Preprocessing of two‐dimensional gel electrophoresis images

Abstract

Proteomics produces a huge amount of two-dimensional gel electrophoresis images. Their analysis can yield a lot of information concerning proteins responsible for different diseases or new unidentified proteins. However, an automatic analysis of such images requires an efficient tool for reducing noise in images. This allows proper detection of the spots' borders, which is important in protein quantification (as the spots' areas are used to determine the amounts of protein present in an analyzed mixture). Also in the feature-based matching methods the detected features (spots) can be described by additional attributes, such as area or shape. In our study, a comparison of different methods of noise reduction is performed in order to find out a method best suited for reducing noise in gel images. Among the compared methods there are the classical methods of linear filtering, e.g., the mean and Gaussian filtering, the nonlinear method, i.e., median filtering, and also the methods better suited for processing of nonstationary signals, such as spatially adaptive linear filtering and filtering in the wavelet domain. The best results are obtained by filtering of gel images in the wavelet domain, using the BayesThresh method of threshold value determination.

Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data

“…The identification of the observed peaks in each spectrum is accomplished by identifying local maxima after smoothing via a wavelet transform (Alsberg et al 1997;Cancino-De-Greiff et al 2002;Kaczmarek et al 2004;Perrin et al 2001;Shao et al 2003). After the observed peaks of each spectrum have been determined, the algorithm determines the optimal bin configuration using a dynamic programming strategy to efficiently find the best solution.…”

“…The procedure for determining the location of the observed peaks begins by smoothing each spectrum using a decimated wavelet transformation (Alsberg et al 1997;Cancino-De-Greiff et al 2002;Kaczmarek et al 2004;Perrin et al 2001;Shao et al 2003). A smooth spectrum is created Fig.…”

Application of Chemometrics to the Interpretation of Analytical Separations Data

“…The functions are usually first-order polynomials; however, higher-order polynomials or a series of connected first-order polynomials are also used in some situations. Having determined the equation of the background signal, the fitted line is then subtracted from the signal (Brereton, 2003;Gan et al, 2006;Kaczmarek et al, 2005;Zhang et al, 2010;Persson & Strang, 2003;Eilers, 2003). Correction of the baseline using curve fitting is demonstrated in Figure 3.…”

The start-to-end chemometric image processing of 2D thin-layer videoscans

“…However, the topic of 2D-TLC fingerprinting is not present in the literature. The very well starting point for our study was a series of papers related to preprocessing of two-dimensional gel electropherograms [11][12][13][14][15][16][17]. The studies performed in the field of 2D electrophoresis were a great challenge for the authors, due to the fact, that electropherograms contained a set of small spots and the main problem was to find corresponding features during warping.…”