Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques

Manuel Mauricio Goez,Sarah Rothlisberger,Edilson Delgado-Trejos,Maria C Torres-Madronero

doi:10.1186/s12859-020-03713-0

Manuel Mauricio Goez, Sarah Rothlisberger + Show 2 more

Open Access

https://doi.org/10.1186/s12859-020-03713-0

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Journal: BMC bioinformatics	Publication Date: Aug 31, 2020
Citations: 4	License type: open-access

Affiliation: Instituto Tecnológico Metropolitano

Abstract

BackgroundTwo-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images.ResultsAn improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%.ConclusionsThe proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.

Highlights

A commonly used gel-based approach for proteomic analysis is two-dimensional gel electrophoresis (2-DGE), a technique that separates proteins in a sample based on both their isoelectric point and molecular weight [1]
In a previous work [4], we presented a comparison between Wavelet, Contourlet, total variation (TV), and Wavelet and TV (WTTV) filters using synthetic and real 2-DGE images, showing that with synthetic data, Wavelet and WTTV had the lowest sensitivity to noise levels, while wavelet presented the best detection rate for known proteins on real 2-DGE images
We used four metrics to evaluate the performance of the techniques applied to synthetic data, and we evaluated their capabilities in reducing anomalies in real 2-DGE images

Summary

Introduction

A commonly used gel-based approach for proteomic analysis is two-dimensional gel electrophoresis (2-DGE), a technique that separates proteins in a sample based on both their isoelectric point and molecular weight [1]. Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. Joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images. Comparison of noise reduction techniques Wavelet transform (WT) is one of the nonlinear filters that presents the best performance for noise reduction in 2-DGE images [4].

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Conclusion