ArticleBMC bioinformatics2010
TESTLoc: protein subcellular localization prediction from EST data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Identification and Expression Pattern of EZH2 in Pig Developing Fetuses.BioMed research international · 2020Article
- Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features.Molecules (Basel, Switzerland) · 2019Article
- PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction.PloS one · 2013Article
- Article
- A hybrid distance measure for clustering expressed sequence tags originating from the same gene family.PloS one · 2012Article
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2 authors.
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Abstract
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.
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