Introduction And Brief Bibliographic Review

A third of a century ago, a novel method capable of separating 1100 proteins on one single PAG was introduced [1]. Although this powerful separation method could detect proteins with abundances as low as 1/10th to 1/100th percent of the total protein content in a cell, difficulties associated with visually estimating protein abundances limited its use to primarily qualitative applications. Since 1975, a steady stream of technical improvements has eliminated many of the original limitations [2] of 2-DGE to the point where semiquantitative studies have become possible. One of the major breakthroughs in this aspect was the development of powerful bioinformatics tools for image analysis and quantification. However, despite the fact that the resulting 2-DE analysis software were designed to reduce the experimental variance in order to enhance the biological variance of interest, the post-experimental software-assisted image analysis has been shown to introduce additional variance into the analysis [3]. In this chapter, we attempt to provide an overview of the main sources of post-electrophoretic variance in current 2-DGE analysis, as well as tools with which this type of variance can be quantified.

Plant Proteomics: Technologies, Strategies, and Applications. Edited by G. K. Agrawal and R. Rakwal Copyright © 2008 John Wiley & Sons, Inc.

The first generation of computational approaches for analysis of 2D gels started to appear toward the end of the 1970s (Gellab, LIPS, Elsie, TYCHO) [4]. These early softwares were characterized by heavy user interaction and low automation, often requiring a great deal of programming and technical expertise by the user. A decade later, the introduction of graphical interfaces such as windows prompted a second generation of 2-DE analysis software (Elsie-4, Melanie, QUEST, Gellab-II). However, the original issues of non-user-friendly interfaces persisted, rendering this generation of 2-DE software inaccessible to non-computer scientists [4]. It was not until low-cost personal computers equipped with more user-friendly graphical interfaces and powerful processors became widely available in the late 1990s that the third, modern generation of 2-DE software was produced [4]. Several of these pioneers (Melanie II, CAROL, Z3, and MIR) [4] were subsequently developed into the 2-DE analysis software commercially available today, such as ImageMaster 2D Platinum™/Melanie™, PDQuest™, DeCyder™, Proteomweaver™, and Progenesis™. A major obstacle with this trend away from academic development toward heavy commercialization is that algorithms used in the products become proprietary information, resulting in a black box research approach. However, information regarding the workings of these softwares can often be inferred from the details of the algorithms in the original academic versions [5]. Efforts are still being made to produce custom systems [6], but since their use in the existing literature has been very limited, this line of products is beyond the scope of this chapter. Although the order of events may differ, the automated analyses of 2D gel images utilized in various commercial software programs generally include the following steps: (i) segmentation (spot detection), (ii) quantification, (iii) background adjustment, (iv) image warping, (v) registration (spot matching), and (vi) normalization. The challenges in computational 2-DGE analysis imparted by technical problems in the experimental technique such as artifacts or irregularly shaped or overlapping spots, as well as the purpose, strengths, and weaknesses of the algorithms used today, are highlighted below.

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