Decoding 2-D Maps by Autocovariance Function

  • Maria Chiara Pietrogrande
  • Nicola Marchetti
  • Francesco Dondi
Part of the Methods in Molecular Biology book series (MIMB, volume 1384)


This chapter describes a mathematical approach based on the study of the 2-D autocovariance function (2-D ACVF) useful for decoding the complex signals resulting from the separation of protein mixtures. The method allows to obtain fundamental analytical information hidden in 2-D PAGE maps by spot overlapping, such as the number of proteins present in the sample and the mean standard deviation of the spots, describing the separation performance. In addition, it is possible to identify ordered patterns potentially present in spot positions, which can be related to the chemical composition of the protein mixture, such as post-translational modifications.

The procedure was validated on computer-simulated maps and successfully applied to reference maps obtained from literature sources.

Key words

2-D PAGE (2-D polyacrylamide gel electrophoresis) maps Chemometric methods Bidimensional autocovariance function Spot overlapping Bioinformatics 


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Maria Chiara Pietrogrande
    • 1
  • Nicola Marchetti
    • 1
  • Francesco Dondi
    • 1
  1. 1.Department of Chemical and Pharmaceutical SciencesUniversity of FerraraFerraraItaly

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