Reference : Wisdom of crowds for robust gene network inference
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
Engineering, computing & technology : Computer science
Wisdom of crowds for robust gene network inference
Marbach, Daniel []
Costello, James C. []
Küffner, Robert []
Vega, Nicole M. []
Prill, Robert J. []
Camacho, Diogo M. []
Allison, Kyle R. []
Aderhold, Andrej []
Bonneau, Richard []
Chen, Yukun []
Cordero, Francesca []
Crane, Martin []
Dondelinger, Frank []
Drton, Mathias []
Esposito, Roberto []
Foygel, Rina []
de la Fuente, Alberto []
Gertheiss, Jan []
Geurts, Pierre mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Greenfield, Alex []
Grzegorczyk, Marco []
Haury, Anne-Claire []
Holmes, Benjamin []
Hothorn, Torsten []
Husmeier, Dirk []
Huynh-Thu, Vân Anh mailto [Université de Liège - ULiège > > GIGA-Management : Coordination ALMA-GRID >]
Irrthum, Alexandre mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Karlebach, Guy []
Lèbre, Sophie []
De Leo, Vincenzo []
Madar, Aviv []
Mani, Subramani []
Mordelet, Fantine []
Ostrer, Harry []
Ouyang, Zhengyu []
Pandya, Ravi []
Petri, Tobias []
Pinna, Andrea []
Poultney, Christopher S. []
Rezny, Serena []
Ruskin, Heather J. []
Saeys, Yvan []
Shamir, Ron []
Sîrbu, Alina []
Song, Mingzhou []
Soranzo, Nicola []
Statnikov, Alexander []
Vega, Nicci []
Vera-Licona, Paola []
Vert, Jean-Philippe []
Visconti, Alessia []
Wang, Haizhou []
Wehenkel, Louis mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Windhager, Lukas []
Zhang, Yang []
Zimmer, Ralf []
Kellis, Manolis []
Collins, James J. []
Stolovitzky, Gustavo []
Nature Methods
Nature Publishing Group
Yes (verified by ORBi)
[en] Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~ 1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

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