Bayesian inference for 2D gel electrophoresis image analysis

Ji Won Yoon, Simon J. Godsill, ChulHun Kang, Tae Seong Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Two-dimensional gel electrophoresis (2DGE) is a technique to separate individual proteins in biological samples. The 2DGE technique results in gel images where proteins appear as dark spots on a white background. However, the analysis and inference of these images get complicated due to 1) contamination of gels, 2) superposition of proteins, 3) noisy background, and 4) weak protein spots. Therefore there is a strong need for an automatic analysis technique that is fast, robust, objective, and automatic to find protein spots. In this paper, to find protein spots more accurately and reliably from gel images, we propose Reversible Jump Markov Chain Monte Carlo method (RJMCMC) to search for underlying spots which are assume to have Gaussian-distribution shape. Our statistical method identifies very weak spots, restores noisy spots, and separates mixed spots into several meaningful spots which are likely to be ignored and missed. Our proposed approach estimates the proper number, centreposition, width, and amplitude of the spots and has been successfully applied to the field of projection reconstruction NMR (PR-NMR) processing [15,16]. To obtain a 2DGE image, we peformed 2DGE on the purified mitochondiral protein of liver from an adult Sprague-Dawley rat.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages343-356
Number of pages14
Volume4414 LNBI
Publication statusPublished - 2007 Aug 27
Externally publishedYes
Event1st International Conference on Bioinformatics Research and Development, BIRD 2007 - Berlin, Germany
Duration: 2007 Mar 122007 Mar 14

Other

Other1st International Conference on Bioinformatics Research and Development, BIRD 2007
CountryGermany
CityBerlin
Period07/3/1207/3/14

Fingerprint

Electrophoresis, Gel, Two-Dimensional
Bayesian inference
Electrophoresis
Image Analysis
Image analysis
Gels
Proteins
Protein
Reversible Jump Markov Chain Monte Carlo
Monte Carlo Method
Markov Chains
Normal Distribution
Markov Chain Monte Carlo Methods
Gaussian distribution
Contamination
Liver
Statistical method
Markov processes
Superposition
Sprague Dawley Rats

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Yoon, J. W., Godsill, S. J., Kang, C., & Kim, T. S. (2007). Bayesian inference for 2D gel electrophoresis image analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4414 LNBI, pp. 343-356)

Bayesian inference for 2D gel electrophoresis image analysis. / Yoon, Ji Won; Godsill, Simon J.; Kang, ChulHun; Kim, Tae Seong.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4414 LNBI 2007. p. 343-356.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yoon, JW, Godsill, SJ, Kang, C & Kim, TS 2007, Bayesian inference for 2D gel electrophoresis image analysis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4414 LNBI, pp. 343-356, 1st International Conference on Bioinformatics Research and Development, BIRD 2007, Berlin, Germany, 07/3/12.
Yoon JW, Godsill SJ, Kang C, Kim TS. Bayesian inference for 2D gel electrophoresis image analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4414 LNBI. 2007. p. 343-356
Yoon, Ji Won ; Godsill, Simon J. ; Kang, ChulHun ; Kim, Tae Seong. / Bayesian inference for 2D gel electrophoresis image analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4414 LNBI 2007. pp. 343-356
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