TY - GEN
T1 - Bayesian inference for 2D gel electrophoresis image analysis
AU - Yoon, Ji Won
AU - Godsill, Simon J.
AU - Kang, Chul Hun
AU - Kim, Tae Seong
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=34548087085&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548087085&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71233-6_27
DO - 10.1007/978-3-540-71233-6_27
M3 - Conference contribution
AN - SCOPUS:34548087085
SN - 3540712321
SN - 9783540712329
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 343
EP - 356
BT - Bioinformatics Research and Development - First International Conference, BIRD 2007 Proceedings
PB - Springer Verlag
T2 - 1st International Conference on Bioinformatics Research and Development, BIRD 2007
Y2 - 12 March 2007 through 14 March 2007
ER -