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Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques
BACKGROUND: 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: dif...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457503/ https://www.ncbi.nlm.nih.gov/pubmed/32867673 http://dx.doi.org/10.1186/s12859-020-03713-0 |
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author | Goez, Manuel Mauricio Torres-Madronero, Maria C. Rothlisberger, Sarah Delgado-Trejos, Edilson |
author_facet | Goez, Manuel Mauricio Torres-Madronero, Maria C. Rothlisberger, Sarah Delgado-Trejos, Edilson |
author_sort | Goez, Manuel Mauricio |
collection | PubMed |
description | BACKGROUND: 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. 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. RESULTS: An 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%. CONCLUSIONS: The 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. |
format | Online Article Text |
id | pubmed-7457503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74575032020-08-31 Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques Goez, Manuel Mauricio Torres-Madronero, Maria C. Rothlisberger, Sarah Delgado-Trejos, Edilson BMC Bioinformatics Methodology Article BACKGROUND: 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. 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. RESULTS: An 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%. CONCLUSIONS: The 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. BioMed Central 2020-08-31 /pmc/articles/PMC7457503/ /pubmed/32867673 http://dx.doi.org/10.1186/s12859-020-03713-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Article Goez, Manuel Mauricio Torres-Madronero, Maria C. Rothlisberger, Sarah Delgado-Trejos, Edilson Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques |
title | Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques |
title_full | Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques |
title_fullStr | Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques |
title_full_unstemmed | Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques |
title_short | Joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques |
title_sort | joint pre-processing framework for two-dimensional gel electrophoresis images based on nonlinear filtering, background correction and normalization techniques |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457503/ https://www.ncbi.nlm.nih.gov/pubmed/32867673 http://dx.doi.org/10.1186/s12859-020-03713-0 |
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