Wisdom of crowds for robust gene network inference.
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.
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Daniel
D
Marbach
James C
JC
Costello
Robert
R
Küffner
Nicole M
NM
Vega
Robert J
RJ
Prill
Diogo M
DM
Camacho
Kyle R
KR
Allison
Manolis
M
Kellis
James J
JJ
Collins
Gustavo
G
Stolovitzky
D000465:Algorithms; D019295:Computational Biology; D004926:Escherichia coli / Q000235:genetics; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics*; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software; D013211:Staphylococcus aureus / Q000235:genetics; D014158:Transcription, Genetic / Q000235:genetics