Contents

1 Introduction

There are two packages available in Bioconductor for visualizing genomic data: rtracklayer and Gviz. rtracklayer provides an interface to genome browsers and associated annotation tracks. Gviz plots data and annotation information along genomic coordinates. TrackViewer is a light-weighted visualization tool for generating neat figures for publication. It utilizes Gviz, is easy to use, and has a low memory and cpu consumption.

library(Gviz)
library(rtracklayer)
library(trackViewer)
extdata <- system.file("extdata", package="trackViewer",
                       mustWork=TRUE)
gr <- GRanges("chr11", IRanges(122929275, 122930122), strand="-")
fox2 <- importScore(file.path(extdata, "fox2.bed"), format="BED",
                    ranges=gr)
fox2$dat <- coverageGR(fox2$dat)

viewTracks(trackList(fox2), gr=gr, autoOptimizeStyle=TRUE, newpage=FALSE)

dt <- DataTrack(range=fox2$dat[strand(fox2$dat)=="-"] , 
                genome="hg19", type="hist", name="fox2", 
                window=-1, chromosome="chr11", 
                fill.histogram="black", col.histogram="NA",
                background.title="white",
                col.frame="white", col.axis="black",
                col="black", col.title="black")
plotTracks(dt, from=122929275, to=122930122, strand="-")
Plot data with **Gviz** and **trackViewer**. Note that **trackViewer** can generate similar figure as **Gviz** with several lines of simple codes.

Figure 1: Plot data with Gviz and trackViewer
Note that trackViewer can generate similar figure as Gviz with several lines of simple codes.

TrackViewer not only has the functionalities to plot the figures generated by Gviz, as shown in Figure above, but also provides additional plotting styles as shown in Figure below. The mimimalist design requires minimum input from users while retaining the flexibility to change output style easily.

gr <- GRanges("chr1", IRanges(c(1, 6, 10), c(3, 6, 12)), score=c(3, 4, 1))
dt <- DataTrack(range=gr, data="score", type="hist")
plotTracks(dt, from=2, to=11)
tr <- new("track", dat=gr, type="data", format="BED")
viewTracks(trackList(tr), chromosome="chr1", start=2, end=11)
Plot data with **Gviz** and **trackViewer**. Note that **trackViewer** is not only including more details but also showing all the data involved in the given range.

Figure 2: Plot data with Gviz and trackViewer
Note that trackViewer is not only including more details but also showing all the data involved in the given range.

It requires huge memory space to handle big wig files. To solve this problem, trackViewer rewrote the import function to import whole file first and parse it later when plot. trackViewer provides higher import speed (21 min vs. over 180 min) and acceptable memory cost (5.32G vs. over 10G) for a half giga wig file (GSM917672) comparing to Gviz.

2 Browse tracks

2.1 Steps of using

2.1.1 step1 import data

Function importScore is used to import BED, WIG, bedGraph or BigWig files. Function importBam is employed to import bam file. Here is the example.

library(trackViewer)
extdata <- system.file("extdata", package="trackViewer",
                       mustWork=TRUE)
repA <- importScore(file.path(extdata, "cpsf160.repA_-.wig"),
                    file.path(extdata, "cpsf160.repA_+.wig"),
                    format="WIG")
## because the wig file does not contain strand info, 
## we need to set it manually
strand(repA$dat) <- "-"
strand(repA$dat2) <- "+"

Function coverageGR could be used to calculate coverage after importing if needed.

fox2 <- importScore(file.path(extdata, "fox2.bed"), format="BED",
                    ranges=GRanges("chr11", IRanges(122929000, 122931000)))
dat <- coverageGR(fox2$dat)
## we can split the data by strand into two different track channels
## here we set the dat2 slot to save the negative strand info, 
## reverse order as previous. 
fox2$dat <- dat[strand(dat)=="+"]
fox2$dat2 <- dat[strand(dat)=="-"]

2.1.2 step2 build gene model

The gene model can be built for a given genomic range using geneModelFromTxdb function which uses TranscriptDb object as input.

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
gr <- GRanges("chr11", IRanges(122929275, 122930122), strand="-")
trs <- geneModelFromTxdb(TxDb.Hsapiens.UCSC.hg19.knownGene,
                         org.Hs.eg.db,
                         gr=gr)

2.1.3 step3 view the tracks

Use viewTracks function to plot data and annotation information along genomic coordinates. addGuideLine or addArrowMark can be used to highlight the peaks.

viewerStyle <- trackViewerStyle()
setTrackViewerStyleParam(viewerStyle, "margin", c(.1, .05, .02, .02))
vp <- viewTracks(trackList(repA, fox2, trs), 
                 gr=gr, viewerStyle=viewerStyle, 
                 autoOptimizeStyle=TRUE)
addGuideLine(c(122929767, 122929969), vp=vp)
addArrowMark(list(x=122929650, 
                  y=2), # 2 means track 2 from bottom.
             label="label",
             col="blue",
             vp=vp)