Proteomics plays a central role in drug discovery, molecular diagnostics, and the practice of medicine in the postgenomic era. Among all the proteomics technologies, 2-D gel electrophoresis is by far the most commonly used technique for protein separation. In most cases where 2-D gel electrophoresis is employed, the purpose is to find statistically significant differences between predefined conditions. These differences should be related to distinct protein spots that have been correctly detected, matched, and quantified.
To achieve these goals, sophisticated image-analysis algorithms are applied to extract relevant data from complex spot patterns. The acknowledged problems of reproducibility and resolution inherent in the technology present a challenge for any software. As a result, in nearly all cases, the extracted data is partly incorrect and incomplete.
As a consequence, researchers have to deal with two serious problems in image analysis—false positives and false negatives. Both are costly, not just in terms of the resources spent on downstream analysis of false hits but perhaps more importantly, by impeding a true understanding of the underlying biological system.
In nearly all 2-D gel-based proteomic studies, the interesting data is from the protein spots whose intensities have been calculated to be significantly different between the predefined conditions. One could say that those are the desired hits. Upon visual inspection, however, some of these hits turn out to be image-analysis errors such as incorrectly detected or mismatched spots.