Open Access
Wisdom of crowds for robust gene network inference
Daniel Marbach,James C. Costello,Robert Küffner,Nicole M. Vega,Robert J. Prill,Diogo M. Camacho,Kyle R. Allison,Andrej Aderhold,Richard Bonneau,Yukun Chen,James J. Collins,Francesca Cordero,Martin Crane,Frank Dondelinger,Mathias Drton,Roberto Esposito,Rina Foygel,Alberto de la Fuente,Jan Gertheiss,Pierre Geurts,Alex Greenfield,Marco Grzegorczyk,Anne-Claire Haury,Benjamin Holmes,Torsten Hothorn,Dirk Husmeier,Vân Anh Huynh-Thu,Alexandre Irrthum,Manolis Kellis,Guy Karlebach,Sophie Lèbre,Vincenzo De Leo,Aviv Madar,Subramani Mani,Fantine Mordelet,Harry Ostrer,Zhengyu Ouyang,Ravi Pandya,Tobias Petri,Andrea Pinna,Christopher S. Poultney,Serena Rezny,Heather J. Ruskin,Yvan Saeys,Ron Shamir,Alina Sîrbu,Mingzhou Song,Nicola Soranzo,Alexander Statnikov,Gustavo Stolovitzky,Nicci Vega,Paola Vera-Licona,Jean-Philippe Vert,Alessia Visconti,Haizhou Wang,Louis Wehenkel,Lukas Windhager,Yang Zhang,Ralf Zimmer +58 more
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TLDR
A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.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.read more
Citations
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Journal ArticleDOI
SCENIC: single-cell regulatory network inference and clustering.
Sara Aibar,Carmen Bravo González-Blas,Thomas Moerman,Vân Anh Huynh-Thu,Hana Imrichova,Gert Hulselmans,Florian Rambow,Jean-Christophe Marine,Pierre Geurts,Jan Aerts,Joost van den Oord,Zeynep Kalender Atak,Jasper Wouters,Stein Aerts +13 more
TL;DR: On a compendium of single-cell data from tumors and brain, it is demonstrated that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states.
Journal ArticleDOI
Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation
Jia Xue,Susanne Schmidt,Jil Sander,Astrid M. Draffehn,Wolfgang Krebs,Inga Quester,Dominic De Nardo,Trupti D. Gohel,Martina Emde,Lisa Schmidleithner,Hariharasudan Ganesan,Andrea Niño-Castro,Michael R. Mallmann,Larisa I. Labzin,Heidi Theis,Michael Kraut,Marc Beyer,Eicke Latz,Eicke Latz,Tom C. Freeman,Thomas Ulas,Joachim L. Schultze +21 more
TL;DR: By integrating murine data from the ImmGen project, this work proposes a refined, activation-independent core signature for human and murine macrophages that serves as a framework for future research into regulation of macrophage activation in health and disease.
Posted ContentDOI
SCENIC: Single-Cell Regulatory Network Inference And Clustering
Sara Aibar,Carmen Bravo González-Blas,Thomas Moerman,Jasper Wouters,Vân Anh Huynh-Thu,Hana Imrichova,Zeynep Kalender Atak,Gert Hulselmans,Michael Dewaele,Florian Rambow,Pierre Geurts,Jan Aerts,Jean-Christophe Marine,Joost van den Oord,Stein Aerts +14 more
TL;DR: SCENIC (Single Cell rEgulatory Network Inference and Clustering) is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs.
Journal ArticleDOI
A Validated Regulatory Network for Th17 Cell Specification
Maria Ciofani,Aviv Madar,Aviv Madar,Carolina Galan,MacLean Sellars,Kieran Mace,Florencia Pauli,Ashish Agarwal,Wendy Huang,Christopher N. Parkurst,Michael Muratet,Kim M. Newberry,Sarah Meadows,Alex Greenfield,Yi Yang,Preti Jain,Francis K. Kirigin,Carmen Birchmeier,Erwin F. Wagner,Kenneth M. Murphy,Kenneth M. Murphy,Richard M. Myers,Richard Bonneau,Richard Bonneau,Dan R. Littman,Dan R. Littman +25 more
TL;DR: It is found that cooperatively bound BATF and IRF4 contribute to initial chromatin accessibility and, with STAT3, initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci.
Journal ArticleDOI
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review
Peter Csermely,Tamas Korcsmaros,Tamas Korcsmaros,Huba Kiss,Gábor London,Ruth Nussinov,Ruth Nussinov +6 more
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.
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