Decoding two-dimensional polyacrylamide gel ectrophoresis complex maps by autocovariance function: A simplified approach useful for proteomics

This paper describes a mathematical approach applied for decoding the signal of two-dimensional polyacrylamide gel electrophoresis maps of protein tures. The method is helpful in extracting analytical information since separation of the proteins present in the sample is still far from being achieved and proteins are generally present in the same spot. The simplified method described based on the study of the 2-D autocovariance function (2D-ACVF) computed on experimental digitized map. The first part of the 2D-ACVF allows for the estimation the number of proteins present in the sample (2D-ACVF computed at the origin) and the separation performance (mean spot size). Moreover, the 2D-ACVF plot is a erful tool in identifying order in the spot position, and singling it out from the separation pattern. This method was validated on synthetic maps obtained by puter simulation to describe 2-D PAGE real maps and reference maps retrieved the SWISS-2DPAGE database. The results obtained are discussed by focusing specific information relevant in proteomics: sample complexity, separation mance, and identification of spot trains related to post-translational modifications.