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\begin{document}

\title{Introduction to the \Rpackage{TSSi} package:\\
Identification of Transcription Start Sites}


\author{Julian Gehring, Clemens Kreutz}

\maketitle
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\begin{abstract}
Along with the advances in high-throughput sequencing, the detection
of transcription start sites \emph{(TSS)} using CAP-capture techniques
has evolved recently. While many experimental applications exist,
the analysis of such data is still non-trivial. Approaching this,
the \Rpackage{TSSi} package offers a flexible statistical preprocessing
for CAP-capture data and an automated identification of start sites.
\end{abstract}

\section{Introduction}

High throughput sequencing has become an essential experimental approach
to investigate genomes and transcriptional processes. While cDNA sequencing
(\emph{RNA-seq}) using random priming and/or fragmentation of cDNA
will result in a shallow distribution of reads typically biased towards
the 3' end, approaches like CAP-capture enrich 5' ends and yield more
clearly distinguishable peaks around the transcription start sites.

Predicting the location of transcription start sites \emph{(TSS)}
is hampered by the existence of alternative ones since their number
within regions of transcription is unknown. In addition, measurements
contain false positive counts. Therefore, only the counts which are
significantly larger than an expected number of background reads are
intended to be predicted as TSS. The number of false positive reads
increases in regions of transcriptional activity and such reads obviously
do not map to random positions. On the one hand, these reads seem
to occur sequence dependently and therefore cluster to certain genomic
positions, on the other hand, they are detected more frequently than
being originated from real TSS. Because currently, there is no error
model available describing such noise, the \Rpackage{TSSi} package
implements an heuristic approach for an automated and flexible prediction
of TSS. 


\section{Data set}

To demonstrate the functionality and usage of this package, experimental
CAP-capture data obtained with Solexa sequencing is used. The reads
were mapped to the genome, such that the number of sequenced 5' ends
and their positions in the genome are available%
\footnote{The \emph{sequencing workflow} at the bioconductor website describes
basic steps and tools for the import and processing of sequcencing
data (\url{http://bioconductor.org/help/workflows/high-throughput-sequencing/}).%
}. The data frame \Rvar{physcoCounts} contains information about the
chromosome, the strand, the 5' position, and the total number of mapped
reads. Additionally, regions based on existing annotation are provided
which are used here to divide the data into independent subsets for
analysis.

<<load_package>>=
library(TSSi)
@
<<load_data>>=
data(physcoCounts)
head(physcoCounts)
table(physcoCounts$chromosome, physcoCounts$strand)
@


\section{Segment read data}

As a first step in the analysis, the reads are passed to the \Rmethod{segmentizeCounts}
method. Here, the data is divided into \emph{segments}, for which
the following analysis is performed independently. This is performed
based on the information about the chromosomes, the strands, and the
regions of the reads. The segmented data is returned as an object
of the class \Rclass{TssData}.

<<segmentize>>=
attach(physcoCounts)
x <- segmentizeCounts(counts=counts, start=start, chr=chromosome, region=region, strand=strand)
detach(physcoCounts)
x
@

The segments and the associated read data are accessible through several
\Rmethod{get} methods. Data from individual segments can be referred
to by either its name or an index.


<<get_segments>>=
segments(x)
names(x)
@

<<get_reads>>=
head(reads(x, 3))
head(start(x, 3))
head(start(x, names(x)[3]))
@


\section{Normalization}

The normalization reduces the noise by shrinking the counts towards
zero. This step is intended to eliminate false positive counts as
well as making further analyzes more robust by reducing the impact
of large counts. Such a shrinkage or regularization procedure constitutes
a well-established strategy in statistics to make predictions conservative,
that means to reduce the number of false positive predictions. To
enhance the shrinkage of isolated counts in comparison to counts in
regions of strong transcriptional activity, the information of consecutive
genomic positions in the measurements is regarded by evaluating differences
between adjacent count estimates.

The computation can be performed with a approximation based on the
frequency of all reads or fitted explicitly for each segment. On platforms
supporting the \Rpackage{multicore} package, the fitting can be spread
over multiple processor cores in order to decrease computation time.

<<normalize_ratio>>=
yRatio <- normalizeCounts(x)
@

<<normalize_fit>>=
yFit <- normalizeCounts(x, fit=TRUE)
yFit
head(reads(yFit, 3))
@

<<plot_normalize_fit, fig=TRUE, echo=TRUE>>=
plot(yFit, 3)
@


\section{Identifying transcription start sites}

After normalization of the count data, an iterative algorithm is applied
for each segment to identify the TSS. The expected number of false
positive counts is initialized with a default value given by the read
frequency in the whole data set. The position with the largest counts
above is identified as a TSS, if the expected transcription level
is at least one read above the expected number of false positive reads.
The transcription levels for all TSS are calculated by adding all
counts to their nearest neighbor TSS.

Then, the expected number of false positive reads is updated by convolution
with exponential kernels. The decay rates \Rfunarg{tau} in 3' direction
and towards the 5'-end can be chosen differently to account for the
fact that false positive counts are preferably found in 5' direction
of a TSS. This procedure is iterated as long as the set of TSS increases.

<<identify>>=
z <- identifyStartSites(yFit)
z
head(segments(z))
head(tss(z, 3))
head(reads(z, 3))
@

<<plot_identify, fig=TRUE, echo=TRUE>>=
plot(z, 3)
@


\section{Visualizing and customizing figures}

The \Rmethod{plot} method allows for a simple, but powerful visualization
and customization of the produced figures. For each element of the
figure, all graphical parameters can be set, supplying them in the
form of named lists. In the following, plotting of the threshold and
the ratio estimates are omitted, as well as the representation of
some components is adapted. For a detailed description on the individual
settings, please refer to the \Rmethod{plot} documentation of this
package.


<<plot_custom, fig=TRUE, echo=TRUE>>=
plot(z, 4,
ratio=FALSE,
threshold=FALSE,
baseline=FALSE,
expect=TRUE, expectArgs=list(type="l"), extend=TRUE,
countsArgs=list(type="h", col="darkgray", pch=NA),
plotArgs=list(xlab="Genomic position", main="TSS for segment 's1_-_155'"))
@


\section{Converting and exporting results}

While the get methods \Rmethod{reads}, \Rmethod{segments}, and \Rmethod{tss}
provide a simple access to relevant results, such data can also be
represented with the framework provided by the \Rpackage{IRanges}
package. Converting the data to an object of class \Rclass{RangedData}
allows for a standard representation and interface to other formats,
for example using the \Rpackage{rtracklayer} package.

<<convert_iranges>>=
readsRd <- readsAsRangedData(z)
segmentsRd <- segmentsAsRangedData(z)
tssRd <- tssAsRangedData(z)
tssRd
@
<<export_rtracklayer>>=
#library(rtracklayer)
#tmpFile <- tempfile()
#export.gff3(readsRd, paste(tmpFile, "gff", sep="."))
#export.bed(segmentsRd, paste(tmpFile, "bed", sep="."))
#export.bed(tssRd, paste(tmpFile, "bed", sep="."))
@

\newpage{}


\section*{Session info}

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\end{document}