Meanwhile the data of a many fully sequenced genomes are available, including the human genome. Depending on the type of organism, only a part of the sequence can be directly correlated with a biological function. It had been found, that there is very low correlation between mRNA abundance and protein level (Anderson and Seilhamer, 1997;. Furthermore, since it became apparent, that the human genome surprisingly contains less than about 26,000 genes, it became obvious, that biological events cannot be explained from the genomic information directly. It has thus become necessary to analyse the protein complement of the genome, which has been defined as the "Proteome" (Wasinger et al. 1995).
A proteome consists of considerably more proteins than expected by direct transcription and translation due to alternative splicing and post-translational modifications. Not all proteins are expressed at the same time, and in a living cell continuously changes are happening. This makes it impossible to create a proteome map like a static gene map. Therefore, during an experiment, a series of samples is analysed, and the quantitative changes of expression levels are monitored. This approach is called "proteome analysis" or "proteomics". The new concept is mainly applied to drug discovery, diagnostics, therapy, and agricultural research.
In practice cell lysates or tissue extracts are searched for up or down regulated proteins. In an experiment, cells are stimulated by gene deletion or overexpression, pharmaceutical treatment, withdrawal of nutrients, or by physical or chemical stimulation. The great challenge of the Proteomics approach is finding target proteins with high significance. This is not an easy task, because there are a lot of variants in the analysed biological systems, and the separation and detection techniques have limited reproducibility.