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[LOGO] Analysis of 2D Electrophoresis Gels

In proteomics, which is the field of protein analysis, protein samples are separated using a technology called two-dimensional gel electrophoresis (2-DE). This produces gel images in which every dark spot represents one protein. Eventually one would like to compare several images (which is basically a point matching problem; it is similar to considering the starry sky and comparing different star constellations), which is called gel matching. But first one needs to segment the image into the spot areas (spot detection). Both tasks are not straight-forward, and still need a lot of human help nowadays, which is slow and costly. Pharmaceutical companies are very much interested in automating these processes as much as possible.

Spot detection and spot matching across multiple sets of gel images are essential first steps for proteomics investigations based on two-dimensional gel electrophoresis. There are a number of software packages on the market that can be used for this purpose, with each offering distinct advantages and disadvantages. A major difference between many of these programs are the algorithms used for gel matching and spot detection. A high-quality spot detection algorithm is fundamental since accurately detected spots are the basis for all further analyses. A high-quality gel matching algorithm is essential in order to minimize manual postprocessing and in order to facilitate fast cross-matching between sets of gels. However at this time, there is no consensus about which program/algorithm provides the most accurate assessment of changes in protein levels among sample groups.

A 3-dimensional Gaussian distribution (see left) is a widely accepted modeling assumption for protein spots, which is used in several spot detection algorithms. It is however clear from the 2-D gels (see right) obtained from Sue Weintraub's laboratory at the UT Health Science Center at San Antonio that many spots exhibit reproducible intensity patterns that do not fit a Gaussian model.

         

This project investigates new spot detection and gel matching algorithms. The CAROL software [Hoffmann et al. 1999, Pleissner et al. 1999, Kriegel et al. 2000] has been integrated into the gel analysis software package PDQuest by Bio-Rad. Our current research focusses on new spot models, new spot detection algorithms, faster gel matching algorithms, and the integration of gel matching data with microarray data.

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