It is often of interest to display observed data in the context of genomic reference information. We’ll examine how to do this with the ESRRA binding data and Gviz.

First we load up relevant data and annotation packages along with Gviz.

library(ERBS)
library(Gviz)
library(Homo.sapiens)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene

Genes in the vicinity of ESRRA

How can we identify a slice of the human genome containing ESRRA and some neighboring genes? There are various approaches; we’ll start by obtaining the ENTREZ identifier.

library(Homo.sapiens)
eid = select(Homo.sapiens, keys="ESRRA", keytype="SYMBOL", columns="ENTREZID")
## 'select()' returned 1:1 mapping between keys and columns
eid
##   SYMBOL ENTREZID
## 1  ESRRA     2101

Now we obtain the addresses for the ESRRA gene body, collect addresses of neighboring genes, and bind in the symbols for these genes.

allg = genes(txdb)
esrraAddr = genes(txdb, vals=list(gene_id=2101)) # redundant...
esrraNeigh = subsetByOverlaps(allg, esrraAddr+500000)
esrraNeigh$symbol = mapIds(Homo.sapiens, keys=esrraNeigh$gene_id, keytype="ENTREZID",
  column="SYMBOL")
## 'select()' returned 1:1 mapping between keys and columns

A quick check on the task with Gviz:

plotTracks(GeneRegionTrack(esrraNeigh, showId=TRUE))

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The ESRRA binding peaks in this region

We obtain the ESRRA binding data for the GM12878 EBV-transformed B-cell and subset to events near our group of genes.

data(GM12878)
sc = subsetByOverlaps(GM12878, range(esrraNeigh))
sc
## GRanges object with 6 ranges and 7 metadata columns:
##       seqnames               ranges strand |      name     score       col
##          <Rle>            <IRanges>  <Rle> | <numeric> <integer> <logical>
##   [1]    chr11 [64071338, 64073242]      * |         3         0      <NA>
##   [2]    chr11 [63913452, 63914077]      * |        61         0      <NA>
##   [3]    chr11 [63741506, 63742754]      * |       210         0      <NA>
##   [4]    chr11 [63992515, 63994725]      * |       408         0      <NA>
##   [5]    chr11 [64098744, 64100083]      * |      1778         0      <NA>
##   [6]    chr11 [63639263, 63640021]      * |      2248         0      <NA>
##       signalValue    pValue    qValue      peak
##         <numeric> <numeric> <numeric> <integer>
##   [1]       69.87       310        32      1040
##   [2]       52.48    123.72  1.785156       259
##   [3]       18.24    59.652  2.022276       622
##   [4]        9.04    41.001  1.910095       990
##   [5]        9.03    19.758  2.075721       653
##   [6]        8.65    17.481  2.031517       436
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

Computing an ideogram to give context on the chromosome

This computation is slow.

idxTrack = IdeogramTrack(genome="hg19", chr="chr11")

Putting it all together

We start at the top with the ideogram to identify chromosome and region on chromosome to which we are zooming with observational and structural information.

plotTracks(list(idxTrack, GenomeAxisTrack(), 
   DataTrack(sc[,7], name="ESRRA peak values"), 
   GeneRegionTrack(esrraNeigh, showId=TRUE,
         name="genes near ESRRA"), GenomeAxisTrack()))

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