ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

By Adam A. Margolin, Ilya Nemenman, Katia Basso, Chris H. Wiggins, Gustavo A. Stolovitzky, Riccardo Dalla Favera, Andrea Califano. BMC Bioinformatics, 2004

2,804 citations recorded by the source corpus.

BackgroundElucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.ResultsWe prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.ConclusionARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.

View Paper: https://www.semanticscholar.org/paper/e8ed956ce6a454ffb74a915f02280799eb1f133a

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Citations in the corpus

CoNet app: inference of biological association networks using Cytoscape (2016, unknown): “We would like to thank the reviewer for this appreciation of our work.”
Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, methodology): “This correlation analysis was performed on the same dataset used to infer the network by ARACNe.”
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information (2008, methodology): “The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) [8] is based on the Data Processing Inequality [16].”
Aging effects on DNA methylation modules in human brain and blood tissue (2012, methodology): “A recent review article compares the performance of WGCNA to ARACNE and other algorithms [49].”
Revealing strengths and weaknesses of methods for gene network inference (2010, background): “Even though several interesting and telling efforts to compare between different network-inference methods have been reported (13, 14, 15), these efforts typically compare a small number of algorithms that include methods developed by the same authors that do the comparisons.”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “…approaches have been used for network inference from population level data (Penfold et al. 2011, Penfold et al. 2015, Bonneau et al. 2006, Margolin et al. 2006a, Villaverde et al. 2013, Villaverde et al. 2013, Liang et al. 2008, Madar et al. 2010, Hill et al. 2012, Lèbre et al. 2010,…”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, unknown): “ARACNE [Basso et al., 2005, Margolin et al., 2006] belongs to the family of informationtheoretic approaches to gene network inference with their relevance network algorithm [Butte and Kohane, 2000].”
Network analysis methods for studying microbial communities: A mini review (2021, methodology): “It employs four different approaches, such as Sign Pattern [69], Overlap [68], Interaction Information [69,70], and Data Processing Inequality [71,72] to identify indirect (environmentally-driven) edges.”
ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems (2010, methodology): “…of molecular and biological entities and properties, a critical goal must be that we also enhance our ability to identify components, activities and interactions responsible for systems functions and regulatory mechanisms such as has been pioneered in applications such as WGCNA (7) and ARACNE (8).”