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KMID : 0380020050200020071
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Korean Journal of Biotechnology and Bioengineering
2005 Volume.20 No. 2 p.71 ~ p.75
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Clustering of 2D-Gel Images
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Hur Won
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Abstract
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Alignment of 2D-gel images of biological samples can visualize the difference of expression profiles and also inform us candidates of protein spots to be further analyzed. However, comparison of two proteome images between the case and control does not always successfully identify differentially expressed proteins because of sample-to-sample variation, poor reproducibility of 2D-gel electrophoresis and inconsistent electrophoresis conditions. Multiple alignment of 2D-gel image must be preceded before visualizing the difference of expression profiles or clustering proteome images. Thus, a software for the alignment of multiple 2D-Gel images and their clustering was developed by applying various algorithms and statistical methods. Microsoft Visual C++ was used to implement the algorithms in this work. Multiresoultion-multilevel algorithm was found out to be suitable for fast alignment and for largely distorted images. Clustering of 10 different proteome images of Fetal Alcohol Syndrome, was carried out by implementing a k-means algorithm and it gave a phylogenetic tree of proteomic distance map of the samples. However, the phylogenetic tree does not discriminate the case and control. The whole image clustering shows that the proteomic distance is more dependent to age and sex.
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KEYWORD
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Proteomics, 2D-Gel image, Clustering, Image registration
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