A New Algorithm for the Simulation of Sodium Dodecil Sulfate Two-Dimensional Polyacrylamide Gel Electrophoresis Data Sets

Preprocessing of 2-Dimensional Gel Electrophoresis Images Applied to Proteomic Analysis: A Review

Various methods and specialized software programs are available for processing two-dimensional gel electrophoresis (2-DGE) images. However, due to the anomalies present in these images, a reliable, automated, and highly reproducible system for 2-DGE image analysis has still not been achieved. The most common anomalies found in 2-DGE images include vertical and horizontal streaking, fuzzy spots, and background noise, which greatly complicate computational analysis. In this paper, we review the preprocessing techniques applied to 2-DGE images for noise reduction, intensity normalization, and background correction. We also present a quantitative comparison of non-linear filtering techniques applied to synthetic gel images, through analyzing the performance of the filters under specific conditions. Synthetic proteins were modeled into a two-dimensional Gaussian distribution with adjustable parameters for changing the size, intensity, and degradation. Three types of noise were added to the images: Gaussian, Rayleigh, and exponential, with signal-to-noise ratios (SNRs) ranging 8-20 decibels (dB). We compared the performance of wavelet, contourlet, total variation (TV), and wavelet-total variation (WTTV) techniques using parameters SNR and spot efficiency. In terms of spot efficiency, contourlet and TV were more sensitive to noise than wavelet and WTTV. Wavelet worked the best for images with SNR ranging 10-20 dB, whereas WTTV performed better with high noise levels. Wavelet also presented the best performance with any level of Gaussian noise and low levels (20-14 dB) of Rayleigh and exponential noise in terms of SNR. Finally, the performance of the non-linear filtering techniques was evaluated using a real 2-DGE image with previously identified proteins marked. Wavelet achieved the best detection rate for the real image.

Spot quantification in two dimensional gel electrophoresis image analysis: comparison of different approaches and presentation of a novel compound fitting algorithm

Background

Various computer-based methods exist for the detection and quantification of protein spots in two dimensional gel electrophoresis images. Area-based methods are commonly used for spot quantification: an area is assigned to each spot and the sum of the pixel intensities in that area, the so-called volume, is used a measure for spot signal. Other methods use the optical density, i.e. the intensity of the most intense pixel of a spot, or calculate the volume from the parameters of a fitted function.

Results

In this study we compare the performance of different spot quantification methods using synthetic and real data. We propose a ready-to-use algorithm for spot detection and quantification that uses fitting of two dimensional Gaussian function curves for the extraction of data from two dimensional gel electrophoresis (2-DE) images. The algorithm implements fitting using logical compounds and is computationally efficient. The applicability of the compound fitting algorithm was evaluated for various simulated data and compared with other quantification approaches. We provide evidence that even if an incorrect bell-shaped function is used, the fitting method is superior to other approaches, especially when spots overlap. Finally, we validated the method with experimental data of urea-based 2-DE of Aβ peptides andre-analyzed published data sets. Our methods showed higher precision and accuracy than other approaches when applied to exposure time series and standard gels.

Conclusion

Compound fitting as a quantification method for 2-DE spots shows several advantages over other approaches and could be combined with various spot detection methods.

The algorithm was scripted in MATLAB (Mathworks) and is available as a supplemental file.

A new algorithm for the simulation of SDS 2D-PAGE datasets

This chapter describes a new software for the generation of simulated realistic sodium dodecyl sulfate two-dimensional polyacrylamide gel electrophoresis (SDS 2D-PAGE) images. In order to choose the simulation strategy to provide realistic 2D-PAGE maps the statistical characteristics of such images were taken into account, such as the distributions of sizes, intensities, and volumes of the spots. Also, the low reproducibility typical of replicated SDS 2D-PAGE maps of the same sample was simulated. This approach can be used to generate simulated datasets useful in the development and performance evaluation of new classification and/or image analysis algorithms applied to two-dimensional electrophoresis datasets, given the usually small number of experimental replications available.