31 agosto 2012
Wisdom of crowds to infer gene networks, published on Nature Methods, 15 July 2012.
The work resulted in an improved gene network from the model organism Escherichia coli and a completely novel gene network from the pathogenic bacterium Staphylococcus aureus.
I asked Alberto de la Fuente (CRS4, Dream5 consortium) to explain objectives and methods of this research.
What is the main idea of this research?
"The idea is that we addressed network inference as a community, as a 'crowd'. Many groups from all over the world inferred networks from the same data. It is shown in this paper that creating a 'community consensus' network from all the different inferred networks is more reliable than any of the individual networks. Hence, the crowd is 'wiser' than any of the individuals in it".
What is the role played by you and your collegues of CRS4 into the Dream5 consortium (Andrea Pinna, Vincenzo De Leo and Nicola Soranzo)?
"We were one of the groups that contributed a network for the community predictions. I would like to emphasize that our method was evaluated as one of the best performing, so I like to think we made an important contribution beyond being just a member of the community".
Andrea Mameli www.linguaggiomacchina.it 31 August 2012
Wisdom of crowds for robust gene network inference
Nature Methods, 9, 796–804 (2012) doi:10.1038/nmeth.2016
Published online 15 July 2012
Daniel Marbach, James C Costello, Robert Küffner, Nicole M Vega, Robert J Prill, Diogo M Camacho, Kyle R Allison, The DREAM5 Consortium, Manolis Kellis, James J Collins, Gustavo Stolovitzky.
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.