# FlyMine Genomic Visualizations
Author: InterMine Team
Date: 2019-07-08

InterMine Team

#### 8 July 2019

# Contents

# 1 Introduction

InterMineR constitutes an R package that interacts with InterMine, a data warehouse framework, which provides the ability to access, retrieve and analyze rapidly a variety of biological data (Smith et al. 2012; Kalderimis et al. 2014).

In this tutorial we will use the functionality of the InterMineR package to retrieve various information about the genomic regions that contain the Drosophila melanogaster genes zen, eve and r.

Then, we will visualize this information using the Gviz package (Hahne and Ivanek 2016).

All query results and calculations are correct according to FlyMine release 46.1 (November 2018). Genomic coordinates and p-values are likely to change between database releases but the methods will remain the same.

    # load packages
    library(InterMineR)
    library(Gviz)

    # load FlyMine and HumanMine
    im.fly = initInterMine(listMines()["FlyMine"])

    # load templates
    templates.fly = getTemplates(im.fly)

    # load data models
    # model.fly = getModel(im.fly)  # temporarily removed

# 2 Retrieve information about zen, eve and r genes with the Template Queries of InterMineR

First, we will create a new query to retrieve gene identifiers and genomic information for zen, eve and r.

There are two ways to build a query in InterMineR. We can either build a query as a list object with `newQuery` function, and assign all input values (selection of retrieved data type, constraints, etc.) as items of that list:

    # Build new query to retrieve information for zen, eve, and r Drosophila genes

    # Get Gene types from FlyMine model
    # head(subset(model.fly, type == "Gene"), 3)  # temporarily removed

    gene_info.list = as.list(1:3)
    for(i in 1:3){

      gene = c("zen", "eve", "r")[i]

      # define new query
      queryGeneIds = newQuery()
      queryGeneIds

      # set name
      queryGeneIds$name = "Gene identifiers"

      # set columns
      queryGeneIds$select = c(
        "Gene.primaryIdentifier",
        "Gene.secondaryIdentifier",
        "Gene.symbol",
        "Gene.id",
        "Gene.chromosome.primaryIdentifier",
        "Gene.chromosomeLocation.start",
        "Gene.chromosomeLocation.end",
        "Gene.chromosomeLocation.strand"
      )

      # set sort order
      queryGeneIds$orderBy = list(
        c(Gene.secondaryIdentifier = "ASC")
      )

      # set constraints
      newConstraint1 = list(
        path = "Gene",
        op = "LOOKUP",
        value = gene,
        code = "A"
      )
      queryGeneIds$where = list(newConstraint1)

      # run query and store results
      gene_info.list[[i]] = runQuery(im = im.fly, qry = queryGeneIds)
    }

    # concatenate to data.frame
    gene_info.list = do.call(rbind, gene_info.list)

    # print dimensions
    print(dim(gene_info.list))

    ## [1] 3 8

Or we can build the query as an `InterMineR-class` object with the functions `setConstraint`, which allows us to generate a new or modify an existing list of constraints, and `setQuery`, which generates the query as a `InterMineR-class` object:

    # set constraints
    constraints = setConstraints(
      paths = "Gene" ,
      operators = "LOOKUP",
      values = list(c("zen", "eve", "r"))
    )

    # define new query
    queryGeneIds = setQuery(
      select = c(
        "Gene.primaryIdentifier",
        "Gene.secondaryIdentifier",
        "Gene.symbol",
        "Gene.id",
        "Gene.chromosome.primaryIdentifier",
        "Gene.chromosomeLocation.start",
        "Gene.chromosomeLocation.end",
        "Gene.chromosomeLocation.strand"
      ),
      where = constraints
    )

    # run query and store results
    gene_info = runQuery(im = im.fly,
                         qry = queryGeneIds)

    # print dimensions
    print(dim(gene_info))

    ## [1] 3 8

As it is demonstrated below, `setConstraints` and `setQuery` functions are designed to facilitate the generation of queries for InterMine instances and avoid using multiple iterative loops, especially when it is required to include multiple constraints or constraint values (e.g. genes, organisms) in your query.

    # compare the results from both type of queries
    all(gene_info == gene_info.list)

    ## [1] TRUE

    gene_info

    ##   Gene.primaryIdentifier Gene.secondaryIdentifier Gene.symbol Gene.id
    ## 1            FBgn0004053                   CG1046         zen 1007747
    ## 2            FBgn0000606                   CG2328         eve 1007235
    ## 3            FBgn0003189                  CG18572           r 1048944
    ##   Gene.chromosome.primaryIdentifier Gene.chromosomeLocation.start
    ## 1                                3R                       6752864
    ## 2                                2R                       9979319
    ## 3                                 X                      16655359
    ##   Gene.chromosomeLocation.end Gene.chromosomeLocation.strand
    ## 1                     6754194                             -1
    ## 2                     9980795                              1
    ## 3                    16668712                              1

Now that we have the identifiers and the genomic information for zen, eve and r (Genes of Interest, GOIs), we can use Template Queries to retrieve information about:

1. The exons that comprise our GOIs
2. The genes that are adjacent to our GOIs
3. The genes that overlap with our GOIs

    # Retrieve Template Queries:

    # Use Gene_ExonLocation2 template query to get the exons for the genes of interest
    queryExons = getTemplateQuery(
      im.fly,
      name = "Gene_ExonLocation2")

    # Use Gene_AdjacentGenes template query to get the adjacent genes
    queryGeneAdjacentGenes = getTemplateQuery(
      im = im.fly,
      name = "Gene_AdjacentGenesLocations"
    )

    # Use Gene_OverlapppingGenes template query to get the overlapping genes
    queryGeneOverlapppingGenes = getTemplateQuery(
      im.fly,
      name = "Gene_OverlapppingGenes"
    )

Having retrieved the necessary template queries, we iterate through our GOIs and retrieve the appropriate gene identifiers for each query.

These identifiers will be used to modify the constraints of each query before we run it.

    for(i in 1:3){

      gene = c("zen", "eve", "r")[i]

      # 1. Gene_ExonLocation2

      # set Gene.secondaryIdentifier value
      queryExons$where[[1]]$value = gene_info[which(gene_info$Gene.symbol == gene),2]

      # or alternatively use setConstraints function
      queryExons$where = setConstraints(
        modifyQueryConstraints = queryExons,
        m.index = 1,
        values = list(gene_info[which(gene_info$Gene.symbol == gene),2])
      )

      # run query and save results
      assign(
        x = paste0(gene,".Exons"),
        value = runQuery(im.fly, queryExons)
      )

      # 2. Gene_AdjacentGenes

      # change the value of the third constraint of the Gene_AdjacentGenes query with the
      # Gene.secondaryIdentifier of the genes of interest
      queryGeneAdjacentGenes$where[[3]]$value = gene_info$Gene.secondaryIdentifier[i]

      # or alternatively use setConstraints function
      queryGeneAdjacentGenes$where = setConstraints(
        modifyQueryConstraints = queryGeneAdjacentGenes,
        m.index = 3,
        values = list(gene_info$Gene.secondaryIdentifier[i])
      )

      # assign the adjacent gene information to each gene of interest
      assign(x = paste0(gene_info$Gene.symbol[i], "_AdjacentGenes"),
             value = runQuery(im.fly, queryGeneAdjacentGenes))

      if(is.null(get(paste0(gene_info$Gene.symbol[i], "_AdjacentGenes")))){
        print(paste0(gene_info$Gene.symbol[i], " query returns no adjacent genes"))
      }

      # 3. Gene_OverlapppingGenes

      queryGeneOverlapppingGenes$where[[2]]$value = gene_info$Gene.secondaryIdentifier[i]

      # or alternatively use setConstraints function
      queryGeneOverlapppingGenes$where = setConstraints(
        modifyQueryConstraints = queryGeneOverlapppingGenes,
        m.index = 2,
        values = list(gene_info$Gene.secondaryIdentifier[i])
      )

      assign(x = paste0(gene_info$Gene.symbol[i], "_OverlappingGenes"),
             value = runQuery(im.fly, queryGeneOverlapppingGenes))

      if(is.null(get(paste0(gene_info$Gene.symbol[i], "_OverlappingGenes")))){

        print(paste0(gene_info$Gene.symbol[i], " query returns no overlapping genes"))
      }

    }

    ## [1] "zen query returns no overlapping genes"
    ## [1] "eve query returns no adjacent genes"

    # show adjacent genes
    head(zen_AdjacentGenes, 3)

    ##   Gene.secondaryIdentifier Gene.symbol Gene.chromosome.primaryIdentifier
    ## 1                   CG1046         zen                                3R
    ##   Gene.chromosomeLocation.start Gene.chromosomeLocation.end
    ## 1                       6752864                     6754194
    ##   Gene.chromosomeLocation.strand
    ## 1                             -1
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.primaryIdentifier
    ## 1                                                     FBgn0085326
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.symbol
    ## 1                                              CG34297
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.chromosomeLocation.start
    ## 1                                                                6747068
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.chromosomeLocation.end
    ## 1                                                              6748007
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.chromosomeLocation.strand
    ## 1                                                                      -1
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.primaryIdentifier
    ## 1                                                   FBgn0000166
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.symbol
    ## 1                                                bcd
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.chromosomeLocation.start
    ## 1                                                              6755842
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.chromosomeLocation.end
    ## 1                                                            6759466
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.chromosomeLocation.strand
    ## 1                                                                    -1

    head(r_AdjacentGenes, 3)

    ##   Gene.secondaryIdentifier Gene.symbol Gene.chromosome.primaryIdentifier
    ## 1                  CG18572           r                                 X
    ##   Gene.chromosomeLocation.start Gene.chromosomeLocation.end
    ## 1                      16655359                    16668712
    ##   Gene.chromosomeLocation.strand
    ## 1                              1
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.primaryIdentifier
    ## 1                                                     FBgn0030769
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.symbol
    ## 1                                              CG13012
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.chromosomeLocation.start
    ## 1                                                               16668818
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.chromosomeLocation.end
    ## 1                                                             16676753
    ##   Gene.downstreamIntergenicRegion.adjacentGenes.chromosomeLocation.strand
    ## 1                                                                      -1
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.primaryIdentifier
    ## 1                                                   FBgn0030768
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.symbol
    ## 1                                             CG9723
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.chromosomeLocation.start
    ## 1                                                             16653220
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.chromosomeLocation.end
    ## 1                                                           16655015
    ##   Gene.upstreamIntergenicRegion.adjacentGenes.chromosomeLocation.strand
    ## 1                                                                    -1

    # show overlapping genes
    head(eve_OverlappingGenes, 3)

    ##   Gene.secondaryIdentifier Gene.symbol Gene.chromosome.primaryIdentifier
    ## 1                   CG2328         eve                                2R
    ##   Gene.chromosomeLocation.start Gene.chromosomeLocation.end
    ## 1                       9979319                     9980795
    ##   Gene.chromosomeLocation.strand Gene.overlappingFeatures.primaryIdentifier
    ## 1                              1                                FBgn0264599
    ##   Gene.overlappingFeatures.symbol
    ## 1                   asRNA:CR43948
    ##   Gene.overlappingFeatures.chromosomeLocation.start
    ## 1                                           9976996
    ##   Gene.overlappingFeatures.chromosomeLocation.end
    ## 1                                         9979584
    ##   Gene.overlappingFeatures.chromosomeLocation.strand
    ## 1                                                 -1

    head(r_OverlappingGenes, 3)

    ##   Gene.secondaryIdentifier Gene.symbol Gene.chromosome.primaryIdentifier
    ## 1                  CG18572           r                                 X
    ##   Gene.chromosomeLocation.start Gene.chromosomeLocation.end
    ## 1                      16655359                    16668712
    ##   Gene.chromosomeLocation.strand Gene.overlappingFeatures.primaryIdentifier
    ## 1                              1                                FBgn0015336
    ##   Gene.overlappingFeatures.symbol
    ## 1                         CG15865
    ##   Gene.overlappingFeatures.chromosomeLocation.start
    ## 1                                          16660898
    ##   Gene.overlappingFeatures.chromosomeLocation.end
    ## 1                                        16664455
    ##   Gene.overlappingFeatures.chromosomeLocation.strand
    ## 1                                                 -1

Only the queries about the r gene return both adjacent and overlapping genes.

The queries for genes zen and eve return only the adjacent and the overlapping genes respectively.

# 3 Defining Genomic Regions of Interest that contain zen, eve and r, along with their respective adjacent and/or overlapping genes.

It is time to define the Genomic Regions of Interest (ROIs) for which we will retrieve even more features.

To do so we will add 1000 bases in both sides of GOIs, taking also into consideration the start and the end of the adjacent and/or the overlapping genes.

    # get genomic region of zen, eve, r and their respective
    # adjacent and/or overlapping genes.
    # Add 1000 bp on both sides of this region!

    # chromosome region of interest (ROI) containing zen and adjacent genes
    zen.ROI.start = min(
      as.numeric(zen_AdjacentGenes[,grep("start", colnames(zen_AdjacentGenes))])
      ) - 1000
    zen.ROI.end = max(
      as.numeric(zen_AdjacentGenes[,grep("end", colnames(zen_AdjacentGenes))])
      ) + 1000

    # chromosome region of interest (ROI) containing eve and overlapping genes
    eve.ROI.start = min(
      as.numeric(eve_OverlappingGenes[,grep("start", colnames(eve_OverlappingGenes))])
      ) - 1000
    eve.ROI.end = max(
      as.numeric(eve_OverlappingGenes[,grep("end", colnames(eve_OverlappingGenes))])
      ) + 1000

    # chromosome region of interest (ROI) containing r, adjacent and overlapping genes
    r.ROI.start = min(
      as.numeric(
        c(r_OverlappingGenes[,grep("start", colnames(r_OverlappingGenes))],
          r_AdjacentGenes[,grep("start", colnames(r_AdjacentGenes))])
      )
    ) - 1000

    r.ROI.end = max(
      as.numeric(
        c(r_OverlappingGenes[,grep("end", colnames(r_OverlappingGenes))],
          r_AdjacentGenes[,grep("end", colnames(r_AdjacentGenes))])
      )
    ) + 1000

# 4 Retrieve information about the Genomic Regions of Interest with the Template Queries of InterMineR

With our ROIs well-defined, it is time to retrieve specifically:

1. the Transcription Factor (TF) binding sites, retrieved by REDfly database, as well as
2. all Regulatory Regions (RRs) for each of them.

Template queries ChromLocation_TFBindingSiteLocationGeneFactor and ChromLocation_RegulatoryRegion will be used for this purpose.

    # find all transcription factor (TF) binding sites within the ROIs
    # by using the ChromLocation_TFBindingSiteLocationGeneFactor template query
    queryTFBindingSites = getTemplateQuery(
      im.fly,
      "ChromLocation_TFBindingSiteLocationGeneFactor"
    )

    # find all Regulatory Regions (RRs) within the ROIs
    # by using the ChromLocation_RegulatoryRegion template query
    queryRRLocations = getTemplateQuery(
      im.fly, "ChromLocation_RegulatoryRegion"
    )

As before, the appropriate gene identifiers will be used to modify the constraints of our queries before we run them.

    for(i in 1:3){

      gene = c("zen", "eve", "r")[i]

      # 1. ChromLocation_TFBindingSiteLocationGeneFactor

      # set chromosome value
      queryTFBindingSites$where[[3]]$value = gene_info[which(gene_info$Gene.symbol == gene),5]

      # set location start
      queryTFBindingSites$where[[4]]$value = as.character(get(paste0(gene,".ROI.start")))

      # set location end
      queryTFBindingSites$where[[5]]$value = as.character(get(paste0(gene,".ROI.end")))

      # or alternatively use setConstraints function
      queryTFBindingSites$where = setConstraints(
        modifyQueryConstraints = queryTFBindingSites,
        m.index = 3:5,
        values = list(
          # set chromosome value
          gene_info[which(gene_info$Gene.symbol == gene),5],
          # set location start
          as.character(get(paste0(gene,".ROI.start"))),
          # set location end
          as.character(get(paste0(gene,".ROI.end")))
          )
      )

      # run query and save results
      assign(
        x = paste0(gene, ".ROI.TFBindingSites"),
        value = runQuery(im.fly, queryTFBindingSites)
      )

      if(is.null(get(paste0(gene, ".ROI.TFBindingSites")))){

        print(paste0(gene, " ROI query returns no TF binding sites from REDfly database"))
      }

      # 2. ChromLocation_RegulatoryRegion

      # set chromosome value
      queryRRLocations$where[[1]]$value = gene_info[which(gene_info$Gene.symbol == gene),5]

      # set location start
      queryRRLocations$where[[2]]$value = as.character(get(paste0(gene,".ROI.start")))

      # set location end
      queryRRLocations$where[[3]]$value = as.character(get(paste0(gene,".ROI.end")))

      # or alternatively use setConstraints function
      queryRRLocations$where = setConstraints(
        modifyQueryConstraints = queryRRLocations,
        m.index = 1:3,
        values = list(
          # set chromosome value
          gene_info[which(gene_info$Gene.symbol == gene),5],
          # set location start
          as.character(get(paste0(gene,".ROI.start"))),
          # set location end
          as.character(get(paste0(gene,".ROI.end")))
        )
      )

      # run query and save results
      assign(
        x = paste0(gene, ".ROI.RRLocations"),
        value = runQuery(im.fly, queryRRLocations)
      )

      if(is.null(get(paste0(gene, ".ROI.RRLocations")))){

        print(paste0(gene, " ROI query returns no RRs"))
      }

    }

    ## [1] "r ROI query returns no TF binding sites from REDfly database"
    ## [1] "r ROI query returns no RRs"

    head(zen.ROI.TFBindingSites, 3)

    ##   TFBindingSite.chromosome.primaryIdentifier
    ## 1                                         3R
    ## 2                                         3R
    ## 3                                         3R
    ##   TFBindingSite.chromosomeLocation.start
    ## 1                                6754236
    ## 2                                6754261
    ## 3                                6754323
    ##   TFBindingSite.chromosomeLocation.end TFBindingSite.primaryIdentifier
    ## 1                              6754248                        TF000822
    ## 2                              6754273                        TF000823
    ## 3                              6754350                        TF000824
    ##   TFBindingSite.gene.primaryIdentifier TFBindingSite.gene.name
    ## 1                          FBgn0004053               zerknullt
    ## 2                          FBgn0004053               zerknullt
    ## 3                          FBgn0004053               zerknullt
    ##   TFBindingSite.factor.primaryIdentifier
    ## 1                            FBgn0031436
    ## 2                            FBgn0031436
    ## 3                            FBgn0031436
    ##                       TFBindingSite.factor.name
    ## 1 NADH dehydrogenase (ubiquinone) B17.2 subunit
    ## 2 NADH dehydrogenase (ubiquinone) B17.2 subunit
    ## 3 NADH dehydrogenase (ubiquinone) B17.2 subunit
    ##   TFBindingSite.gene.regulatoryRegions.dataSets.dataSource.name
    ## 1                                                        REDfly
    ## 2                                                        REDfly
    ## 3                                                        REDfly

    head(eve.ROI.TFBindingSites, 3)

    ##   TFBindingSite.chromosome.primaryIdentifier
    ## 1                                         2R
    ## 2                                         2R
    ## 3                                         2R
    ##   TFBindingSite.chromosomeLocation.start
    ## 1                                9975998
    ## 2                                9977715
    ## 3                                9977725
    ##   TFBindingSite.chromosomeLocation.end TFBindingSite.primaryIdentifier
    ## 1                              9976007                        TF000353
    ## 2                              9977724                        TF000354
    ## 3                              9978295                        TF002028
    ##   TFBindingSite.gene.primaryIdentifier TFBindingSite.gene.name
    ## 1                          FBgn0000606            even skipped
    ## 2                          FBgn0000606            even skipped
    ## 3                          FBgn0000606            even skipped
    ##   TFBindingSite.factor.primaryIdentifier TFBindingSite.factor.name
    ## 1                            FBgn0001180                 hunchback
    ## 2                            FBgn0001325                   Kruppel
    ## 3                            FBgn0086134 Proteasome alpha2 subunit
    ##   TFBindingSite.gene.regulatoryRegions.dataSets.dataSource.name
    ## 1                                                        REDfly
    ## 2                                                        REDfly
    ## 3                                                        REDfly

    head(r.ROI.TFBindingSites, 3)

    ## NULL

    head(zen.ROI.RRLocations, 3)

    ##   RegulatoryRegion.chromosome.primaryIdentifier
    ## 1                                            3R
    ## 2                                            3R
    ## 3                                            3R
    ##   RegulatoryRegion.chromosomeLocation.start
    ## 1                                   6749892
    ## 2                                   6754137
    ## 3                                   6754137
    ##   RegulatoryRegion.chromosomeLocation.end RegulatoryRegion.primaryIdentifier
    ## 1                                 6752933                Unspecified_VT37508
    ## 2                                 6754240                     FBsf0000435771
    ## 3                                 6754240                           zen_0.04
    ##                             RegulatoryRegion.dataSets.name
    ## 1 REDfly Drosophila transcriptional cis-regulatory modules
    ## 2             FlyBase data set for Drosophila melanogaster
    ## 3 REDfly Drosophila transcriptional cis-regulatory modules

    head(eve.ROI.RRLocations, 3)

    ##   RegulatoryRegion.chromosome.primaryIdentifier
    ## 1                                            2R
    ## 2                                            2R
    ## 3                                            2R
    ##   RegulatoryRegion.chromosomeLocation.start
    ## 1                                   9975998
    ## 2                                   9976181
    ## 3                                   9976184
    ##   RegulatoryRegion.chromosomeLocation.end RegulatoryRegion.primaryIdentifier
    ## 1                                 9976007                           TF000353
    ## 2                                 9979300                        eve_GMR9E02
    ## 3                                 9977089                           eve_3130
    ##                             RegulatoryRegion.dataSets.name
    ## 1     REDfly Drosophila transcription factor binding sites
    ## 2 REDfly Drosophila transcriptional cis-regulatory modules
    ## 3 REDfly Drosophila transcriptional cis-regulatory modules

    head(r.ROI.RRLocations, 3)

    ## NULL

# 5 Retrieve information about the Genomic Regions of Interest with the Gviz::UcscTrack function

Finally, the Gviz::UcscTrack function allows us to retrieve extra information about the CpG islands, the Conservation score, and GC content of our ROIs.

    for(i in 1:3){
      gene = c("zen", "eve", "r")[i]

      # set chromosome value
      chrom = paste0("chr",gene_info[which(gene_info$Gene.symbol == gene),5])

      # set the beginning and the end of the ROI
      gene.start = get(paste0(gene, ".ROI.start"))
      gene.end = get(paste0(gene, ".ROI.end"))

      # get CpG islands
      assign(
        x = paste0(gene, ".ROI.cpgIslands"),
        value = UcscTrack(genome = "dm6", chromosome = chrom,
                          track = "cpgIslandExt",
                          from = gene.start,
                          to = gene.end,
                          trackType = "AnnotationTrack",
                          start = "chromStart",
                          end = "chromEnd",
                          id = "name",
                          shape = "box",
                          fill = "lightgreen",
                          name = "CpGs")
      )

      # get Conservation
      assign(
        x = paste0(gene, ".ROI.conservation"),
        value = UcscTrack(genome = "dm6", chromosome = chrom,
                          track = "Conservation",
                          table = "phyloP27way",
                          from = gene.start,
                          to = gene.end,
                          trackType = "DataTrack",
                          start = "start",
                          end = "end",
                          data = "score",
                          type = "hist",
                          window = "auto",
                          col.histogram = "darkblue",
                          fill.histogram = "darkblue",
                          name = "Cons")
      )
      # get GC Percent
      assign(
        x = paste0(gene, ".ROI.gcContent"),
        value =  UcscTrack(genome = "dm6", chromosome = chrom,
                           track = "GC Percent",
                           table = "gc5BaseBw",
                           from = gene.start,
                           to = gene.end,
                           trackType = "DataTrack", start = "start",
                           end = "end", data = "score", type = "hist",
                           window = "auto",
                           windowSize = 1500, fill.histogram = "#800080",
                           col.histogram = "#800080",
                           name = "GC%")
      )
    }

# 6 Visualize all information about the Genomic Regions of Interest

At this point, we are ready visualize all the information that we retrieved for the ROIs using Gviz package.

    # Plot all features for the genes of interest
    axTrack <- GenomeAxisTrack()

## 6.1 Genomic Region of Interest containing zen

    # zen gene
    idxTrack <- IdeogramTrack(genome = "dm6", chromosome = "chr3R")

    # get Exons for zen adjacent genes
    queryExons$where[[1]]$value = zen_AdjacentGenes$Gene.downstreamIntergenicRegion.adjacentGenes.symbol
    zen.down.Exons = runQuery(im.fly, queryExons)

    queryExons$where[[1]]$value = zen_AdjacentGenes$Gene.upstreamIntergenicRegion.adjacentGenes.symbol
    zen.up.Exons = runQuery(im.fly, queryExons)

    # get strand for zen ROI genes
    zen.strand = gsub(
      pattern = "-1",
      replacement = "-",
      x = c(
        zen.Exons$Gene.exons.chromosomeLocation.strand
      )
    )

    zen.adjacent.strand = gsub(
      pattern = "-1",
      replacement = "-",
      x = c(
        zen.down.Exons$Gene.exons.chromosomeLocation.strand,
        zen.up.Exons$Gene.exons.chromosomeLocation.strand
      )
    )

    # index for zen gene_info
    ind.gi = which(gene_info$Gene.symbol == "zen")

    # zen data.frame for GeneRegionTrack
    zenTrack = data.frame(
      chromosome = "chr3R",
      start = as.numeric(c(
        zen.Exons$Gene.exons.chromosomeLocation.start
      )),
      end = as.numeric(c(
        zen.Exons$Gene.exons.chromosomeLocation.end
      )),
      strand = zen.strand,
      feature = "protein-coding",
      gene = gene_info[ind.gi,1],
      exon = c(
        zen.Exons$Gene.exons.primaryIdentifier
      ),
      transcript = "zen"
    )

    zenTrack <- GeneRegionTrack(zenTrack,
                                genome = "dm6",
                                chromosome = "chr3R",
                                name = "zen",
                                background.title = "brown",
                                transcriptAnnotation = "transcript"
    )

    # zen Adjacent genes data.frame for GeneRegionTrack
    zenAdjacentTrack = data.frame(
      chromosome = "chr3R",
      start = as.numeric(c(
        zen.down.Exons$Gene.exons.chromosomeLocation.start,
        zen.up.Exons$Gene.exons.chromosomeLocation.start
      )),
      end = as.numeric(c(
        zen.down.Exons$Gene.exons.chromosomeLocation.end,
        zen.up.Exons$Gene.exons.chromosomeLocation.end
      )),
      strand = zen.adjacent.strand,
      exon = c(
        zen.down.Exons$Gene.exons.primaryIdentifier,
        zen.up.Exons$Gene.exons.primaryIdentifier
      ),
      transcript = c(
        rep(zen_AdjacentGenes$Gene.downstreamIntergenicRegion.adjacentGenes.symbol,nrow(zen.down.Exons)),
        rep(zen_AdjacentGenes$Gene.upstreamIntergenicRegion.adjacentGenes.symbol, nrow(zen.up.Exons))
      )
    )

    zenAdjacentTrack <- GeneRegionTrack(zenAdjacentTrack,
                                genome = "dm6",
                                chromosome = "chr3R",
                                name = "zen Adjacent Genes",
                                transcriptAnnotation = "transcript",
                                background.title = "brown"
                                )

    # zen ROI TFbinding sites for GeneRegionTrack

    zen.ROI.TFBindingSites.track = data.frame(
      chromosome = paste0("chr",zen.ROI.TFBindingSites$TFBindingSite.chromosome.primaryIdentifier),
      start = as.numeric(zen.ROI.TFBindingSites$TFBindingSite.chromosomeLocation.start),
      end = as.numeric(zen.ROI.TFBindingSites$TFBindingSite.chromosomeLocation.end),
      symbol = zen.ROI.TFBindingSites$TFBindingSite.factor.name
    )

    zen.ROI.TFBindingSites.track = GeneRegionTrack(
      zen.ROI.TFBindingSites.track,
      genome = "dm6",
      chromosome = "chr3R",
      name = "TFs",
      background.title = "darkgreen",
      fill = "salmon"
    )

    # zen ROI Regulatory Regions for GeneRegionTrack

    zen.ROI.RRLocations.track = data.frame(
      chromosome = paste0("chr",zen.ROI.RRLocations$RegulatoryRegion.chromosome.primaryIdentifier),
      start = as.numeric(zen.ROI.RRLocations$RegulatoryRegion.chromosomeLocation.start),
      end = as.numeric(zen.ROI.RRLocations$RegulatoryRegion.chromosomeLocation.end),
      symbol = zen.ROI.RRLocations$RegulatoryRegion.primaryIdentifier
    )

    zen.ROI.RRLocations.track = GeneRegionTrack(
      zen.ROI.RRLocations.track,
      genome = "dm6",
      chromosome = "chr3R",
      name = "Regulatory Regions",
      background.title = "darkgreen",
      fill = "lightblue"
    )

    plotTracks(list(idxTrack,
                    axTrack,
                    zenTrack,
                    zenAdjacentTrack,
                    zen.ROI.TFBindingSites.track,
                    zen.ROI.RRLocations.track,
                    zen.ROI.cpgIslands,
                    zen.ROI.conservation,
                    zen.ROI.gcContent),
               showTitle = T,
               shape = "arrow")

## 6.2 Genomic Region of Interest containing eve

    # eve gene
    idxTrack <- IdeogramTrack(genome = "dm6", chromosome = "chr2R")

    # get Exons for eve overlapping genes
    queryExons$where[[1]]$value = eve_OverlappingGenes$Gene.overlappingFeatures.symbol
    eve.over.Exons = runQuery(im.fly, queryExons)

    # get strand for eve ROI genes
    eve.strand = gsub(
      pattern = "1",
      replacement = "+",
      x = c(
        eve.Exons$Gene.exons.chromosomeLocation.strand
      )
    )

    eve.over.strand = gsub(
      pattern = "-1",
      replacement = "-",
      x = c(
        eve.over.Exons$Gene.exons.chromosomeLocation.strand
      )
    )

    # index for eve gene_info
    ind.gi = which(gene_info$Gene.symbol == "eve")

    # eve data.frame for GeneRegionTrack
    eveTrack = data.frame(
      chromosome = "chr2R",
      start = as.numeric(c(
        eve.Exons$Gene.exons.chromosomeLocation.start
      )),
      end = as.numeric(c(
        eve.Exons$Gene.exons.chromosomeLocation.end
      )),
      strand = eve.strand,
      feature = "protein-coding",
      gene = gene_info[ind.gi,1],
      exon = c(
        eve.Exons$Gene.exons.primaryIdentifier
      ),
      transcript = "eve"
    )

    eveTrack <- GeneRegionTrack(eveTrack,
                                genome = "dm6",
                                chromosome = "chr2R",
                                name = "eve",
                                background.title = "brown",
                                transcriptAnnotation = "transcript"
    )

    # eve Adjacent genes data.frame for GeneRegionTrack
    eveOverlapTrack = data.frame(
      chromosome = "chr2R",
      start = as.numeric(c(
        eve.over.Exons$Gene.exons.chromosomeLocation.start
      )),
      end = as.numeric(c(
        eve.over.Exons$Gene.exons.chromosomeLocation.end
      )),
      strand = eve.over.strand,
      exon = c(
        eve.over.Exons$Gene.exons.primaryIdentifier
      ),
      transcript = c(
        eve_OverlappingGenes$Gene.overlappingFeatures.symbol
      )
    )

    eveOverlapTrack <- GeneRegionTrack(eveOverlapTrack,
                                        genome = "dm6",
                                        chromosome = "chr2R",
                                        name = "eve Overlapping Genes",
                                        transcriptAnnotation = "transcript",
                                        background.title = "brown"
    )

    # eve ROI TFbinding sites for GeneRegionTrack

    eve.ROI.TFBindingSites.track = data.frame(
      chromosome = paste0("chr",eve.ROI.TFBindingSites$TFBindingSite.chromosome.primaryIdentifier),
      start = as.numeric(eve.ROI.TFBindingSites$TFBindingSite.chromosomeLocation.start),
      end = as.numeric(eve.ROI.TFBindingSites$TFBindingSite.chromosomeLocation.end),
      symbol = eve.ROI.TFBindingSites$TFBindingSite.factor.name
    )

    eve.ROI.TFBindingSites.track = GeneRegionTrack(
      eve.ROI.TFBindingSites.track,
      genome = "dm6",
      chromosome = "chr2R",
      name = "TFs",
      background.title = "darkgreen",
      fill = "salmon"
    )

    # eve ROI Regulatory Regions for GeneRegionTrack

    eve.ROI.RRLocations.track = data.frame(
      chromosome = paste0("chr",eve.ROI.RRLocations$RegulatoryRegion.chromosome.primaryIdentifier),
      start = as.numeric(eve.ROI.RRLocations$RegulatoryRegion.chromosomeLocation.start),
      end = as.numeric(eve.ROI.RRLocations$RegulatoryRegion.chromosomeLocation.end),
      symbol = eve.ROI.RRLocations$RegulatoryRegion.primaryIdentifier
    )

    eve.ROI.RRLocations.track = GeneRegionTrack(
      eve.ROI.RRLocations.track,
      genome = "dm6",
      chromosome = "chr2R",
      name = "Regulatory Regions",
      background.title = "darkgreen",
      fill = "lightblue"
    )

    plotTracks(list(idxTrack,
                    axTrack,
                    eveTrack,
                    eveOverlapTrack,
                    eve.ROI.TFBindingSites.track,
                    eve.ROI.RRLocations.track,
                    eve.ROI.cpgIslands,
                    eve.ROI.conservation,
                    eve.ROI.gcContent),
               showTitle = T,
               shape = "arrow")

## 6.3 Genomic Region of Interest containing r

    # r gene
    idxTrack <- IdeogramTrack(genome = "dm6", chromosome = "chrX")

    # get Exons for r adjacent genes
    queryExons$where[[1]]$value = r_AdjacentGenes$Gene.downstreamIntergenicRegion.adjacentGenes.symbol
    r.down.Exons = runQuery(im.fly, queryExons)

    queryExons$where[[1]]$value = r_AdjacentGenes$Gene.upstreamIntergenicRegion.adjacentGenes.symbol
    r.up.Exons = runQuery(im.fly, queryExons)

    # get Exons for r adjacent genes
    queryExons$where[[1]]$value = r_OverlappingGenes$Gene.overlappingFeatures.symbol
    r.over.Exons = runQuery(im.fly, queryExons)

    # get strand for r ROI genes
    r.strand = gsub(
      pattern = "1",
      replacement = "+",
      x = c(
        r.Exons$Gene.exons.chromosomeLocation.strand
      )
    )

    r.adjacent.strand = gsub(
      pattern = "-1",
      replacement = "-",
      x = c(
        r.down.Exons$Gene.exons.chromosomeLocation.strand,
        r.up.Exons$Gene.exons.chromosomeLocation.strand
      )
    )

    r.over.strand = gsub(
      pattern = "-1",
      replacement = "-",
      x = c(
        r.over.Exons$Gene.exons.chromosomeLocation.strand
      )
    )

    # index for r gene_info
    ind.gi = which(gene_info$Gene.symbol == "r")

    # r data.frame for GeneRegionTrack
    rTrack = data.frame(
      chromosome = "chrX",
      start = as.numeric(c(
        r.Exons$Gene.exons.chromosomeLocation.start
      )),
      end = as.numeric(c(
        r.Exons$Gene.exons.chromosomeLocation.end
      )),
      strand = r.strand,
      feature = "protein-coding",
      gene = gene_info[ind.gi,1],
      exon = c(
        r.Exons$Gene.exons.primaryIdentifier
      ),
      transcript = "r"
    )

    rTrack <- GeneRegionTrack(rTrack,
                                genome = "dm6",
                                chromosome = "chrX",
                                name = "r",
                                background.title = "brown",
                                transcriptAnnotation = "transcript"
    )

    # r Adjacent genes data.frame for GeneRegionTrack
    rAdjacentTrack = data.frame(
      chromosome = "chrX",
      start = as.numeric(c(
        r.down.Exons$Gene.exons.chromosomeLocation.start,
        r.up.Exons$Gene.exons.chromosomeLocation.start
      )),
      end = as.numeric(c(
        r.down.Exons$Gene.exons.chromosomeLocation.end,
        r.up.Exons$Gene.exons.chromosomeLocation.end
      )),
      strand = r.adjacent.strand,
      exon = c(
        r.down.Exons$Gene.exons.primaryIdentifier,
        r.up.Exons$Gene.exons.primaryIdentifier
      ),
      transcript = c(
        rep(r_AdjacentGenes$Gene.downstreamIntergenicRegion.adjacentGenes.symbol,nrow(r.down.Exons)),
        rep(r_AdjacentGenes$Gene.upstreamIntergenicRegion.adjacentGenes.symbol, nrow(r.up.Exons))
      )
    )

    rAdjacentTrack <- GeneRegionTrack(rAdjacentTrack,
                                        genome = "dm6",
                                        chromosome = "chrX",
                                        name = "r Adjacent Genes",
                                        transcriptAnnotation = "transcript",
                                        background.title = "brown"
    )

    # r Overlapping genes data.frame for GeneRegionTrack
    rOverTrack = data.frame(
      chromosome = "chrX",
      start = as.numeric(c(
        r.over.Exons$Gene.exons.chromosomeLocation.start
      )),
      end = as.numeric(c(
        r.over.Exons$Gene.exons.chromosomeLocation.end
      )),
      strand = r.over.strand,
      exon = c(
        r.over.Exons$Gene.exons.primaryIdentifier
      ),
      transcript = c(
        r_OverlappingGenes$Gene.overlappingFeatures.symbol
      )
    )

    rOverTrack <- GeneRegionTrack(rOverTrack,
                                      genome = "dm6",
                                      chromosome = "chrX",
                                      name = "r Overlapping Genes",
                                      transcriptAnnotation = "transcript",
                                      background.title = "brown"
    )

    # r ROI TFbinding sites for GeneRegionTrack
    is.null(r.ROI.TFBindingSites)

    # r ROI Regulatory Regions for GeneRegionTrack
    is.null(r.ROI.RRLocations)

    plotTracks(list(idxTrack,
                    axTrack,
                    rTrack,
                    rAdjacentTrack,
                    rOverTrack,
                    r.ROI.cpgIslands,
                    r.ROI.conservation,
                    r.ROI.gcContent),
               showTitle = T,
               shape = "arrow")

# 7 System info

    sessionInfo()

    ## R version 3.6.1 (2019-07-05)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 18.04.2 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=C                  LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ##  [1] grid      parallel  stats4    stats     graphics  grDevices utils
    ##  [8] datasets  methods   base
    ##
    ## other attached packages:
    ##  [1] Gviz_1.28.0          GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
    ##  [4] org.Hs.eg.db_3.8.2   GO.db_3.8.2          GeneAnswers_2.26.0
    ##  [7] RColorBrewer_1.1-2   Heatplus_2.30.0      MASS_7.3-51.4
    ## [10] RSQLite_2.1.1        annotate_1.62.0      XML_3.98-1.20
    ## [13] AnnotationDbi_1.46.0 IRanges_2.18.1       S4Vectors_0.22.0
    ## [16] Biobase_2.44.0       BiocGenerics_0.30.0  RCurl_1.95-4.12
    ## [19] bitops_1.0-6         igraph_1.2.4.1       InterMineR_1.6.1
    ## [22] BiocStyle_2.12.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] colorspace_1.4-1            biovizBase_1.32.0
    ##  [3] htmlTable_1.13.1            XVector_0.24.0
    ##  [5] base64enc_0.1-3             dichromat_2.0-0
    ##  [7] rstudioapi_0.10             bit64_0.9-7
    ##  [9] sqldf_0.4-11                xml2_1.2.0
    ## [11] splines_3.6.1               knitr_1.23
    ## [13] Formula_1.2-3               jsonlite_1.6
    ## [15] Rsamtools_2.0.0             cluster_2.1.0
    ## [17] graph_1.62.0                BiocManager_1.30.4
    ## [19] compiler_3.6.1              httr_1.4.0
    ## [21] backports_1.1.4             assertthat_0.2.1
    ## [23] Matrix_1.2-17               lazyeval_0.2.2
    ## [25] acepack_1.4.1               htmltools_0.3.6
    ## [27] prettyunits_1.0.2           tools_3.6.1
    ## [29] gtable_0.3.0                glue_1.3.1
    ## [31] GenomeInfoDbData_1.2.1      dplyr_0.8.3
    ## [33] Rcpp_1.0.1                  Biostrings_2.52.0
    ## [35] RJSONIO_1.3-1.2             rtracklayer_1.44.0
    ## [37] xfun_0.8                    stringr_1.4.0
    ## [39] proto_1.0.0                 ensembldb_2.8.0
    ## [41] zlibbioc_1.30.0             scales_1.0.0
    ## [43] BSgenome_1.52.0             VariantAnnotation_1.30.1
    ## [45] ProtGenerics_1.16.0         hms_0.4.2
    ## [47] SummarizedExperiment_1.14.0 RBGL_1.60.0
    ## [49] AnnotationFilter_1.8.0      yaml_2.2.0
    ## [51] curl_3.3                    memoise_1.1.0
    ## [53] gridExtra_2.3               ggplot2_3.2.0
    ## [55] downloader_0.4              biomaRt_2.40.1
    ## [57] rpart_4.1-15                latticeExtra_0.6-28
    ## [59] stringi_1.4.3               checkmate_1.9.4
    ## [61] GenomicFeatures_1.36.3      BiocParallel_1.18.0
    ## [63] chron_2.3-53                rlang_0.4.0
    ## [65] pkgconfig_2.0.2             matrixStats_0.54.0
    ## [67] evaluate_0.14               lattice_0.20-38
    ## [69] purrr_0.3.2                 GenomicAlignments_1.20.1
    ## [71] htmlwidgets_1.3             bit_1.1-14
    ## [73] tidyselect_0.2.5            magrittr_1.5
    ## [75] bookdown_0.11               R6_2.4.0
    ## [77] Hmisc_4.2-0                 DelayedArray_0.10.0
    ## [79] DBI_1.0.0                   gsubfn_0.7
    ## [81] pillar_1.4.2                foreign_0.8-71
    ## [83] survival_2.44-1.1           nnet_7.3-12
    ## [85] tibble_2.1.3                crayon_1.3.4
    ## [87] rmarkdown_1.13              progress_1.2.2
    ## [89] data.table_1.12.2           blob_1.1.1
    ## [91] digest_0.6.20               xtable_1.8-4
    ## [93] munsell_0.5.0

# References

Hahne, Florian, and Robert Ivanek. 2016. “Visualizing Genomic Data Using Gviz and Bioconductor.” In, 335–51. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3578-9_16.

Kalderimis, Alex, Rachel Lyne, Daniela Butano, Sergio Contrino, Mike Lyne, Joshua Heimbach, Fengyuan Hu, et al. 2014. “InterMine: extensive web services for modern biology.” Nucleic Acids Research 42 (Web Server issue). Oxford University Press:W468–72. https://doi.org/10.1093/nar/gku301.

Smith, R. N., J. Aleksic, D. Butano, A. Carr, S. Contrino, F. Hu, M. Lyne, et al. 2012. “InterMine: a flexible data warehouse system for the integration and analysis of heterogeneous biological data.” Bioinformatics 28 (23):3163–5. https://doi.org/10.1093/bioinformatics/bts577.