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Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana
Author(s) -
Hansen Bjoern Oest,
Meyer Etienne H.,
Ferrari Camilla,
Vaid Neha,
Movahedi Sara,
Vandepoele Klaas,
Nikoloski Zoran,
Mutwil Marek
Publication year - 2018
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/nph.14921
Subject(s) - arabidopsis thaliana , function (biology) , gene , computational biology , inference , biology , gene regulatory network , arabidopsis , gene prediction , computer science , data mining , genetics , artificial intelligence , gene expression , genome , mutant
Summary Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co‐function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user‐friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting‐edge community tool to guide experimentalists.