RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat –Omics Data
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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2074696"], "description"=>"<p>Venn diagram showing node (A) and edge (B) homology between the RulNet platform, WGCNA and Pearson network inference methods.</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420072, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.g005", "stats"=>{"downloads"=>4, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Node_and_edges_comparison_between_three_inference_methods_/1420072", "title"=>"Node and edges comparison between three inference methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074694"], "description"=>"<p>(A) and the GCNA (B) and Pearson (C) methods and consensus network obtained using the intersection algorithm of Cytoscape (D). In A, B, and C node color indicate the conservation between the three methods: green, nodes specific to the network; blue, nodes common with one other network; and red nodes found with all three methods. In D, node color is dependent on node degree, from gray for less connected nodes to red for nodes showing the highest connectivity. To enhance the clarity of the figure, disconnected nodes and connected components involving two nodes or less are not shown.</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420070, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Undirected_scale_free_regulatory_networks_inferred_with_the_RulNet_platform_/1420070", "title"=>"Undirected scale-free regulatory networks inferred with the RulNet platform.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074710", "https://ndownloader.figshare.com/files/2074711", "https://ndownloader.figshare.com/files/2074712", "https://ndownloader.figshare.com/files/2074713", "https://ndownloader.figshare.com/files/2074715", "https://ndownloader.figshare.com/files/2074716", "https://ndownloader.figshare.com/files/2074717", "https://ndownloader.figshare.com/files/2074718", "https://ndownloader.figshare.com/files/2074719", "https://ndownloader.figshare.com/files/2074720", "https://ndownloader.figshare.com/files/2074721", "https://ndownloader.figshare.com/files/2074722", "https://ndownloader.figshare.com/files/2074723", "https://ndownloader.figshare.com/files/2074724", "https://ndownloader.figshare.com/files/2074725", "https://ndownloader.figshare.com/files/2074726"], "description"=>"<div><p>With the increasing amount of –omics data available, a particular effort has to be made to provide suitable analysis tools. A major challenge is that of unraveling the molecular regulatory networks from massive and heterogeneous datasets. Here we describe RulNet, a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data by means of rule discovery. Queries for rule discovery can be written in an extended form of the RQL query language, which has a syntax similar to SQL. RulNet also offers users interactive features that progressively adjust and refine the inferred networks. In this paper, we present a functional characterization of RulNet and compare inferred networks with correlation-based approaches. The performance of RulNet has been evaluated using the three benchmark datasets used for the transcriptional network inference challenge DREAM5. Overall, RulNet performed as well as the best methods that participated in this challenge and it was shown to behave more consistently when compared across the three datasets. Finally, we assessed the suitability of RulNet to analyze experimental –omics data and to infer regulatory networks involved in the response to nitrogen and sulfur supply in wheat (<i>Triticum aestivum</i> L.) grains. The results highlight putative actors governing the response to nitrogen and sulfur supply in wheat grains. We evaluate the main characteristics and features of RulNet as an all-in-one solution for RN inference, visualization and editing. Using simple yet powerful RulNet queries allowed RNs involved in the adaptation of wheat grain to N and S supply to be discovered. We demonstrate the effectiveness and suitability of RulNet as a platform for the analysis of RNs involving different types of –omics data. The results are promising since they are consistent with what was previously established by the scientific community.</p></div>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420084, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0127127.s001", "https://dx.doi.org/10.1371/journal.pone.0127127.s002", "https://dx.doi.org/10.1371/journal.pone.0127127.s003", "https://dx.doi.org/10.1371/journal.pone.0127127.s004", "https://dx.doi.org/10.1371/journal.pone.0127127.s005", "https://dx.doi.org/10.1371/journal.pone.0127127.s006", "https://dx.doi.org/10.1371/journal.pone.0127127.s007", "https://dx.doi.org/10.1371/journal.pone.0127127.s008", "https://dx.doi.org/10.1371/journal.pone.0127127.s009", "https://dx.doi.org/10.1371/journal.pone.0127127.s010", "https://dx.doi.org/10.1371/journal.pone.0127127.s011", "https://dx.doi.org/10.1371/journal.pone.0127127.s012", "https://dx.doi.org/10.1371/journal.pone.0127127.s013", "https://dx.doi.org/10.1371/journal.pone.0127127.s014", "https://dx.doi.org/10.1371/journal.pone.0127127.s015", "https://dx.doi.org/10.1371/journal.pone.0127127.s016"], "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_RulNet_A_Web_Oriented_Platform_for_Regulatory_Network_Inference_Application_to_Wheat_8211_Omics_Data_/1420084", "title"=>"RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat –Omics Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074690"], "description"=>"<p>The overall workflow from user’s point of view consists of three steps that are data upload, query design and visualization and edition of inferred networks. These steps can be saved and reloaded afterwards when using a registered account.</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420066, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.g002", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scheme_of_the_workflow_of_the_RulNet_platform_and_leading_to_the_discovery_and_visualization_of_rules_/1420066", "title"=>"Scheme of the workflow of the RulNet platform and leading to the discovery and visualization of rules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074703"], "description"=>"<p>Network topological properties of regulatory networks inferred using RulNet, WGCNA and Pearson methods.</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420079, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.t001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_topological_properties_of_regulatory_networks_inferred_using_RulNet_WGCNA_and_Pearson_methods_/1420079", "title"=>"Network topological properties of regulatory networks inferred using RulNet, WGCNA and Pearson methods.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074702"], "description"=>"<p>Directed network inferred using the RulNet platform and illustrating the use of central attributes. Linkages of transcription factors expression (hexagons), and the quantity per grain of storage proteins (squares) and metabolites (circles) with the phases of grain development and nitrogen and sulfur deficiencies defined as central attributes. The network was exported and enhanced in Cytoscape. Nodes were moved and edges were bundled and reorganized for better readability. Pink and light blue edges indicate rules discovered with the QNS1 and QNS2 queries, respectively. The storage proteins ω1,2-, ω5-, γ- and α/β-gliadins (gli) and low (LMW-GS) and high (HMW-GS) molecular weight glutenin subunits were expressed in mg N per grain and the metabolites in μmol per grain. For metabolites and transcription factors, the correspondence between the node id and actual entity names is given in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127127#pone.0127127.s015\" target=\"_blank\">S1</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127127#pone.0127127.s016\" target=\"_blank\">S2</a> Tables.</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420078, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.g007", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nitrogen_and_Sulfur_influenced_regulatory_network_in_wheat_/1420078", "title"=>"Nitrogen and Sulfur influenced regulatory network in wheat.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074698"], "description"=>"<p>The overall (geometric mean across the three inferred networks) area under the precision-recall (AUPR) and receiver operating characteristic (AUROC) and overall score (mean of the overall AUPR and AUROC scores) obtained using RulNet with the QD1, QD2 and QD1+2 queries (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127127#pone.0127127.s014\" target=\"_blank\">S3 File</a>) were compared to the 35 methods evaluated in the DREAM5 challenge. The horizontal continuous red lines indicate the scores of the integrated community predictions from DREAM5, the vertical dashed green lines indicate the scores of the best individual model from DREAM5 based on the overall score, and the horizontal dash-dot blue lines indicate the median score for 35 methods evaluated in the DREAM5 challenge.</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420074, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.g006", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_RulNet_performance_using_the_DREAM5_network_inference_challenge_datasets_/1420074", "title"=>"Evaluation of RulNet performance using the DREAM5 network inference challenge datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074692"], "description"=>"<p>Computing time (dashed lines) and number of rules (solid lines) for query Q1 (A, B) and Q2 (C, D) for 1–1 rules and for query Q1 for n-1 rules with n ≤ 3 (E, F) applied to a randomized dataset with 10 to 4,000 samples (A, C, E) and attributes (B, D, F).</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420068, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.g003", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computing_time_and_number_of_rules_of_networks_inferred_with_the_RulNet_platform_/1420068", "title"=>"Computing time and number of rules of networks inferred with the RulNet platform.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-19 03:12:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/2074689"], "description"=>"<p>Table gen (gene expression), met (metabolites assay) and ann (sample annotation), containing heterogeneous data for six samples. These tables contain quantitative attributes (<i>G</i>1, <i>G</i>2, <i>G</i>3, <i>G</i>4, <i>M</i>1, <i>M</i>2, <i>M</i>3, <i>A</i>3), binary attributes (<i>A</i>1, <i>A</i>2) and categorical attributes (time).</p>", "links"=>[], "tags"=>["rn", "sql", "omic", "Regulatory Network Inference", "rule discovery", "transcriptional network inference challenge DREAM 5.", "RQL query language", "sulfur supply", "data", "RulNet"], "article_id"=>1420065, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jonathan Vincent", "Pierre Martre", "Benjamin Gouriou", "Catherine Ravel", "Zhanwu Dai", "Jean-Marc Petit", "Marie Pailloux"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0127127.g001", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_a_database_composed_of_three_tables_/1420065", "title"=>"Example of a database composed of three tables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-19 03:12:21"}

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  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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