Cornelia M Hooper1, Sandra K Tanz2, Ian R Castleden1, Michael A Vacher2, Ian D Small2, A Harvey Millar1. 1. Centre of Excellence in Computational Systems Biology, The University of Western Australia, Perth, WA 6009, Australia and ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, WA 6009, Australia. 2. Centre of Excellence in Computational Systems Biology, The University of Western Australia, Perth, WA 6009, Australia and ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, WA 6009, Australia Centre of Excellence in Computational Systems Biology, The University of Western Australia, Perth, WA 6009, Australia and ARC Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, WA 6009, Australia.
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. AVAILABILITY: 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).
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. AVAILABILITY: 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).
Authors: Thomas Nietzel; Jörg Mostertz; Cristina Ruberti; Guillaume Née; Philippe Fuchs; Stephan Wagner; Anna Moseler; Stefanie J Müller-Schüssele; Abdelilah Benamar; Gernot Poschet; Michael Büttner; Ian Max Møller; Christopher H Lillig; David Macherel; Markus Wirtz; Rüdiger Hell; Iris Finkemeier; Andreas J Meyer; Falko Hochgräfe; Markus Schwarzländer Journal: Proc Natl Acad Sci U S A Date: 2019-12-23 Impact factor: 11.205
Authors: Nuria Andrés-Colás; Qiang Zhu; Mizuki Takenaka; Bert De Rybel; Dolf Weijers; Dominique Van Der Straeten Journal: Proc Natl Acad Sci U S A Date: 2017-07-31 Impact factor: 11.205