; D019295:Computational Biology; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D040081:Protein Array Analysis / Q000379:methods*; D011506:Proteins / Q000378:metabolism; D040901:Proteomics; D012333:RNA, Messenger / Q000378:metabolism; D049490:Systems Biology
D011506:Proteins; D012333:RNA, Messenger