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

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Abstract

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.
Peer-reviewedYes
Original languageEnglish
Pages (from-to)3356-3364
JournalBioinformatics
Volume30
Issue number23
Early online date22 Aug 2014
DOIs
StatePublished - 1 Dec 2014


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