The "omics" haystack: defining sources of sample bias in expression profiling.

Great medical benefit may result from biomarker discovery, but the scarcity of useful biomarkers among myriad genes and proteins makes this task every bit as daunting as finding the needle in a haystack. At the moment we are not even certain what the needle or the haystack looks like (although many of us are certain we know one when we see one) or, more precisely, how to separate the signal from the noise. The need for better disease management tools has placed considerable demand on the scientific community to find appropriate clinical biomarkers, ushering in the “omics” era—the application of specific technologies such as proteomics, genomics, and metabolomics along with the mainstreaming of high-throughput, high-volume analytical approaches. Clearly the development and implementation of novel technologies as well as innovative new application of “old” technologies is justified. However, this pushing of the technical envelope must be balanced with careful scientific evaluation of the performance characteristics of each new paradigm. The collision of these two imperatives has never been more apparent than in the current debate over protein expression profiling and pattern recognition–based diagnostics. The opposing forces of excitement associated with innovation (1)(2) and caution regarding …

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