SUBAcon: a consensus algorithm for unifying the subcellular localization data of the Arabidopsis proteome.

Abstract

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. 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. SUBAcon is a useful tool for recovering proteome-wide subcellular locations of Arabidopsis proteins and is displayed in the SUBA3 database (http://suba.plantenergy.uwa.edu.au). The source code and input data is available through the SUBA3 server (http://suba.plantenergy.uwa.edu.au//SUBAcon.html) and the Arabidopsis SUbproteome REference (ASURE) training set can be accessed using the ASURE web portal (http://suba.plantenergy.uwa.edu.au/ASURE).