An integrated strategy for the optimization of microarray data interpretation.
The completion of a microarray experiment represents just a starting point toward understanding the biology of interest. A follow-up strategy is needed to fully elucidate the functional significance of microarray-derived measurements of differential expression. Given the fact that no single approach can fully unravel the fundamental biology that is typically quite complex, the follow-up strategy must be integrated at multiple levels encompassing bioinformatics, genomics, and proteomics. In this review, we discuss an integrative approach, which can be used to prioritize microarray-derived candidate genes, define their functions, and place them in the context of the biological system being studied.
Li
Xinmin
X
Functional Genomics Facility, Division of Biological Sciences, The University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, USA. xli@medicine.bsd.uchicago.edu
Quigg
Richard J
RJ
D000818:Animals; D002874:Chromosome Mapping; D019295:Computational Biology; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D040901:Proteomics; D040641:Quantitative Trait Loci; D051381:Rats