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LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Author

Listed:
  • Mingyi Wang
  • Jerome Verdier
  • Vagner A Benedito
  • Yuhong Tang
  • Jeremy D Murray
  • Yinbing Ge
  • Jörg D Becker
  • Helena Carvalho
  • Christian Rogers
  • Michael Udvardi
  • Ji He

Abstract

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.

Suggested Citation

  • Mingyi Wang & Jerome Verdier & Vagner A Benedito & Yuhong Tang & Jeremy D Murray & Yinbing Ge & Jörg D Becker & Helena Carvalho & Christian Rogers & Michael Udvardi & Ji He, 2013. "LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-7, July.
    • Handle: RePEc:plo:pone00:0067434
    DOI: 10.1371/journal.pone.0067434
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    References listed on IDEAS

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    1. Vân Anh Huynh-Thu & Alexandre Irrthum & Louis Wehenkel & Pierre Geurts, 2010. "Inferring Regulatory Networks from Expression Data Using Tree-Based Methods," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-10, September.
    2. Jeremiah J Faith & Boris Hayete & Joshua T Thaden & Ilaria Mogno & Jamey Wierzbowski & Guillaume Cottarel & Simon Kasif & James J Collins & Timothy S Gardner, 2007. "Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles," PLOS Biology, Public Library of Science, vol. 5(1), pages 1-13, January.
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