{
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  "Package": "BioNERO",
  "Type": "Package",
  "Title": "Biological Network Reconstruction Omnibus",
  "Version": "1.21.0",
  "Authors@R": "c(person(\"Fabricio\", \"Almeida-Silva\", \nemail=\"fabricio_almeidasilva@hotmail.com\",\nrole=c(\"cre\", \"aut\"),\ncomment = c(ORCID = \"0000-0002-5314-2964\")),\nperson(\"Thiago\", \"Venancio\",\nrole=\"aut\",\ncomment = c(ORCID = \"0000-0002-2215-8082\")))",
  "Description": "BioNERO aims to integrate all aspects of biological\nnetwork inference in a single package, including data\npreprocessing, exploratory analyses, network inference, and\nanalyses for biological interpretations. BioNERO can be used to\ninfer gene coexpression networks (GCNs) and gene regulatory\nnetworks (GRNs) from gene expression data. Additionally, it can\nbe used to explore topological properties of protein-protein\ninteraction (PPI) networks. GCN inference relies on the popular\nWGCNA algorithm. GRN inference is based on the \"wisdom of the\ncrowds\" principle, which consists in inferring GRNs with\nmultiple algorithms (here, CLR, GENIE3 and ARACNE) and\ncalculating the average rank for each interaction pair. As all\nsteps of network analyses are included in this package, BioNERO\nmakes users avoid having to learn the syntaxes of several\npackages and how to communicate between them. Finally, users\ncan also identify consensus modules across independent\nexpression sets and calculate intra and interspecies module\npreservation statistics between different networks.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "URL": "https://github.com/almeidasilvaf/BioNERO",
  "BugReports": "https://github.com/almeidasilvaf/BioNERO/issues",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:55:27 UTC",
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  "Author": "Fabricio Almeida-Silva [cre, aut] (ORCID:\n<https://orcid.org/0000-0002-5314-2964>),\nThiago Venancio [aut] (ORCID: <https://orcid.org/0000-0002-2215-8082>)",
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  "_exports": [
    "check_SFT",
    "consensus_modules",
    "consensus_SFT_fit",
    "consensus_trait_cor",
    "cor2adj",
    "cormat_to_edgelist",
    "detect_communities",
    "dfs2one",
    "enrichment_analysis",
    "exp_genes2orthogroups",
    "exp_preprocess",
    "exp2cor",
    "exp2gcn",
    "exp2gcn_blockwise",
    "exp2grn",
    "filter_by_variance",
    "gene_significance",
    "get_edge_list",
    "get_HK",
    "get_hubs_gcn",
    "get_hubs_grn",
    "get_hubs_ppi",
    "get_neighbors",
    "grn_average_rank",
    "grn_combined",
    "grn_filter",
    "grn_infer",
    "is_singleton",
    "modPres_netrep",
    "modPres_WGCNA",
    "module_enrichment",
    "module_preservation",
    "module_stability",
    "module_trait_cor",
    "net_stats",
    "parse_orthofinder",
    "PC_correction",
    "plot_dendro_and_colors",
    "plot_eigengene_network",
    "plot_expression_profile",
    "plot_gcn",
    "plot_gene_significance",
    "plot_grn",
    "plot_heatmap",
    "plot_module_trait_cor",
    "plot_ngenes_per_module",
    "plot_PCA",
    "plot_ppi",
    "q_normalize",
    "remove_nonexp",
    "replace_na",
    "SFT_fit",
    "ZKfiltering"
  ],
  "_datasets": [
    {
      "name": "filt.se",
      "title": "Filtered maize gene expression data from Shin et al., 2021.",
      "object": "filt.se",
      "file": "filt.se.rda",
      "class": [
        "SummarizedExperiment"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "og.zma.osa",
      "title": "Orthogroups between maize and rice",
      "object": "og.zma.osa",
      "file": "og.zma.osa.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Family",
        "Species",
        "Gene"
      ],
      "rows": 18034,
      "table": true,
      "tojson": true
    },
    {
      "name": "osa.se",
      "title": "Rice gene expression data from Shin et al., 2021.",
      "object": "osa.se",
      "file": "osa.se.rda",
      "class": [
        "SummarizedExperiment"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "zma.interpro",
      "title": "Maize Interpro annotation",
      "object": "zma.interpro",
      "file": "zma.interpro.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Gene",
        "Description"
      ],
      "rows": 27655,
      "table": true,
      "tojson": true
    },
    {
      "name": "zma.se",
      "title": "Maize gene expression data from Shin et al., 2021.",
      "object": "zma.se",
      "file": "zma.se.rda",
      "class": [
        "SummarizedExperiment"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "zma.tfs",
      "title": "Maize transcription factors",
      "object": "zma.tfs",
      "file": "zma.tfs.rda",
      "class": [
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      ],
      "fields": [
        "Gene",
        "Family"
      ],
      "rows": 387,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "check_SFT",
      "title": "Check scale-free topology fit for a given network",
      "topics": [
        "check_SFT"
      ]
    },
    {
      "page": "consensus_modules",
      "title": "Identify consensus modules across independent data sets",
      "topics": [
        "consensus_modules"
      ]
    },
    {
      "page": "consensus_SFT_fit",
      "title": "Pick power to fit networks to scale-free topology",
      "topics": [
        "consensus_SFT_fit"
      ]
    },
    {
      "page": "consensus_trait_cor",
      "title": "Correlate set-specific modules and consensus modules to sample information",
      "topics": [
        "consensus_trait_cor"
      ]
    },
    {
      "page": "cor2adj",
      "title": "Calculate an adjacency matrix from a correlation matrix",
      "topics": [
        "cor2adj"
      ]
    },
    {
      "page": "cormat_to_edgelist",
      "title": "Transform a correlation matrix to an edge list",
      "topics": [
        "cormat_to_edgelist"
      ]
    },
    {
      "page": "detect_communities",
      "title": "Detect communities in a network",
      "topics": [
        "detect_communities"
      ]
    },
    {
      "page": "dfs2one",
      "title": "Combine multiple expression tables (.tsv) into a single data frame",
      "topics": [
        "dfs2one"
      ]
    },
    {
      "page": "enrichment_analysis",
      "title": "Perform overrepresentation analysis for a set of genes",
      "topics": [
        "enrichment_analysis"
      ]
    },
    {
      "page": "exp_genes2orthogroups",
      "title": "Collapse gene-level expression data to orthogroup level",
      "topics": [
        "exp_genes2orthogroups"
      ]
    },
    {
      "page": "exp_preprocess",
      "title": "Preprocess expression data for network reconstruction",
      "topics": [
        "exp_preprocess"
      ]
    },
    {
      "page": "exp2cor",
      "title": "Calculate pairwise correlations between genes in a matrix",
      "topics": [
        "exp2cor"
      ]
    },
    {
      "page": "exp2gcn",
      "title": "Infer gene coexpression network from gene expression",
      "topics": [
        "exp2gcn"
      ]
    },
    {
      "page": "exp2gcn_blockwise",
      "title": "Infer gene coexpression network from gene expression in a blockwise manner",
      "topics": [
        "exp2gcn_blockwise"
      ]
    },
    {
      "page": "exp2grn",
      "title": "Infer gene regulatory network from expression data",
      "topics": [
        "exp2grn"
      ]
    },
    {
      "page": "filt.se",
      "title": "Filtered maize gene expression data from Shin et al., 2021.",
      "topics": [
        "filt.se"
      ]
    },
    {
      "page": "filter_by_variance",
      "title": "Keep only genes with the highest variances",
      "topics": [
        "filter_by_variance"
      ]
    },
    {
      "page": "gene_significance",
      "title": "Calculate gene significance for a given group of genes",
      "topics": [
        "gene_significance"
      ]
    },
    {
      "page": "get_edge_list",
      "title": "Get edge list from an adjacency matrix for a group of genes",
      "topics": [
        "get_edge_list"
      ]
    },
    {
      "page": "get_HK",
      "title": "Get housekeeping genes from global expression profile",
      "topics": [
        "get_HK"
      ]
    },
    {
      "page": "get_hubs_gcn",
      "title": "Get GCN hubs",
      "topics": [
        "get_hubs_gcn"
      ]
    },
    {
      "page": "get_hubs_grn",
      "title": "Get hubs for gene regulatory network",
      "topics": [
        "get_hubs_grn",
        "get_hubs_ppi"
      ]
    },
    {
      "page": "get_neighbors",
      "title": "Get 1st-order neighbors of a given gene or group of genes",
      "topics": [
        "get_neighbors"
      ]
    },
    {
      "page": "grn_average_rank",
      "title": "Rank edge weights for GRNs and calculate average across different methods",
      "topics": [
        "grn_average_rank"
      ]
    },
    {
      "page": "grn_combined",
      "title": "Infer gene regulatory network with multiple algorithms and combine results in a list",
      "topics": [
        "grn_combined"
      ]
    },
    {
      "page": "grn_filter",
      "title": "Filter a gene regulatory network based on optimal scale-free topology fit",
      "topics": [
        "grn_filter"
      ]
    },
    {
      "page": "grn_infer",
      "title": "Infer gene regulatory network with one of three algorithms",
      "topics": [
        "grn_infer"
      ]
    },
    {
      "page": "is_singleton",
      "title": "Logical expression to check if gene or gene set is singleton or not",
      "topics": [
        "is_singleton"
      ]
    },
    {
      "page": "modPres_netrep",
      "title": "Calculate module preservation between two expression data sets using NetRep's algorithm",
      "topics": [
        "modPres_netrep"
      ]
    },
    {
      "page": "modPres_WGCNA",
      "title": "Calculate module preservation between two expression data sets using WGCNA's algorithm",
      "topics": [
        "modPres_WGCNA"
      ]
    },
    {
      "page": "module_enrichment",
      "title": "Perform enrichment analysis for coexpression network modules",
      "topics": [
        "module_enrichment"
      ]
    },
    {
      "page": "module_preservation",
      "title": "Calculate network preservation between two expression data sets",
      "topics": [
        "module_preservation"
      ]
    },
    {
      "page": "module_stability",
      "title": "Perform module stability analysis",
      "topics": [
        "module_stability"
      ]
    },
    {
      "page": "module_trait_cor",
      "title": "Correlate module eigengenes to trait",
      "topics": [
        "module_trait_cor"
      ]
    },
    {
      "page": "net_stats",
      "title": "Calculate network statistics",
      "topics": [
        "net_stats"
      ]
    },
    {
      "page": "og.zma.osa",
      "title": "Orthogroups between maize and rice",
      "topics": [
        "og.zma.osa"
      ]
    },
    {
      "page": "osa.se",
      "title": "Rice gene expression data from Shin et al., 2021.",
      "topics": [
        "osa.se"
      ]
    },
    {
      "page": "parse_orthofinder",
      "title": "Parse orthogroups identified by OrthoFinder",
      "topics": [
        "parse_orthofinder"
      ]
    },
    {
      "page": "PC_correction",
      "title": "Apply Principal Component (PC)-based correction for confounding artifacts",
      "topics": [
        "PC_correction"
      ]
    },
    {
      "page": "plot_dendro_and_colors",
      "title": "Plot dendrogram of genes and modules",
      "topics": [
        "plot_dendro_and_colors"
      ]
    },
    {
      "page": "plot_eigengene_network",
      "title": "Plot eigengene network",
      "topics": [
        "plot_eigengene_network"
      ]
    },
    {
      "page": "plot_expression_profile",
      "title": "Plot expression profile of given genes across samples",
      "topics": [
        "plot_expression_profile"
      ]
    },
    {
      "page": "plot_gcn",
      "title": "Plot gene coexpression network from edge list",
      "topics": [
        "plot_gcn"
      ]
    },
    {
      "page": "plot_gene_significance",
      "title": "Plot a heatmap of gene significance",
      "topics": [
        "plot_gene_significance"
      ]
    },
    {
      "page": "plot_grn",
      "title": "Plot gene regulatory network from edge list",
      "topics": [
        "plot_grn"
      ]
    },
    {
      "page": "plot_heatmap",
      "title": "Plot heatmap of hierarchically clustered sample correlations or gene expression",
      "topics": [
        "plot_heatmap"
      ]
    },
    {
      "page": "plot_module_trait_cor",
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