1 Introduction
2 Retrieve information about zen, eve and r genes with the Template Queries of InterMineR
3 Defining Genomic Regions of Interest that contain zen, eve and r, along with their respective adjacent and/or overlapping genes.
4 Retrieve information about the Genomic Regions of Interest with the Template Queries of InterMineR
5 Retrieve information about the Genomic Regions of Interest with the Gviz::UcscTrack function
6 Visualize all information about the Genomic Regions of Interest
7 System info
References

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 1007877
## 2            FBgn0000606                   CG2328         eve 1007357
## 3            FBgn0003189                  CG18572           r 1048913
##   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:

The exons that comprise our GOIs
The genes that are adjacent to our GOIs
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:

the Transcription Factor (TF) binding sites, retrieved by REDfly database, as well as
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"

head(zen.ROI.TFBindingSites, 3)

##   TFBindingSite.chromosome.primaryIdentifier
## 1                                         3R
## 2                                         3R
## 3                                         3R
##   TFBindingSite.chromosomeLocation.start TFBindingSite.chromosomeLocation.end
## 1                                6754236                              6754248
## 2                                6754261                              6754273
## 3                                6754323                              6754350
##   TFBindingSite.primaryIdentifier TFBindingSite.gene.primaryIdentifier
## 1                        TF000822                          FBgn0004053
## 2                        TF000823                          FBgn0004053
## 3                        TF000824                          FBgn0004053
##   TFBindingSite.gene.name TFBindingSite.factor.primaryIdentifier
## 1               zerknullt                            FBgn0011648
## 2               zerknullt                            FBgn0011648
## 3               zerknullt                            FBgn0011648
##   TFBindingSite.factor.name
## 1       Mothers against dpp
## 2       Mothers against dpp
## 3       Mothers against dpp
##   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 TFBindingSite.chromosomeLocation.end
## 1                                9975998                              9976007
## 2                                9977715                              9977724
## 3                                9977725                              9978295
##   TFBindingSite.primaryIdentifier TFBindingSite.gene.primaryIdentifier
## 1                        TF000353                          FBgn0000606
## 2                        TF000354                          FBgn0000606
## 3                        TF002028                          FBgn0000606
##   TFBindingSite.gene.name TFBindingSite.factor.primaryIdentifier
## 1            even skipped                            FBgn0001180
## 2            even skipped                            FBgn0001325
## 3            even skipped                            FBgn0039740
##                    TFBindingSite.factor.name
## 1                                  hunchback
## 2                                    Kruppel
## 3 Zinc-finger protein interacting with CP190
##   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
##   RegulatoryRegion.sequenceOntologyTerm.name
## 1                                        CRM
## 2                          regulatory_region
## 3                                        CRM

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
##   RegulatoryRegion.sequenceOntologyTerm.name
## 1                            TF_binding_site
## 2                                        CRM
## 3                                        CRM

head(r.ROI.RRLocations, 3)

##   RegulatoryRegion.chromosome.primaryIdentifier
## 1                                             X
##   RegulatoryRegion.chromosomeLocation.start
## 1                                  16665168
##   RegulatoryRegion.chromosomeLocation.end RegulatoryRegion.primaryIdentifier
## 1                                16665667                 Unspecified_HypI29
##                             RegulatoryRegion.dataSets.name
## 1 REDfly Drosophila transcriptional cis-regulatory modules
##   RegulatoryRegion.sequenceOntologyTerm.name
## 1                                        CRM

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 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-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_1.32.0          GenomicRanges_1.40.0 GenomeInfoDb_1.24.0 
##  [4] org.Hs.eg.db_3.10.0  GO.db_3.10.0         GeneAnswers_2.30.0  
##  [7] RColorBrewer_1.1-2   Heatplus_2.34.0      MASS_7.3-51.6       
## [10] RSQLite_2.2.0        annotate_1.66.0      XML_3.99-0.3        
## [13] AnnotationDbi_1.50.0 IRanges_2.22.0       S4Vectors_0.26.0    
## [16] Biobase_2.48.0       BiocGenerics_0.34.0  RCurl_1.98-1.2      
## [19] igraph_1.2.5         InterMineR_1.10.0    BiocStyle_2.16.0    
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_1.4-1            ellipsis_0.3.0             
##   [3] htmlTable_1.13.3            biovizBase_1.36.0          
##   [5] XVector_0.28.0              base64enc_0.1-3            
##   [7] dichromat_2.0-0             rstudioapi_0.11            
##   [9] bit64_0.9-7                 sqldf_0.4-11               
##  [11] xml2_1.3.2                  splines_4.0.0              
##  [13] knitr_1.28                  Formula_1.2-3              
##  [15] jsonlite_1.6.1              Rsamtools_2.4.0            
##  [17] cluster_2.1.0               dbplyr_1.4.3               
##  [19] png_0.1-7                   graph_1.66.0               
##  [21] BiocManager_1.30.10         compiler_4.0.0             
##  [23] httr_1.4.1                  backports_1.1.6            
##  [25] lazyeval_0.2.2              assertthat_0.2.1           
##  [27] Matrix_1.2-18               acepack_1.4.1              
##  [29] htmltools_0.4.0             prettyunits_1.1.1          
##  [31] tools_4.0.0                 gtable_0.3.0               
##  [33] glue_1.4.0                  GenomeInfoDbData_1.2.3     
##  [35] dplyr_0.8.5                 rappdirs_0.3.1             
##  [37] Rcpp_1.0.4.6                vctrs_0.2.4                
##  [39] Biostrings_2.56.0           RJSONIO_1.3-1.4            
##  [41] rtracklayer_1.48.0          xfun_0.13                  
##  [43] stringr_1.4.0               proto_1.0.0                
##  [45] lifecycle_0.2.0             ensembldb_2.12.0           
##  [47] zlibbioc_1.34.0             scales_1.1.0               
##  [49] BSgenome_1.56.0             VariantAnnotation_1.34.0   
##  [51] ProtGenerics_1.20.0         hms_0.5.3                  
##  [53] SummarizedExperiment_1.18.0 RBGL_1.64.0                
##  [55] AnnotationFilter_1.12.0     yaml_2.2.1                 
##  [57] curl_4.3                    gridExtra_2.3              
##  [59] memoise_1.1.0               ggplot2_3.3.0              
##  [61] downloader_0.4              biomaRt_2.44.0             
##  [63] rpart_4.1-15                latticeExtra_0.6-29        
##  [65] stringi_1.4.6               checkmate_2.0.0            
##  [67] GenomicFeatures_1.40.0      BiocParallel_1.22.0        
##  [69] chron_2.3-55                rlang_0.4.5                
##  [71] pkgconfig_2.0.3             matrixStats_0.56.0         
##  [73] bitops_1.0-6                evaluate_0.14              
##  [75] lattice_0.20-41             purrr_0.3.4                
##  [77] htmlwidgets_1.5.1           GenomicAlignments_1.24.0   
##  [79] bit_1.1-15.2                tidyselect_1.0.0           
##  [81] magrittr_1.5                bookdown_0.18              
##  [83] R6_2.4.1                    magick_2.3                 
##  [85] Hmisc_4.4-0                 DelayedArray_0.14.0        
##  [87] DBI_1.1.0                   gsubfn_0.7                 
##  [89] pillar_1.4.3                foreign_0.8-79             
##  [91] survival_3.1-12             nnet_7.3-14                
##  [93] tibble_3.0.1                crayon_1.3.4               
##  [95] BiocFileCache_1.12.0        rmarkdown_2.1              
##  [97] jpeg_0.1-8.1                progress_1.2.2             
##  [99] data.table_1.12.8           blob_1.2.1                 
## [101] digest_0.6.25               xtable_1.8-4               
## [103] openssl_1.4.1               munsell_0.5.0              
## [105] askpass_1.1

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.

FlyMine Genomic Visualizations

27 April 2020

Contents