TY  - JOUR
A1  - Ryngajllo, Malgorzata
A1  - Childs, Liam H.
A1  - Lohse, Marc
A1  - Giorgi, Federico M.
A1  - Lude, Anja
A1  - Selbig, Joachim
A1  - Usadel, Björn
T1  - SLocX  predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
JF  - Frontiers in plant science
N2  - Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.
KW  - subcellular localization
KW  - support vector machine
KW  - prediction
KW  - gene expression
Y1  - 2011
U6  - https://doi.org/10.3389/fpls.2011.00043
SN  - 1664-462X
VL  - 2
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -