alexa Prelocabc: A Novel Predictor of Protein Sub-cellular Localization Using a Bayesian Classifier
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Prelocabc: A Novel Predictor of Protein Sub-cellular Localization Using a Bayesian Classifier

Yanqiong Zhang, Tao Li, Chunyuan Yang, Dong Li, Yu Cui, Ying Jiang, Lingqiang Zhang, Yunping Zhu* and Fuchu He*

State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, P.R. China

Corresponding Author:
Dr. Yunping ZHU
State Key Laboratory of Proteomics
Beijing Proteome Research Center
Beijing Institute of Radiation Medicine
Beijing 102206, P.R. China
E-mail: [email protected]

Dr. Fuchu HE
State Key Laboratory of Proteomics
Beijing Proteome Research Center
Beijing Institute of Radiation Medicine
Beijing 102206, P.R. China
Tel: +86-10-80705225
Fax: +86-10-80705155
E-mail: [email protected]

Received Date: December 13, 2010; Accepted Date: January 19, 2011; Published Date: January 28, 2011

Citation: Zhang Y, Li T, Yang C, Li D, Cui Y, et al. (2011) Prelocabc: A Novel Predictor of Protein Sub-cellular Localization Using a Bayesian Classifier. J Proteomics Bioinform 4: 044-052. doi: 10.4172/jpb.1000165

Copyright: © 2011 Zhang Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Sub-cellular localization of proteins is crucial for the dynamic life of cells. Its ascertainment is an important step to elucidate proteins' biological functions. Various experimental and computational methods have been developed for this purpose. Using a Bayesian model, we integrated five sub-modules based on different protein features, such as homology, amino acid composition, sorting signals and functional motifs, to predict sub-cellular localization of non-plant eukaryotic protein. This method has higher accuracy and Matthew's correlation coefficient values than previous algorithms against five independent test datasets, and is able to predict efficiently nine major sub-cellular compartments for both single-localized and multiple-localized proteins. As an application, we also combined this method with the proteome mass-spectrum quantitative information, improving the performance of PreLocABC dramatically. This method has been developed into an online prediction system (PreLocABC). Users may submit their protein sequences online, and the prediction results for protein sub-cellular localization will be returned. The web interface of PreLocABC is available at https://61.50.138.123/PreLocABC.

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