A probability-based error spot filtering method in protein 2-DE image spot pattern matching analysis

Given a number of 2-DE gel images of the same kind, identifying the spot of each image for the same protein is an important task to monitor the expression level change of the protein. For this purpose, the gel-based two-dimensional electrophoresis method (2-DE) is widely used since it separates thousands of proteins in a sample cost-effectively. However, this approach suffers from inherent noises and irregular geometric distortions of spots observed in a 2-DE gel image. This paper proposes a probability-based error filtering method that can find more reliable spot-matching results, so that the accuracy of protein expression analysis can be improved. The performance of the proposed method is analyzed by various experiments on real 2-DE gel images of human liver tissues.

[1]  M. Dunn,et al.  Proteomics: From Protein Sequence to Function , 2001 .

[2]  Senthilkumar Damodaran,et al.  Minimizing variability in two-dimensional electrophoresis gel image analysis. , 2007, Omics : a journal of integrative biology.

[3]  J. Klose Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues , 1975, Humangenetik.

[4]  A. Görg,et al.  Current two‐dimensional electrophoresis technology for proteomics , 2004, Proteomics.

[5]  Andrew Emili,et al.  De novo peptide sequencing and quantitative profiling of complex protein mixtures using mass-coded abundance tagging , 2002, Nature Biotechnology.

[6]  M J Dunn,et al.  Positional reproducibility of protein spots in two‐dimensional polyacrylamide gel electrophoresis using immobilised pH gradient isoelectric focusing in the first dimension: An interlaboratory comparison , 1994, Electrophoresis.

[7]  A. Link 2-D proteome analysis protocols , 1998 .

[8]  Anders Blomberg,et al.  Warping two‐dimensional electrophoresis gel images to correct for geometric distortions of the spot pattern , 2002, Electrophoresis.

[9]  T. Rabilloud Two‐dimensional gel electrophoresis in proteomics: Old, old fashioned, but it still climbs up the mountains , 2002, Proteomics.

[10]  Tero Aittokallio,et al.  Comparison of PDQuest and Progenesis software packages in the analysis of two‐dimensional electrophoresis gels , 2003, Proteomics.

[11]  T Aittokallio,et al.  Geometrical distortions in two-dimensional gels: applicable correction methods. , 2005, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[12]  Kathryn S Lilley,et al.  Comparison of DIGE and post‐stained gel electrophoresis with both traditional and SameSpots analysis for quantitative proteomics , 2008, Proteomics.

[13]  P. O’Farrell High resolution two-dimensional electrophoresis of proteins. , 1975, The Journal of biological chemistry.

[14]  Cristina-Maria Vâlcu,et al.  Reproducibility of two-dimensional gel electrophoresis at different replication levels. , 2007, Journal of proteome research.

[15]  S. Gygi,et al.  Quantitative analysis of complex protein mixtures using isotope-coded affinity tags , 1999, Nature Biotechnology.

[16]  David O. Nelson,et al.  Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyderTM , 2005, Bioinform..

[17]  Nicola Marchetti,et al.  Spot overlapping in two‐dimensional polyacrylamide gel electrophoresis separations: A statistical study of complex protein maps , 2002, Electrophoresis.

[18]  Gert Lubec,et al.  Limitations of current proteomics technologies. , 2005, Journal of chromatography. A.

[19]  C. G. Edmonds,et al.  Liquid Chromatography/Mass Spectrometry: Techniques and Applications , 1990 .

[20]  Babu Raman,et al.  Quantitative comparison and evaluation of two commercially available, two‐dimensional electrophoresis image analysis software packages, Z3 and Melanie , 2002, Electrophoresis.

[21]  Matthias Berth,et al.  The state of the art in the analysis of two-dimensional gel electrophoresis images , 2007, Applied Microbiology and Biotechnology.