TESTLoc: protein subcellular localization prediction from EST data

BMC Bioinformatics

Research Article

Yao-Qing Shen¹  Gertraud Burger¹ 

[1] Robert-Cedergren Center for Bioinformatics and Genomics; Biochemistry Department, Université de Montréal, 2900 Edouard-Montpetit, H3T 1J4, Montreal, QC, Canada;

关键词: Support Vector Machine;    Amino Acid Composition;    Matthews Correlation Coefficient;    Localization Prediction;    Subcellular Localization Prediction;   

DOI  :  10.1186/1471-2105-11-563

 received in 2010-07-01, accepted in 2010-11-15,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes.ResultsWe developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%).ConclusionsTESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html

【 授权许可】

CC BY   
© Shen and Burger; licensee BioMed Central Ltd. 2010