NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference

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Abstract

Background

In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Results

Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.

Conclusions

The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0728-4) contains supplementary material, which is available to authorized users.

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Most cited references 21

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Identification of functional elements and regulatory circuits by Drosophila modENCODE.

Sushmita Roy, Jason Ernst, Peter V. Kharchenko … (2011)

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.

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

Adam Margolin, Ilya Nemenman, Katia Basso … (2006)

Background Elucidating 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. Results We 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. Conclusion ARACNE 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.

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Advantages and limitations of current network inference methods.

Riet De Smet, Kathleen Marchal (2010)

Network inference, which is the reconstruction of biological networks from high-throughput data, can provide valuable information about the regulation of gene expression in cells. However, it is an underdetermined problem, as the number of interactions that can be inferred exceeds the number of independent measurements. Different state-of-the-art tools for network inference use specific assumptions and simplifications to deal with underdetermination, and these influence the inferences. The outcome of network inference therefore varies between tools and can be highly complementary. Here we categorize the available tools according to the strategies that they use to deal with the problem of underdetermination. Such categorization allows an insight into why a certain tool is more appropriate for the specific research question or data set at hand.

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Author and article information

Contributors

Pau Bellot: pau.bellot@upc.edu

Catharina Olsen: colsen@ulb.ac.be

Philippe Salembier: philippe.salembier@upc.edu

Albert Oliveras-Vergés: albert.oliveras@upc.edu

Patrick E. Meyer: patrick.meyer@ulg.ac.be

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date (Electronic): 29 September 2015

Publication date PMC-release: 29 September 2015

Publication date Collection: 2015

Volume: 16

Electronic Location Identifier: 312

Affiliations

[ ]Universitat Politecnica de Catalunya BarcelonaTECH, Department of Signal Theory and Communications, UPC-Campus Nord, C/ Jordi Girona, 1-3, Barcelona, 08034 Spain

[ ]Bioinformatics and Systems Biology (BioSys), Faculty of Sciences, Université de Liège (ULg), 27 Blvd du Rectorat, Liège, 4000 Belgium

[ ]Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium

[ ]Interuniversity Institute of Bioinformatics Brussels, (IB)², Brussels, Belgium

Article

Publisher ID: 728

DOI: 10.1186/s12859-015-0728-4

PMC ID: 4587916

PubMed ID: 26415849

SO-VID: acbf498c-8f87-4197-9d65-ed16635a0918

License:

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 18 March 2015

Date accepted : 6 September 2015

Custom metadata

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: bioconductor package,gene regulatory networks,gene expression,gene regulation network reconstruction,synthetic genetic networks,benchmark

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: bioconductor package, gene regulatory networks, gene expression, gene regulation network reconstruction, synthetic genetic networks, benchmark

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference

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Abstract

Background

Results

Conclusions

Electronic supplementary material

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Most cited references 21

Identification of functional elements and regulatory circuits by Drosophila modENCODE.

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

Advantages and limitations of current network inference methods.

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