Journal
BIOINFORMATICS
Volume 30, Issue 23, Pages 3356-3364Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu550
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Funding
- Australian Research Council [CE0561495, CE140100008, FT110100242, DE120100307]
- Government of Western Australia
- Australian Research Council [FT110100242, DE120100307] Funding Source: Australian Research Council
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Motivation: Knowing the subcellular location of proteins is critical for understanding their function and developing accurate networks representing eukaryotic biological processes. Many computational tools have been developed to predict proteome-wide subcellular location, and abundant experimental data from green fluorescent protein (GFP) tagging or mass spectrometry (MS) are available in the model plant, Arabidopsis. None of these approaches is error-free, and thus, results are often contradictory. Results: To help unify these multiple data sources, we have developed the SUBcellular Arabidopsis consensus (SUBAcon) algorithm, a naive Bayes classifier that integrates 22 computational prediction algorithms, experimental GFP and MS localizations, protein-protein interaction and co-expression data to derive a consensus call and probability. SUBAcon classifies protein location in Arabidopsis more accurately than single predictors.
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