{
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  "Package": "coMethDMR",
  "Title": "Accurate identification of co-methylated and differentially\nmethylated regions in epigenome-wide association studies",
  "Version": "1.17.0",
  "Authors@R": "c(\nperson(\"Fernanda\", \"Veitzman\", email = \"fveit001@fiu.edu\", role = c(\"cre\")),\nperson(\"Lissette\", \"Gomez\", email = \"lxg255@miami.edu\", role = c(\"aut\")),\nperson(\"Tiago\", \"Silva\", email = \"txs902@med.miami.edu\", role = c(\"aut\")),\nperson(\"Ning\", \"Lijiao\", role = c(\"ctb\")),\nperson(\"Boissel\", \"Mathilde\", role = c(\"ctb\")),\nperson(\"Lily\", \"Wang\", email = \"lily.wang@med.miami.edu\", role = c(\"aut\")),\nperson(\"Gabriel\", \"Odom\", email = \"gabriel.odom@fiu.edu\", role = c(\"aut\"))\n)",
  "Description": "coMethDMR identifies genomic regions associated with\ncontinuous phenotypes by optimally leverages covariations among\nCpGs within predefined genomic regions. Instead of testing all\nCpGs within a genomic region, coMethDMR carries out an\nadditional step that selects co-methylated sub-regions first\nwithout using any outcome information. Next, coMethDMR tests\nassociation between methylation within the sub-region and\ncontinuous phenotype using a random coefficient mixed effects\nmodel, which models both variations between CpG sites within\nthe region and differential methylation simultaneously.",
  "License": "GPL-3",
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  "biocViews": "DNAMethylation, Epigenetics, MethylationArray,\nDifferentialMethylation, GenomeWideAssociation",
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  "BugReports": "https://github.com/TransBioInfoLab/coMethDMR/issues",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 12:57:35 UTC",
  "RemoteUrl": "https://github.com/bioc/coMethDMR",
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  "Packaged": {
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  "Author": "Fernanda Veitzman [cre],\nLissette Gomez [aut],\nTiago Silva [aut],\nNing Lijiao [ctb],\nBoissel Mathilde [ctb],\nLily Wang [aut],\nGabriel Odom [aut]",
  "Maintainer": "Fernanda Veitzman <fveit001@fiu.edu>",
  "MD5sum": "1f012535b9c997f69df5f63265fd6b38",
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  "_created": "2026-05-30T09:06:01.000Z",
  "_published": "2026-05-30T12:54:56.918Z",
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  "_exports": [
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    "CoMethAllRegions",
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    "CpGsInfoAllRegions",
    "CpGsInfoOneRegion",
    "CreateOutputDF",
    "CreateParallelWorkers",
    "CreateRdrop",
    "FindComethylatedRegions",
    "GetCpGsInRegion",
    "GetResiduals",
    "ImportSesameData",
    "lmmTest",
    "lmmTestAllRegions",
    "MarkComethylatedCpGs",
    "MarkMissing",
    "NameRegion",
    "OrderCpGsByLocation",
    "RegionsToRanges",
    "SplitCpGDFbyRegion",
    "WriteCloseByAllRegions"
  ],
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      "title": "Alzheimer's Prefrontal Cortex (PFC) Methylation Data",
      "object": "betaMatrix_ex1",
      "file": "betaMatrix_ex1.rda",
      "class": [
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      "table": true,
      "tojson": true
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      "table": true,
      "tojson": true
    },
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      "title": "Alzheimer's Prefrontal Cortex (PFC) Methylation Data",
      "object": "betaMatrix_ex3",
      "file": "betaMatrix_ex3.rda",
      "class": [
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      "fields": [
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        "cg02433884",
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        "cg13419809",
        "cg26856676",
        "cg25246745"
      ],
      "rows": 110,
      "table": true,
      "tojson": true
    },
    {
      "name": "betaMatrix_ex4",
      "title": "Alzheimer's Prefrontal Cortex (PFC) Methylation Data",
      "object": "betaMatrix_ex4",
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        "cg24040595"
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      "rows": 110,
      "table": true,
      "tojson": true
    },
    {
      "name": "betasChr22_df",
      "title": "Prefrontal Cortex (PFC) Methylation Data from Alzheimer's Disease subjects",
      "object": "betasChr22_df",
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      "object": "pheno_df",
      "file": "pheno_df.rda",
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        "sex",
        "Sample",
        "age.brain",
        "slide"
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      "table": true,
      "tojson": true
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  "_help": [
    {
      "page": "AnnotateResults",
      "title": "Annotate 'coMethDMR' Pipeline Results",
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    {
      "page": "betaMatrix_ex1",
      "title": "Alzheimer's Prefrontal Cortex (PFC) Methylation Data",
      "topics": [
        "betaMatrix_ex1"
      ]
    },
    {
      "page": "betaMatrix_ex2",
      "title": "Alzheimer's Prefrontal Cortex (PFC) Methylation Data",
      "topics": [
        "betaMatrix_ex2"
      ]
    },
    {
      "page": "betaMatrix_ex3",
      "title": "Alzheimer's Prefrontal Cortex (PFC) Methylation Data",
      "topics": [
        "betaMatrix_ex3"
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    },
    {
      "page": "betaMatrix_ex4",
      "title": "Alzheimer's Prefrontal Cortex (PFC) Methylation Data",
      "topics": [
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    },
    {
      "page": "betasChr22_df",
      "title": "Prefrontal Cortex (PFC) Methylation Data from Alzheimer's Disease subjects",
      "topics": [
        "betasChr22_df"
      ]
    },
    {
      "page": "CloseBySingleRegion",
      "title": "Extract clusters of CpGs located closely in a genomic region.",
      "topics": [
        "CloseBySingleRegion"
      ]
    },
    {
      "page": "CoMethAllRegions",
      "title": "Extract contiguous co-methylated genomic regions from a list of pre-defined genomic regions",
      "topics": [
        "CoMethAllRegions"
      ]
    },
    {
      "page": "CoMethSingleRegion",
      "title": "Wrapper function to find contiguous and comethyalted sub-regions within a pre-defined genomic region",
      "topics": [
        "CoMethSingleRegion"
      ]
    },
    {
      "page": "CpGsInfoAllRegions",
      "title": "Test Associations Between Regions and Phenotype",
      "topics": [
        "CpGsInfoAllRegions"
      ]
    },
    {
      "page": "CpGsInfoOneRegion",
      "title": "Test Associations Between a Region and Phenotype",
      "topics": [
        "CpGsInfoOneRegion"
      ]
    },
    {
      "page": "CreateParallelWorkers",
      "title": "Create a Parallel Computing Cluster",
      "topics": [
        "CreateParallelWorkers"
      ]
    },
    {
      "page": "CreateRdrop",
      "title": "Computes leave-one-out correlations (rDrop) for each CpG",
      "topics": [
        "CreateRdrop"
      ]
    },
    {
      "page": "FindComethylatedRegions",
      "title": "Find Contiguous Co-Methylated Regions",
      "topics": [
        "FindComethylatedRegions"
      ]
    },
    {
      "page": "GetCpGsInRegion",
      "title": "Extract probe IDs for CpGs located in a genomic region",
      "topics": [
        "GetCpGsInRegion"
      ]
    },
    {
      "page": "GetResiduals",
      "title": "Get Linear Model Residuals",
      "topics": [
        "GetResiduals"
      ]
    },
    {
      "page": "ImportSesameData",
      "title": "Import Illumina manifests (sesameData versions)",
      "topics": [
        "ImportSesameData"
      ]
    },
    {
      "page": "lmmTest",
      "title": "Fit mixed model to methylation values in one genomic region",
      "topics": [
        "lmmTest"
      ]
    },
    {
      "page": "lmmTestAllRegions",
      "title": "Linear Mixed Models by Region",
      "topics": [
        "lmmTestAllRegions"
      ]
    },
    {
      "page": "MarkComethylatedCpGs",
      "title": "Mark CpGs in contiguous and co-methylated region",
      "topics": [
        "MarkComethylatedCpGs"
      ]
    },
    {
      "page": "MarkMissing",
      "title": "Return Column and Row Names of Samples and Probes under the Missingness Theshold",
      "topics": [
        "MarkMissing"
      ]
    },
    {
      "page": "NameRegion",
      "title": "Name a region with several CpGs based on its genomic location",
      "topics": [
        "NameRegion"
      ]
    },
    {
      "page": "OrderCpGsByLocation",
      "title": "Order CpGs by genomic location",
      "topics": [
        "OrderCpGsByLocation"
      ]
    },
    {
      "page": "pheno_df",
      "title": "Example phenotype data file from Prefrontal Cortex (PFC) Methylation Data of Alzheimer's Disease subjects",
      "topics": [
        "pheno_df"
      ]
    },
    {
      "page": "RegionsToRanges",
      "title": "Convert genomic regions in a data frame to GRanges format",
      "topics": [
        "RegionsToRanges"
      ]
    },
    {
      "page": "WriteCloseByAllRegions",
      "title": "Extract clusters of CpG probes located closely",
      "topics": [
        "WriteCloseByAllRegions"
      ]
    }
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      "title": "Genome-wide methylation analysis using coMethDMR via parallel computing",
      "author": "Gabriel J. Odom, Lissette Gomez, Tiago Chedraoui Silva, Lanyu Zhang and Lily Wang",
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        "Analyzing One Type of Genomic Region via BiocParallel",
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        "Testing the co-methylated regions",
        "coMethDMR Analysis Pipeline for 450k Methylation Arrays Datasets via BiocParallel",
        "A Comment on Using EPIC Methylation Arrays Datasets",
        "Additional Comments on Computational Time and Resources",
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        "Genomic regions tested in gene based pipeline",
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