Dataset Information

ProdoNet: identification and visualization of prokaryotic gene regulatory and metabolic networks.

ABSTRACT: ProdoNet is a web-based application for the mapping of prokaryotic genes and the corresponding proteins to common gene regulatory and metabolic networks. For a given list of genes, the system detects shared operons, identifies co-expressed genes and deduces joint regulators. In addition, the contribution to shared metabolic pathways becomes visible on KEGG maps. Furthermore, the co-occurrence of genes of interest in gene expression profiles can be added to the visualization of the global network. In this way, ProdoNet provides the basis for functional genomics approaches and for the interpretation of transcriptomics and proteomics data. As an example, we present an investigation of an experimental membrane subproteome analysis of Pseudomonas aeruginosa with ProdoNet. The ProdoNet dataset on transcriptional regulation is based on the PRODORIC Prokaryotic Database of Gene Regulation and the Virtual Footprint tool. ProdoNet is accessible at http://www.prodonet.tu-bs.de.

SUBMITTER: Klein J

PROVIDER: S-EPMC2447764 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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ProdoNet: identification and visualization of prokaryotic gene regulatory and metabolic networks.

Klein Johannes J Leupold Stefan S Münch Richard R Pommerenke Claudia C Johl Thorsten T Kärst Uwe U Jänsch Lothar L Jahn Dieter D Retter Ida I

Nucleic acids research 20080425 Web Server issue

ProdoNet is a web-based application for the mapping of prokaryotic genes and the corresponding proteins to common gene regulatory and metabolic networks. For a given list of genes, the system detects shared operons, identifies co-expressed genes and deduces joint regulators. In addition, the contribution to shared metabolic pathways becomes visible on KEGG maps. Furthermore, the co-occurrence of genes of interest in gene expression profiles can be added to the visualization of the global network ...[more]

PMID: 18440972

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Project description:BackgroundFeedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks.ResultsHiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition.ConclusionsHiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop's usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop .

Project description:BackgroundComparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.ResultsHere we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.ConclusionsOur results demonstrate the applicability of the CGB pipeline in multiple settings. CGB's ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

Dataset Information

ProdoNet: identification and visualization of prokaryotic gene regulatory and metabolic networks.

Publications

ProdoNet: identification and visualization of prokaryotic gene regulatory and metabolic networks.

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