Reference: Marbach D, et al. (2012) Wisdom of crowds for robust gene network inference.LID - 10.1038/nmeth.2016 [doi] Nat Methods

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Abstract

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 approximately 1,700 transcriptional interactions at a precision of approximately 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.

Reference Type
Journal Article
Authors
Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Aderhold A, Allison KR, Bonneau R, ... Show all
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Interaction Annotations

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Interactor Interactor Type Assay Annotation Action Modification Phenotype Source Reference

Gene Ontology Annotations

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Gene Gene Ontology Term Qualifier Aspect Method Evidence Source Assigned On Annotation Extension Reference

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Gene Phenotype Experiment Type Mutant Information Strain Background Chemical Details Reference

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Regulator Target Experiment Assay Construct Conditions Strain Background Reference