Wisdom of crowds for robust gene network inference

Daniel Marbach, James C. Costello, Robert Küffner, Nicole M. Vega, Robert J. Prill, Diogo M. Camacho, Kyle R. Allison, Manolis Kellis, James J. Collins, Andrej Aderhold, Gustavo Stolovitzky, Richard Bonneau, Yukun Chen, Francesca Cordero, Martin Crane, Frank Dondelinger, Mathias Drton, Roberto Esposito, Rina Foygel, Alberto De La FuenteJan Gertheiss, Pierre Geurts, Alex Greenfield, Marco Grzegorczyk, Anne Claire Haury, Benjamin Holmes, Torsten Hothorn, Dirk Husmeier, Vân Anh Huynh-Thu, Alexandre Irrthum, Guy Karlebach, Sophie Lèbre, Vincenzo De Leo, Aviv Madar, Subramani Mani, Fantine Mordelet, Harry Ostrer, Zhengyu Ouyang, Ravi Pandya, Tobias Petri, Andrea Pinna, Christopher S. Poultney, Serena Rezny, Heather J. Ruskin, Yvan Saeys, Ron Shamir, Alina Sîrbu, Mingzhou Song, Nicola Soranzo, Alexander Statnikov, Nicci Vega, Paola Vera-Licona, Jean Philippe Vert, Alessia Visconti, Haizhou Wang, Louis Wehenkel, Lukas Windhager, Yang Zhang, Ralf Zimmer

Research output: Contribution to journalArticlepeer-review

1256 Scopus citations


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.

Original languageEnglish
Pages (from-to)796-804
Number of pages9
JournalNature Methods
Issue number8
StatePublished - Aug 2012
Externally publishedYes


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