Journal
NATURE METHODS
Volume 9, Issue 8, Pages 796-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.2016
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Funding
- US National Institutes of Health (NIH) National Centers for Biomedical Computing Roadmap Initiative [U54CA121852]
- Howard Hughes Medical Institute
- NIH [DPI OD003644]
- Swiss National Science Foundation
- French National Research Agency [ANR-07-BLAN-0311-03, ANR-09-BLAN-0051-04]
- Interuniversity Attraction Poles Programme [IAP P6/25 BIOMAGNET]
- APO-SYS program [HEALTH-F4-2007-200767]
- Edmond J. Safra Bioinformatics Program at Tel Aviv University
- Irish Research Council for Science Engineering and Technology under the EMBARK
- US National Cancer Institute [U54CA132383]
- US National Science Foundation [HRD-0420407]
- Sardinian Regional Authorities
- Fonds pour la formation a la Recherche dans l'Industrie et dans l'Agriculture
- Belgian State, Science Policy Office
- French Community of Belgium (ARC Biomod)
- European Network of Excellence [PASCAL2]
- European Community
- Division Of Integrative Organismal Systems
- Direct For Biological Sciences [1126971] Funding Source: National Science Foundation
- Agence Nationale de la Recherche (ANR) [ANR-07-BLAN-0311] Funding Source: Agence Nationale de la Recherche (ANR)
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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 similar to 1,700 transcriptional interactions at a precision of similar to 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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