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      CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks

      research-article
      Author(s): , ,
      Publication date (Electronic):
      Journal: Source Code for Biology and Medicine
      Publisher: BioMed Central

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          Abstract

          Background

          Inference of gene networks from expression data is an important problem in computational biology. Many algorithms have been proposed for solving the problem efficiently. However, many of the available implementations are programming libraries that require users to write code, which limits their accessibility.

          Results

          We have developed a tool called CyNetworkBMA for inferring gene networks from expression data that integrates with Cytoscape. Our application offers a graphical user interface for networkBMA, an efficient implementation of Bayesian Model Averaging methods for network construction. The client-server architecture of CyNetworkBMA makes it possible to distribute or centralize computation depending on user needs.

          Conclusions

          CyNetworkBMA is an easy-to-use tool that makes network inference accessible to non-programmers through seamless integration with Cytoscape. CyNetworkBMA is available on the Cytoscape App Store at http://apps.cytoscape.org/apps/cynetworkbma.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13029-015-0043-5) contains supplementary material, which is available to authorized users.

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          Most cited references23

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          Modelling and analysis of gene regulatory networks.

          Guy Karlebach, Ron Shamir (2008)
          Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.
          Article 0 comments Cited 369 times – based on 0 reviews     Review now
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            Gene regulatory network inference: data integration in dynamic models-a review.

            Michael Hecker, Sandro Lambeck, Susanne Toepfer … (2009)
            Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.
            Article 0 comments Cited 261 times – based on 0 reviews     Review now
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              Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.

              Jun Zhu, Bin Zhang, Erin N Smith … (2008)
              A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.
              Article 0 comments Cited 231 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Maciej Fronczuk: mfroncz@u.washington.edu
                Ka Yee Yeung: kayee@uw.edu
                Journal
                Journal ID (nlm-ta): Source Code Biol Med
                Journal ID (iso-abbrev): Source Code Biol Med
                Title: Source Code for Biology and Medicine
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1751-0473
                Publication date (Electronic): 11 November 2015
                Publication date PMC-release: 11 November 2015
                Publication date Collection: 2015
                Volume: 10
                Electronic Location Identifier: 11
                Affiliations
                [ ]Institute of Technology, University of Washington, Tacoma, 98402 WA USA
                [ ]Department of Statistics, University of Washington, Seattle, 98195 WA USA
                Article
                Publisher ID: 43
                DOI: 10.1186/s13029-015-0043-5
                PMC ID: 4642660
                SO-VID: cc056069-8a9a-478f-9d88-c035549addbd
                Copyright © © Fronczuk et al. 2015
                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 : 22 December 2014
                Date accepted : 31 October 2015
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © The Author(s) 2015

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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