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. © 2012 Nature America, Inc. All rights reserved.

Wisdom of crowds for robust gene network inference

CORDERO, Francesca;ESPOSITO, Roberto;VISCONTI, ALESSIA;
2012

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 ∼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. © 2012 Nature America, Inc. All rights reserved.
NATURE METHODS
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algorithm; article; bacterial metabolism; Bayes theorem; biofilm; bootstrapping; computer model; controlled study; Escherichia coli; gene control; gene expression; gene interaction; gene regulatory network; genetic association; genetic transcription; genetic variability; microarray analysis; microbial community; Monte Carlo method; nonhuman; principal component analysis; priority journal; Saccharomyces cerevisiae; Staphylococcus aureus Species Index: Escherichia coli; Staphylococcus aureus
Marbach D.; Costello C.; Küffner R.; Vega M.; Prill J.; Camacho D.M.; Allison R.; Kellis M.; Collins J.J.; Aderhold A.; Stolovitzky; G.g ; Bonneau R.; Chen Y.; Cordero F.; Crane M.; Dondelinger F.; Drton M.; Esposito R.; Foygel R.; De La Fuente A.; Gertheiss J.; Geurts P.; Greenfield A.; Grzegorczyk M.; Haury A.C.; Holmes B.; Hothorn T.; Husmeier D.; Huynh-Thu A.; Irrthum A.; Karlebach G.; Lèbre S.; De Leo V.; Madar A.; Mani S.; Mordelet F.; Ostrer H.; Ouyang Z.; Pandya R.; Petri T.; Pinna A.; Poultney S.; Rezny S.; Ruskin J.; Saeys Y.; Shamir R.; Sîrbu A.; Song M.; Soranzo N.; Statnikov; A.alam; Vega N.; Vera-Licona P.; Vert J.P.; Visconti A.; Wang H.; Wehenkel L.; Windhager L.; Zhang Y.; Zimmer R.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/122548
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