<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The aim of the investigation presented in this paper was to develop a software-based assistant for the protein analysis workflow. The prior characterization of the unknown protein in two-dimensional electrophoresis gel images is performed according to the molecular weight and isoelectric point of each protein spot estimated from the gel image before further sequence analysis by mass spectrometry. The paper presents a method for automatic and robust identification of the protein standard band in a two-dimensional gel image. In addition, the method introduces the identification of the positions of the markers, prepared by using pre-selected proteins with known molecular mass. The robustness of the method was achieved by using special validation rules in the proposed original algorithms. In addition, a self-organizing map-based decision support algorithm is proposed, which takes Gabor coefficients as image features and searches for the differences in preselected vertical image bars. The experimental investigation proved the good performance of the new algorithms included into the proposed method. The detection of the protein standard markers works without modification of algorithm parameters on two-dimensional gel images obtained by using different staining and destaining procedures, which results in different average levels of intensity in the images.</p></abstract>

The aim of the investigation presented in this paper was to develop a software-based assistant for the protein analysis workflow. The prior characterization of the unknown protein in two-dimensional electrophoresis gel images is performed according to the molecular weight and isoelectric point of each protein spot estimated from the gel image before further sequence analysis by mass spectrometry. The paper presents a method for automatic and robust identification of the protein standard band in a two-dimensional gel image. In addition, the method introduces the identification of the positions of the markers, prepared by using pre-selected proteins with known molecular mass. The robustness of the method was achieved by using special validation rules in the proposed original algorithms. In addition, a self-organizing map-based decision support algorithm is proposed, which takes Gabor coefficients as image features and searches for the differences in preselected vertical image bars. The experimental investigation proved the good performance of the new algorithms included into the proposed method. The detection of the protein standard markers works without modification of algorithm parameters on two-dimensional gel images obtained by using different staining and destaining procedures, which results in different average levels of intensity in the images.

A Robust Identification of the Protein Standard Bands in Two-Dimensional Electrophoresis Gel Images

Electrical, Control and Communication Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The aim of the investigation presented in this paper was to develop a software-based assistant for the protein analysis workflow. The prior characterization of...

A Robust Identification of the Protein Standard Bands in Two-Dimensional Electrophoresis Gel Images

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 63 - 68

DOI: https://doi.org/10.1515/ecce-2017-0009

Schlüsselwörter
Biomedical imaging, Image analysis, Molecular biomarkers, proteins, quantization

© 2017 Artūras Serackis et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Robust Identification of the Protein Standard Bands in Two-Dimensional Electrophoresis Gel Images

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 63 - 68

DOI: https://doi.org/10.1515/ecce-2017-0009

SchlüsselwörterBiomedical imaging, Image analysis, Molecular biomarkers, proteins, quantization

© 2017 Artūras Serackis et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Biomedical imaging, Image analysis, Molecular biomarkers, proteins, quantization