Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. The Reverse Engineering Assessment and Methods (DREAM) project, performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. They characterized the performance, data requirements and inherent biases of different inference approaches, observing that no single inference method performs optimally across all data sets.
Their research unveiled that integration of predictions from multiple inference methods shows robust and high performance across diverse data sets, which led them to construct high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. Their results, published in a paper entitled Wisdom of Crowds for Robust Gene Network Inference (2012), established community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.
Figure above (4b in the article): network modules were identified and tested for Gene Ontology-term enrichment (gray genes do not show enrichment). A network module enriched for Gene ontology terms related to Pathogenesis is highlighted in the S. aureus network. (Image reproduced with permission. NPG Lic. No. 3346711104169)