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\title{The \textbf{rtracklayer} package}
\author{Michael Lawrence}

\begin{document}

\maketitle

<<initialize, echo=FALSE>>=
options(width=70)
@ 

\section{Introduction}

The \textbf{rtracklayer} package is an interface (or \emph{layer})
between \textbf{R} and genome browsers. Its main purpose is the
visualization of genomic annotation \emph{tracks}, whether generated
through experimental data analysis performed in R or loaded from an
external data source. The features of \textbf{rtracklayer} may be
divided into two categories: 1) the import/export of track data and 2)
the control and querying of external genome browser sessions and
views.

There are two basic track data structures in Bioconductor:
\Rclass{GRanges}, defined by the \Rpackage{GenomicRanges} package, 
and \Rclass{RangedData}, a more general data structure from the
\Rpackage{IRanges} package. For most purposes, \Rclass{GRanges} is
preferred in the context of \Rpackage{rtracklayer}; however,
\Rclass{RangedData} has some particular use cases. 

\textbf{rtracklayer} supports the import and export of tracks from and
to files in various formats, see Section~\ref{sec:import-export}. All
positions in a \Rclass{RangedData} or \Rclass{GRanges} should be
1-based, as in R itself.

The \textbf{rtracklayer} package currently interfaces with the
\textbf{UCSC} web-based genome browser. Other packages may provide
drivers for other genome browsers through a plugin system. With
\textbf{rtracklayer}, the user may start a genome browser session,
create and manipulate genomic views, and import/export tracks and
sequences to and from a browser. Please note that not all features are
necessarily supported by every browser interface.

The rest of this vignette will consist of a number of case
studies. First, we consider an experiment investigating microRNA
regulation of gene expression, where the microRNA target sites are the
primary genomic features of interest.

\section{Gene expression and microRNA target sites}

This section will demonstrate the features of
\textbf{rtracklayer} on a microarray dataset from a larger
experiment investigating the regulation of human stem cell
differentiation by microRNAs. The transcriptome of the cells was
measured before and after differentiation by HG-U133plus2 Affymetrix
GeneChip arrays. We begin our demonstration by constructing an
annotation dataset from the experimental data, and then illustrate the
use of the genome browser interface to display interesting genomic
regions in the UCSC browser.

\subsection{Creating a target site track}

For the analysis of the stem cell microarray data, we are interested
in the genomic regions corresponding to differentially expressed genes
that are known to be targeted by a microRNA. We will represent this
information as an annotation track, so that we may view it in the UCSC
genome browser.

\subsubsection{Constructing the \Rclass{GRanges}}

In preparation for creating the microRNA target track, we first used
\textbf{limma} to detect the differentially expressed
genes in the microarray experiment. The locations of the microRNA
target sites were obtained from MiRBase. The code below stores information
about the target sites on differentially expressed genes in
the \textit{data.frame} called \texttt{targets}, which can also be
obtained by entering \texttt{data(targets)} when \textbf{rtracklayer}
is loaded.

% 
<<rtl-init, results = hide>>=
library("humanStemCell")
data(fhesc)
library("genefilter")
filtFhesc <- nsFilter(fhesc)[[1]]
library("limma")
design <- model.matrix(~filtFhesc$Diff) 
hesclim <- lmFit(filtFhesc, design) 
hesceb <- eBayes(hesclim) 
tab <- topTable(hesceb, coef = 2, adjust.method = "BH", n = 7676) 
tab2 <- tab[(tab$logFC > 1) & (tab$adj.P.Val < 0.01),]
affyIDs <- tab2$ID
library("microRNA")
data(hsTargets)
library("hgu133plus2.db")
entrezIDs <- mappedRkeys(hgu133plus2ENTREZID[affyIDs])
library("org.Hs.eg.db")
mappedEntrezIDs <- entrezIDs[entrezIDs %in% mappedkeys(org.Hs.egENSEMBLTRANS)]
ensemblIDs <- mappedRkeys(org.Hs.egENSEMBLTRANS[mappedEntrezIDs])
targetMatches <- match(ensemblIDs, hsTargets$target, 0)
## same as data(targets)
targets <- hsTargets[targetMatches,]
@
% 

The following code creates the track from the \texttt{targets}
dataset:
% 
<<rtl-miRNA-track>>=
library(rtracklayer)
library(GenomicRanges)
## call data(targets) if skipping first block
head(targets)
targetRanges <- IRanges(targets$start, targets$end)
targetTrack <- with(targets, 
                    GRangesForUCSCGenome("hg18", paste("chr", chrom, sep = ""), 
                                         targetRanges, strand, name, target))
@
%
The \Rfunction{GRangesForUCSCGenome} function constructs a
\Rclass{GRanges} object for the named genome. The stand information,
the name of the microRNA and the Ensembl ID of the targeted transcript
are stored in the \Rclass{GRanges}. The chromosome for each site is
passed as the \texttt{chrom} argument. The chromosome names and
lengths for the genome are taken from the UCSC database and stored in
the \Rclass{GRanges} along with the genome identifier. We can retrieve
them as follows:
<<rtl-miRNA-track-seqinfo>>=
genome(targetTrack)
head(seqlengths(targetTrack))
@ 
%
While this extra information is not strictly needed to upload data to
UCSC, calling \Rfunction{GRangesForUCSCGenome} is an easy way to
formally associate interval data to a UCSC genome build. This ensures,
for example, that the data will always be uploaded to the correct
genome, regardless of browser state. It also immediately validates
whether the intervals fall within the bounds of the genome.

For cases where one is not interacting with the UCSC genome browser,
and in particular when network access is unavailable, the
\Rfunction{GRangesForBSGenome} function behaves the same, except it
finds an installed \Rpackage{BSGenome} package and loads it to
retrieve the chromosome information.

\subsubsection{Accessing track information}

The track information is now stored in the R session as a
\Rclass{GRanges} object. It holds the chromosme, start, end and strand
for each feature, along with any number of data columns.

The primary feature attributes are the \texttt{start}, \texttt{end},
\texttt{seqnames} and \texttt{strand}. There are accessors for each
of these, named accordingly. For example, the following code retrieves the
chromosome names and then start positions for each feature in the
track.
%
<<feature-data-accessors>>=
head(seqnames(targetTrack))
head(start(targetTrack))
@
%

\paragraph{Exercises}

\begin{enumerate}
\item Get the strand of each feature in the track
\item Calculate the length of each feature
\item Reconstruct (partially) the \texttt{targets} \Rclass{data.frame}
\end{enumerate}

<<sol-1, echo=FALSE, results=hide>>=
head(strand(targetTrack))
head(width(targetTrack))
data.frame(chrom = as.factor(seqnames(targetTrack)), 
           start = start(targetTrack), end = end(targetTrack),
           strand = as.factor(strand(targetTrack)))
@ 

\subsubsection{Subsetting a \textit{GRanges}}

It is often helpful to extract subsets from \Rclass{GRanges}
instances, especially when uploading to a genome browser.  The data
can be subset though a matrix-style syntax by feature and column.  The
conventional \texttt{[} method is employed for subsetting, where the
first parameter, \textit{i}, indexes the features and \textit{j}
indexes the data columns.  Both \textit{i} and \textit{j} may contain
numeric, logical and character indices, which behave as expected.
%
<<subset-features>>=
## get the first 10 targets
first10 <- targetTrack[1:10]
## get pos strand targets
posTargets <- targetTrack[strand(targetTrack) == "+"]
## get the targets on chr1
chr1Targets <- targetTrack[seqnames(targetTrack) == "chr1"]
@ 
%

\paragraph{Exercises}

\begin{enumerate}
\item Subset the track for all features on the negative strand of
  chromosome 2.
\end{enumerate}

<<sol-2, echo=FALSE, results=hide>>=
negChr2Targets <- targetTrack[strand(targetTrack) == "-" & 
                              seqnames(targetTrack) == "chr2"]
@ 


\subsubsection{Exporting and importing tracks}
\label{sec:import-export}

Import and export of \Rclass{GRanges} and \Rclass{RangedData}
instances is supported in the following formats: Browser Extended
Display (BED), versions 1, 2 and 3 of the General Feature Format
(GFF), and Wiggle (WIG). Support for additional formats may be
provided by other packages through a plugin system.

To save the microRNA target track created above in a format understood
by other tools, we could export it as BED. This is done with the
\texttt{export} function, which accepts a filename or any R connection
object as its target. If a target is not given, the serialized string
is returned. The desired format is derived, by default, from
the extension of the filename. Use the \texttt{format} parameter to
explicitly specify a format.
<<export, eval = FALSE>>=
export(targetTrack, "targets.bed")
@

To read the data back in a future session, we could use the
\texttt{import} function. The source
of the data may be given as a connection, a filename or a character
vector containing the data. Like the \texttt{export} function, the
format is determined from the filename, by default. 
<<import, eval = FALSE>>=
restoredTrack <- import("targets.bed")
@
%
The \Robject{restoredTrack} object is of class \Rclass{RangedData}. To
obtain a \Rclass{GRanges} object directly upon importing, pass
\Rcode{asRangedData = FALSE} to \Rfunction{import}:
<<import-gr, eval = FALSE>>=
restoredTrack <- import("targets.bed", asRangedData = FALSE)
@
%
Eventually, \Rfunction{import} will return \Rclass{GRanges} objects by
default; the extra parameter is necessary for backwards
compatibility. Thus, if a \Rclass{RangedData} is desired, new code
should specify \Rcode{asRangedData = TRUE} in anticipation of the
change in default value.

\paragraph{Exercises}

\begin{enumerate}
\item Output the track to a file in the ``gff'' format.
\item Read the track back into R.
\item Export the track as a character vector.
\end{enumerate}

<<sol-3, echo=FALSE, results=hide>>=
export(targetTrack, "targets.gff")
targetGff <- import("targets.gff")
targetChar <- export(targetTrack, format = "gff1")
@ 

\subsection{Viewing the targets in a genome browser}

For the next step in our example, we will load the track into a genome
browser for visualization with other genomic annotations. The
\textbf{rtracklayer} package is capable of interfacing with any genome
browser for which a driver exists. In this case, we will interact with
the web-based \textbf{UCSC} browser, but the same code should work for
any browser.

\subsubsection{Starting a session}

The first step towards interfacing with a browser is to start a
browser session, represented in R as a \textit{BrowserSession}
object. A \textit{BrowserSession} is primarily a container of tracks
and genomic views. The following code creates a
\textit{BrowserSession} for the \textbf{UCSC} browser:

<<browserSession, eval=FALSE>>=  
session <- browserSession("UCSC")
@ 
Note that the name of any other supported browser could have been
given here instead of ``UCSC''. To see the names of supported
browsers, enter:
<<genomeBrowsers>>=
genomeBrowsers()
@ 

\subsubsection{Laying the track}

Before a track can be viewed on the genome, it must be
loaded into the session using the \texttt{track<-} function, as
demonstrated below:
<<layTrack, eval=FALSE>>=
track(session, "targets") <- targetTrack
@ 
The \textit{name} argument should be a character vector that will
help identify the track within \texttt{session}. Note that the
invocation of \texttt{track<-} above does not specify an upload
format. Thus, the default, ``auto'', is used. Since the track does not
contain any data values, the track is uploaded as BED. To make this
explicit, we could pass ``bed'' as the \textit{format} parameter.

\paragraph{Exercises}

\begin{enumerate}
\item Lay a track with the first 100 features of \texttt{targetTrack}
\end{enumerate}

Here we use the short-cut \texttt{\$} syntax for storing the track.
<<sol-4, eval = FALSE, echo=FALSE, results=hide>>=
session$target100 <- targetTrack[1:100]
@ 

\subsubsection{Viewing the track}

For \textbf{UCSC}, a view roughly corresponds to one tab or window in
the web browser.  The target sites are distributed throughout the
genome, so we will only be able to view a few features at a time. In
this case, we will view only the first feature in the track. A
convenient way to focus a view on a particular set of features is to
subset the track and pass the range of the subtrack to the constructor
of the view.  Below we take a track subset that contains only the
first feature.
<<take-subset>>=
subTargetTrack <- targetTrack[1] # get first feature
@

Now we call the \texttt{browserView} function to construct the view
and pass the subtrack, zoomed out by a factor of 10, as the segment to
view. By passing the name of the targets track in the \textit{pack}
parameter, we instruct the browser to use the ``pack'' mode for
viewing the track. This results in the name of the microRNA appearing
next to the target site glyph.
<<view-subset, eval=FALSE>>=
view <- browserView(session, subTargetTrack * -10, pack = "targets")
@

If multiple ranges are provided, multiple views are launched:
<<view-subset-multi, eval=FALSE>>=
view <- browserView(session, targetTrack[1:5] * -10, pack = "targets")
@


\paragraph{Exercises}

\begin{enumerate}
\item Create a new view with the same region as \texttt{view}, except
  zoomed out 2X.
\item Create a view with the ``targets'' track displayed in ``full''
  mode, instead of ``packed''.
\end{enumerate}

<<sol-6, eval=FALSE, echo=FALSE, results=hide>>=
viewOut <- browserView(session, range(view) * -2)
viewFull <- browserView(session, full = "targets")
@ 

\subsubsection{A shortcut}

There is also a shortcut to the above steps. The \texttt{browseGenome}
function creates a session for a specified browser, loads one or more
tracks into the session and creates a view of a given genome segment.
In the following code, we create a new \textbf{UCSC} session, load the
track and view the first two features, all in one call:
<<browseGenome, eval=FALSE>>=
browseGenome(targetTrack, range = subTargetTrack * -10)
@
It is even simpler to view the subtrack in \textbf{UCSC} by
relying on parameter defaults:
<<browseGenome-simple, eval=FALSE>>=
browseGenome(subTargetTrack)
@ 

\subsubsection{Downloading tracks}

It is possible to query the browser to obtain the names of the loaded
tracks and to download the tracks into R. To list the tracks loaded in
the browser, enter the following:
%
<<get-track-names, eval=FALSE>>=
loaded_tracks <- trackNames(session)
@
%
One may download any of the tracks, such as the ``targets'' track that
was loaded previously in this example.
%
<<get-track-data, eval=FALSE>>=
subTargetTrack <- track(session, "targets")
@
%
The returned object is a \Rclass{RangedData}, even if the data was
originally uploaded as a \Rclass{GRanges} or other object. To get a
\Rclass{GRanges} instead, pass \Rcode{asRangedData = FALSE}. By
default, the segment of the track downloaded is the current default
genome segment associated with the session. One may download track
data for any genome segment, such as those on a particular
chromosome. Note that this does not distinguish by strand; we are only
indicating a position on the genome.
%
<<get-track-segment, eval=FALSE>>=
chr1Targets <- track(session, "targets", chr1Targets)
@

\paragraph{Exercises}

\begin{enumerate}
\item Get the SNP under the first target, displayed in \texttt{view}.
\item Get the UCSC gene for the same target.
\end{enumerate}

<<sol-7, eval=FALSE, echo=FALSE, results=hide>>=
region <- range(subTargetTrack) + 500
targetSNP <- track(session, "snp130", region)
as.data.frame(targetSNP)
targetGene <- track(session, "knownGene", region)
as.data.frame(targetGene)
@ 

\subsubsection{Accessing view state}

The \texttt{view} variable is an instance of \textit{BrowserView},
which provides an interface for getting and setting view
attributes. Note that for the UCSC browser, changing the view state
opens a new view, as a new page must be opened in the web browser. 

To programmatically query the segment displayed by a view, use the
\texttt{range} method for a \textit{BrowserView}.
%
<<genomeSegment-view, eval=FALSE>>=
segment <- range(view)
@
%
Similarly, one may get and set the names of the visible tracks in the view.
<<tracks-view, eval=FALSE>>=
visible_tracks <- trackNames(view)
trackNames(view) <- visible_tracks
@
The visibility mode (hide, dense, pack, squish, full) of the tracks
may be retrieved with the \texttt{ucscTrackModes} method.
%
<<track-modes-view, eval=FALSE>>=
modes <- ucscTrackModes(view)
@
%
The returned value, \texttt{modes}, is of class
\textit{UCSCTrackModes}. The modes may be accessed using the
\texttt{[} function. Here, we set the mode of our ``targets'' track to
``full'' visibility.
<<set-track-modes, eval = FALSE>>=
modes["targets"]
modes["targets"] <- "full"
ucscTrackModes(view) <- modes
@

Existing browser views for a session may be retrieved by calling the
\texttt{browserViews} method on the \textit{browserSession} instance.
%
<<browserViews, eval = FALSE>>=
views <- browserViews(session)
length(views)
@ 
%

\paragraph{Exercises}

\begin{enumerate}
\item Retrieve target currently visible in the view.
\item Limit the view to display only the SNP, UCSC gene and target track.
\item Hide the UCSC gene track.
\end{enumerate}

<<sol-8, eval=FALSE, echo=FALSE, results=hide>>=
viewTarget <- track(session, "targets", range(view))
trackNames(view) <- c("snp130", "knownGene", "targets")
ucscTrackModes(view)["knownGene"] <- "hide"
@ 

\section{CPNE1 expression and HapMap SNPs}

Included with the \textbf{rtracklayer} package is a track object
(created by the \textbf{GGtools} package) with features from a subset
of the SNPs on chromosome 20 from 60 HapMap founders in the CEU
cohort. Each SNP has an associated data value indicating its
association with the expression of the CPNE1 gene according to a
Cochran-Armitage 1df test. The top 5000 scoring SNPs were selected for
the track.

We load the track presently.
<<load-snp>>=
library(rtracklayer)
data(cpneTrack)
@

\subsection{Loading and manipulating the track}

The data values for a track are stored in the columns on the
\textit{RangedData} instance. Often, a track contains a single column
of numeric values, conventionally known as the \textit{score}. The
\texttt{score} function retrieves the column named or \textit{score}
or, if one does not exist, the first column in the
\textit{RangedData}, as long as it is numeric. Otherwise,
\texttt{NULL} is returned.
<<datavals-accessor>>=
head(score(cpneTrack))
@

% Sometimes, it may be convenient to extract the track information as a
% \textit{data.frame}. The \textit{trackData} function does this by
% combining the \textit{featureData} matrix with the \textit{dataVals}.
% It also adds a column named \textit{featMid}, which gives the
% mid-points (the mean of the start and end positions) of each feature
% in the track. Here is an example of using \textit{trackData} to plot
% the test value for each SNP vs. its position.

One use of extracting the data values is to plot the data.
<<trackData, fig=TRUE, png=TRUE>>=
plot(start(cpneTrack), score(cpneTrack))
@ 

\subsection{Browsing the SNPs}

We now aim to view some of the SNPs in the UCSC browser. Unlike the
microRNA target site example above, this track has quantitative
information, which requires special consideration for visualization.

\subsubsection{Laying a WIG track}

To view the SNP locations as a track in a genome browser, we first
need to upload the track to a fresh session. In the code below, we use
the \texttt{[[<-} alias of \texttt{track<-}.
<<layTrack-snp, eval = FALSE>>=
session <- browserSession()
session$cpne <- cpneTrack
@
%
Note that because \texttt{cpneTrack} contains data values and its
features do not overlap, it is uploaded to the browser in the WIG
format. One limitation of the WIG format is that it is not possible to
encode strand information. Thus, each strand needs to have its own
track, and \textbf{rtracklayer} does this automatically, unless only
one strand is represented in the track (as in this case). One could
pass ``bed'' to the \textit{format} parameter of \texttt{track<-} to
prevent the split, but tracks uploaded as BED are much more limited
compared to WIG tracks in terms of visualization options.

To form the labels for the WIG subtracks, `` p'' is concatenated onto
the plus track and `` m'' onto the minus track. Features with missing
track information are placed in a track named with the `` na''
postfix. It is important to note that the subtracks must be identified
individually when, for example, downloading the track or changing
track visibility.

\subsubsection{Plotting the SNP track}

To plot the data values for the SNP's in a track, we need to create a
\textit{browserView}. We will view the region spanning the first 5
SNPs in the track, which will be displayed in the ``full'' mode.
%
<<browserView-snp, eval = FALSE>>=
view <- browserView(session, range(cpneTrack[1:5,]), full = "cpne")
@ 
%
The UCSC browser will plot the data values as bars. There are several
options available for tweaking the plot, as described in the help for
the \textit{GraphTrackLine} class. These need to be specified laying the
track, so we will lay a new track named ``cpne2''. First, we will
turn the \textit{autoScale} option off, so that the bars will be
scaled globally, rather than locally to the current view. Then we
could turn on the \textit{yLineOnOff} option to add horizontal line
that could represent some sort of cut-off. The position of the line is
specified by \textit{yLineMark}. We set it arbitrarily to the 25\%
quantile.
%
<<layTrack-snp2, eval = FALSE>>=
track(session, "cpne2", autoScale = FALSE, yLineOnOff = TRUE, 
      yLineMark = quantile(score(cpneTrack), .25)) <- cpneTrack
view <- browserView(session, range(cpneTrack[1:5,]), full = "cpne2")
@
%

\section{Binding sites for NRSF}
\label{sec:binding}

Another common type of genomic feature is transcription factor binding
sites. Here we will use the \textbf{Biostrings} package to search for
matches to the binding motif for NRSF, convert the result to
a track, and display a portion of it in the \textbf{UCSC} browser.

\subsection{Creating the binding site track}

We will use the \textbf{Biostrings} package to search human chromosome
1 for NRSF binding sites. The binding sequence motif is assumed to be
\textit{TCAGCACCATGGACAG}, though in reality it is more variable.
To perform the search, we run \textit{matchPattern} on the positive
strand of chromosome 1.
%
<<search-nrsf>>=
library(BSgenome.Hsapiens.UCSC.hg19)
nrsfHits <- matchPattern("TCAGCACCATGGACAG", Hsapiens[["chr1"]])
length(nrsfHits)  # number of hits
@ 
%

We then convert the hits, stored as a \textit{Views} object (a
particular type of \textit{IRanges} object), to a \textit{RangedData}
instance.
%
<<track-nrsf>>=
nrsfTrack <- GenomicData(nrsfHits, strand="+", chrom="chr1", genome = "hg19")
@ 
%
\Rfunction{GenomicData} is a convenience function that by default
constructs a \Rclass{RangedData} object. A \Rclass{GRanges} may be
obtained by passing \Rcode{asRangedData = FALSE}; however, a
\Rclass{RangedData} may be preferred in this case, as the
\Robject{nrsfHits} object is stored as a \Rclass{Views} object in
\Robject{nrsfTrack} without any loss of informatqion, in particular
the sequence of chr1.

\subsection{Browsing the binding sites}

Now that the NRSF binding sites are stored as a track, we can upload
them to the UCSC browser and view them. Below, load the track and we
view the region around the first hit in a single call to
\texttt{browseGenome}.
%
<<browserView-nrsf, eval = FALSE>>=
session <- browseGenome(nrsfTrack, range = range(nrsfTrack[1,]) * -10)
@ 
%
We observe significant conservation across mammal species in the
region of the motif.

\section{Conclusion}

These case studies have demonstrated a few of the most important
features of \textbf{rtracklayer}.
Please see the package documentation for more details.

The following is the session info that generated this vignette:
<<session-info>>=
  sessionInfo()
@ 

\end{document}
