%\VignetteIndexEntry{Genome intervals and read alignments for functional exploration}
%\VignetteDepends{girafe, RColorBrewer}
%\VignetteKeywords{next-generation sequencing, read alignment}
%\VignettePackage{girafe} % name of package

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\title{An overview of the \Rpackage{girafe} package}
\author{J.\ Toedling, C.\ Ciaudo, O.\ Voinnet,
E.\ Heard, E.\ Barillot}
\maketitle


\section{Introduction}

The intent of package \Rpackage{girafe} is to facilitate
the functional exploration of the alignments of multiple
reads\footnote{
  The package has been developed for 
  analysing single-end reads (fragment libraries) and 
  does not support mate-pair reads yet.}
from next-generation sequencing~(NGS) data to a genome.

It extends the functionality of the
Bioconductor~\citep{Gentleman2004}
packages \Rpackage{ShortRead}~\citep{Morgan2009}
and \Rpackage{genomeIntervals}.
%
\begin{Schunk}
\begin{Sinput}
> library("girafe")
> library("RColorBrewer")
\end{Sinput}
\end{Schunk}

If you use \Rpackage{girafe} for analysing your data,
please cite:
\begin{itemize}
\item{J Toedling, C Ciaudo, O Voinnet, E Heard and E Barillot (2010) girafe -- an {R/Bioconductor} package for functional exploration of aligned next-generation sequencing reads. \textsl{Bioinformatics}, 26(22):2902-3.}
\end{itemize}
\nocite{Toedling2010}

\subsection*{Getting help}

If possible, please send questions about \Rpackage{girafe} to the
Bioconductor mailing list.\\
See \url{http://www.bioconductor.org/docs/mailList.html} \\
Their archive of questions and responses may prove helpful, too.


\section{Workflow}

We present the functionality of the package \Rpackage{girafe}
using example data that was downloaded from the Gene Expression
Omnibus (GEO) database~\citep[GSE10364]{Edgar2002}.
The example data are Solexa reads of 26~nt length derived from
small RNA profiling of mouse oocytes.
The data has previously been described in \citet{Tam2008}.

\begin{Schunk}
\begin{Sinput}
> exDir <- system.file("extdata", package="girafe")
> ### load object describing annotated mouse genome features:
> load(file.path(exDir, "mgi_gi.RData"))
\end{Sinput}
\end{Schunk}

\subsection{Adapter trimming}

We load reads that include parts of the  adapter sequence.
%
\begin{Schunk}
\begin{Sinput}
> ra23.wa  <- readFastq(dirPath=exDir, pattern=
+                       "aravinSRNA_23_plus_adapter_excerpt.fastq")
\end{Sinput}
\end{Schunk}
\begin{Schunk}
\begin{Sinput}
> show(ra23.wa)
\end{Sinput}
\begin{Soutput}
class: ShortReadQ
length: 1000 reads; width: 26 cycles
\end{Soutput}
\end{Schunk}

To removing adapter sequences, we use
the function \Rfunction{trimAdapter}, which relies
on the \Rfunction{pairwiseAlignment} function
from the \Rpackage{Biostrings} package.
The adapter sequence was obtained from the GEO page
of the data.
%
\begin{Schunk}
\begin{Sinput}
> adapter <- "CTGTAGGCACCATCAAT"
> ra23.na  <- trimAdapter(ra23.wa, adapter)
> show(ra23.na)
\end{Sinput}
\begin{Soutput}
class: ShortReadQ
length: 1000 reads; width: 23 cycles
\end{Soutput}
\end{Schunk}

\subsection{Importing aligned reads}

The reads have been mapped to the mouse genome
(assembly \textit{mm9}) using the alignment tool
\textit{Bowtie} alignment tool~\citep{Langmead2009}.

The resulting tab-delimited map file can be read into
an object of class \Rclass{AlignedRead}
using the function \Rfunction{readAligned}.
Both, this class and this function, are defined in the
Bioconductor package \Rpackage{ShortRead}.

\begin{Schunk}
\begin{Sinput}
> exA   <- readAligned(dirPath=exDir, type="Bowtie", 
+    pattern="aravinSRNA_23_no_adapter_excerpt_mm9_unmasked.bwtmap")
> show(exA)
\end{Sinput}
\begin{Soutput}
class: AlignedRead
length: 1689 reads; width: 23 cycles
chromosome: chr14 chr17 ... chr3 chr14 
position: 115443405 13011859 ... 68813840 62250772 
strand: + + ... + - 
alignQuality: NumericQuality 
alignData varLabels: similar mismatch 
\end{Soutput}
\end{Schunk}

The object of class \Rclass{AlignedRead} can be converted
into an object of class \Rclass{AlignedGenomeIntervals},
which is the main class of the \Rpackage{girafe} package.
%
\begin{Schunk}
\begin{Sinput}
> exAI <- as(exA, "AlignedGenomeIntervals")
> organism(exAI) <- "Mm"
\end{Sinput}
\end{Schunk}

For alignments in BAM format~\citep{Li2009}, there is an
alternative way of importing the data.
The function \Rfunction{agiFromBam} can be used to directly
create \Rclass{AlignedGenomeIntervals} objects from
indexed and sorted BAM files, making use of functionalities
in the \Rpackage{Rsamtools} package.


\subsection{Exploring the aligned reads}

\begin{Schunk}
\begin{Sinput}
> show(exAI)
\end{Sinput}
\begin{Soutput}
1,286 genome intervals with 1,689 aligned reads on 22 chromosome(s).
Organism: Mm  
\end{Soutput}
\end{Schunk}

Which chromosomes are the intervals located on?
\begin{Schunk}
\begin{Sinput}
> table(seqnames(exAI))
\end{Sinput}
\begin{Soutput}
 chr1 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19  chr2  chr3 
  112    50    60    53    65    74    59    48    47    43    19    87    62 
 chr4  chr5  chr6  chr7  chr8  chr9 chrMT  chrX  chrY 
   57    52    82    51    57    69     5   132     2 
\end{Soutput}
\end{Schunk}

A subset of the intervals on a specific chromosome
can be obtained using subsetting via \verb='['=.
\begin{Schunk}
\begin{Sinput}
> detail(exAI[seqnames(exAI)=="chrMT"])
\end{Sinput}
\begin{Soutput}
  start   end seq_name strand reads matches                sequence
1   964   986    chrMT      +     1       1 GTTTATGAGAGGAGATAAGTTGT
2 11613 11635    chrMT      +    10       2 AAGAAAGATTGCAAGAACTGCTA
3 11613 11635    chrMT      +     1       2 AAGCAAGATTGCAAGAACTGCTA
4 11613 11635    chrMT      +     1       2 AAGAACGATTGCAAGAACTGCTA
5 11613 11635    chrMT      +     1       3 AAGAAAGATTGCAAGAACTGTTA
\end{Soutput}
\end{Schunk}

Finally, what is the number of
aligned \underline{reads} per chromosome?
\begin{Schunk}
\begin{Sinput}
> summary(exAI)
\end{Sinput}
\begin{Soutput}
Number of aligned reads per chromosome:
 chr1 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19  chr2  chr3 
  139    51   105    56    83   131    68    61    48    44    19   106   107 
 chr4  chr5  chr6  chr7  chr8  chr9 chrMT  chrX  chrY 
   61    52   107    57    70   102    14   206     2 
\end{Soutput}
\end{Schunk}

\subsection{Processing the aligned intervals}

Sometimes,
users may wish to combine certain aligned intervals.
One intention could be to combine aligned reads at
exactly the same position, which only differ in their
sequence due to sequencing errors.
Another objective could be to combine overlapping
short reads that may be (degradation) products
of the same primary transcript.
The function \Rfunction{reduce} combines a set of
aligned intervals into a single aligned interval, if
the intervals:
\begin{itemize}
  \item are on the same strand,
  \item are overlapping (or contained in each other)
    or directly adjacent to each other AND
  \item have the same \emph{read match specificity} (see below)
\end{itemize}

\phead{Read match specificity}
By the \emph{read match specificity}~$r(I_i)$
of an interval~$I_i$, we refer to the total number of
valid alignments of reads that have been aligned to $I_i$,
\textsl{i.e.} the total numbers of intervals with the same
reads aligned in the whole genome
(or other set of reference sequences).
If $r(I_i)=1$, the reads that were aligned to the
interval~$I_i$ have no other valid alignments in the whole
genome, i.e. the interval~$I_i$ is the unique match
position of these reads.
If $r(I_i)=2$, the reads that were aligned to the
interval~$I_i$ have exactly one other valid alignment
to another interval~\mbox{$I_j,~j \neq i$}.
The match specificity is stored in the \Robject{matches}
slot of objects of the
class \Rclass{AlignedGenomeIntervals}.

We first demonstrate the \Rfunction{reduce}
using a toy example data object.
\begin{Schunk}
\begin{Sinput}
> D <- AlignedGenomeIntervals(
+      start=c(1,3,4,5,8,10,11), end=c(5,5,6,8,9,11,13),
+      chromosome=rep(c("chr1","chr2","chr3"), c(2,2,3)),
+      strand=c("-","-","+","+","+","+","+"),
+      sequence=c("ACATT","ACA","CGT","GTAA","AG","CT","TTT"),
+      reads=rep(1,7), matches=c(rep(1,6),3))
> detail(D)
\end{Sinput}
\begin{Soutput}
  start end seq_name strand reads matches sequence
1     1   5     chr1      -     1       1    ACATT
2     3   5     chr1      -     1       1      ACA
3     4   6     chr2      +     1       1      CGT
4     5   8     chr2      +     1       1     GTAA
5     8   9     chr3      +     1       1       AG
6    10  11     chr3      +     1       1       CT
7    11  13     chr3      +     1       3      TTT
\end{Soutput}
\end{Schunk}
Calling the \Rfunction{reduce} method on these example
data results in the following:
\begin{Schunk}
\begin{Sinput}
> detail(reduce(D))
\end{Sinput}
\begin{Soutput}
  start end seq_name strand reads matches sequence
1     1   5     chr1      -     2       1    ACATT
2     4   8     chr2      +     2       1    CGTAA
3     8  11     chr3      +     2       1     AGCT
4    11  13     chr3      +     1       3      TTT
\end{Soutput}
\end{Schunk}
Note that the two last intervals still show overlap.
However, the last interval is a non-unique match position
of the respective reads (\mbox{\Robject{matches}$=3$}),
in contrast to the other intervals.

Here is another example using the data introduced above.
\begin{Schunk}
\begin{Sinput}
> S <- exAI[seqnames(exAI)=="chrX" & matches(exAI)==1L & exAI[,1]>1e8]
> detail(S)
\end{Sinput}
\begin{Soutput}
      start       end seq_name strand reads matches                sequence
1 100768450 100768472     chrX      -     1       1 ATATAATACAACCTGCTAACTGT
2 101311567 101311589     chrX      -    18       1 TGAGGTTGGTGTACTGTGTGTGG
3 101311567 101311589     chrX      -    12       1 TGAGGTTGGTGTACTGTGTGTGA
4 101311567 101311589     chrX      -     2       1 TGAGGTTGGTGTACTGTGTGTGT
5 101311567 101311589     chrX      -     1       1 TGACGTTGGTGTACTGTGTGTGA
6 101311567 101311589     chrX      -     1       1 TGAGGTTGGTGTACTGTGTGCGG
7 148346896 148346918     chrX      +     4       1 TGAGGTAGTAGATTGTATAGTTT
\end{Soutput}
\end{Schunk}

Calling the \Rfunction{reduce} method on these
data leads to the following result:
%
\begin{Schunk}
\begin{Sinput}
> detail(reduce(S))
\end{Sinput}
\begin{Soutput}
      start       end seq_name strand reads matches                sequence
1 100768450 100768472     chrX      -     1       1 ATATAATACAACCTGCTAACTGT
2 101311567 101311589     chrX      -    34       1 TGAGGTTGGTGTACTGTGTGTGG
3 148346896 148346918     chrX      +     4       1 TGAGGTAGTAGATTGTATAGTTT
\end{Soutput}
\end{Schunk}

Notice that the reads that match the same segment of the
X~chromosome differ in their last base.
However, since most of the reads have a 'G' as final
letter, the combined aligned interval als has a 'G'
%an 'R', the IUPAC code for 'A' or 'G', %an 'N'
as the last letter.

The additional argument~\Rfunarg{method="exact"}
can be specified if you want to solely combine intervals
that have exactly the same start and stop position (but may
have reads of slightly different sequence aligned to them).
Consider the following example:
\begin{Schunk}
\begin{Sinput}
> S2 <- exAI[seqnames(exAI)=="chr11" & matches(exAI)==1L & exAI[,1]>8e7]
> detail(S2)
\end{Sinput}
\begin{Soutput}
      start       end seq_name strand reads matches                sequence
1  86397621  86397643    chr11      -    20       1 TAGCTTATCAGACTGATGTTTAC
2  86397621  86397643    chr11      -     1       1 TAGATTATCAGACTGATGTTTAC
3  86397621  86397643    chr11      -     2       1 TAGCTTATCAGACTGATGTTCAC
4  88515338  88515360    chr11      -     1       1 GGTGCAGGGAGCGCCAGTGTCTC
5  96178500  96178522    chr11      +     2       1 TACCCTGTAGATCCGAATTTTTG
6  96178501  96178523    chr11      +     1       1 ACCCTGTAGATCCGAATTTGTGA
7 108873196 108873218    chr11      -     1       1 AGTGCGGTAACGCGACCGCTACC
\end{Soutput}
\begin{Sinput}
> detail(reduce(S2, method="exact"))
\end{Sinput}
\begin{Soutput}
      start       end seq_name strand reads matches                sequence
1  86397621  86397643    chr11      -    23       1 TAGCTTATCAGACTGATGTTTAC
2  88515338  88515360    chr11      -     1       1 GGTGCAGGGAGCGCCAGTGTCTC
3  96178500  96178522    chr11      +     2       1 TACCCTGTAGATCCGAATTTTTG
4  96178501  96178523    chr11      +     1       1 ACCCTGTAGATCCGAATTTGTGA
5 108873196 108873218    chr11      -     1       1 AGTGCGGTAACGCGACCGCTACC
\end{Soutput}
\end{Schunk}
Notice that the 6th aligned interval in~\Robject{S2} is
only shifted by 1~nt from the 5th one. 
By default, the function \Rfunction{reduce}
would merge them into one aligned genome interval.
However, when \Rfunarg{method="exact"} is specified,
these two intervals are not merged
since they are not at exactly the same position.
There are additional methods for restricting the merging
to intervals with the same 5'- and \mbox{3'-ends}
(specify \Rfunarg{method="same5"} and
\Rfunarg{method="same3"}, respectively).


\subsection{Visualising the aligned genome intervals}

The package~\Rpackage{girafe} contains functions for
visualising genomic regions with aligned reads.
%
\begin{Schunk}
\begin{Sinput}
> plot(exAI, mgi.gi, chr="chrX", start=50400000, 
+      end=50410000, show="minus")
\end{Sinput}
\end{Schunk}
See the result in Figure~\ref{girafe-plotAI}.

\myincfig{girafe-plotAI}{0.8\textwidth}{A 10-kb region on
  the mouse X~chromosome. Reads aligned to the Watson strand
  in this region would be shown above the chromosome coordinate
  axis with the annotation of genome elements in this region,
  while reads aligned to the Crick strand are shown below.
  In the region shown, there are only intervals with aligned
  reads on the Crick strand, and these four intervals overlap
  with annotated microRNA positions.}

Note that the annotation of genome elements (as shown
in Figure~\ref{girafe-plotAI}) has to be supplied to the
function.
Here the object \Robject{mgi.gi} contains most annotated
genes and ncRNAs for the mouse genome (assembly: \textit{mm9}).
This object has been created beforehand\footnote{See the
script \texttt{prepareAnnotation.R} in the package scripts
directory for an example of how to create such an object.}
and it is of class \Rclass{Genome\_intervals\_stranded},
a class defined in package \Rpackage{genomeIntervals}.


\subsection{Summarising the data using sliding windows}
 
The data can be searched for regions of defined interest
using a sliding-window approach implemented in the
function~\Rfunction{perWindow}.
For each window, the number of intervals with
aligned reads, the total number of reads aligned,
the number of unique reads aligned, the fraction
of intervals on the Plus-strand, and the higher number
of aligned reads at a single interval within the window
are reported.

\begin{Schunk}
\begin{Sinput}
> exPX  <- perWindow(exAI, chr="chrX", winsize=1e5, step=0.5e5)
> head(exPX[order(exPX$n.overlap, decreasing=TRUE),])
\end{Sinput}
\begin{Soutput}
      chr     start       end n.overlap n.reads n.unique frac.plus max.reads
942  chrX  50341103  50441102        18      55       18         0        28
943  chrX  50391103  50491102        18      55       18         0        28
1960 chrX 101241103 101341102         5      34        5         0        18
1961 chrX 101291103 101391102         5      34        5         0        18
1216 chrX  64041103  64141102         4       5        4         0         2
1215 chrX  63991103  64091102         3       4        3         0         2
         first      last
942   50401220  50407226
943   50401220  50407226
1960 101311567 101311589
1961 101311567 101311589
1216  64049984  64092296
1215  64049984  64067192
\end{Soutput}
\end{Schunk}


\subsection{Exporting the data}

The package \Rpackage{girafe} also contains methods for
exporting the data into tab-delimited text files, which
can be uploaded to the UCSC genome
browser\footnote{\url{http://genome.ucsc.edu}}
as 'custom tracks'.

Currently, there are methods for exporting the data
in 'bed' format and 'bedGraph' format, either writing
intervals from both strands into one file or into
two separate files.
Details about these track formats can be found
at the UCSC genome browser web pages.

\begin{Schunk}
\begin{Sinput}
> export(exAI, con="export.bed",
+        format="bed", name="example_reads",
+        description="Example reads",
+        color="100,100,255", visibility="pack")
\end{Sinput}
\end{Schunk}

Additional arguments to the export function,
besides \Rfunarg{object}, \Rfunarg{con},
and \Rfunarg{format}, are treated
as attributes for the track definition line, which
specifies details concerning how the data should be
visualised in the genome browser.

Users may also wish to consult the Bioconductor
package~\Rpackage{rtracklayer} for data transfer and
direct interaction between R and the UCSC genome
browser.

\subsection{Overlap with annotated genome features}

Next, we determine the degree of overlap of the aligned
reads with annotated genomic elements.
In this example, the annotated genome elements are provided
as an object of class
\Rclass{Genome\_intervals\_stranded}\footnote{%
Objects of class \Rclass{Genome\_intervals} and
\Rclass{AlignedGenomeIntervals} are also allowed.}.
This objects needs to be created beforehand. See the
script \texttt{prepareAnnotation.R} in the package scripts
directory\footnote{\texttt{system.file("scripts", package="girafe")}}
for an example of how to create such an object.
%
\begin{Schunk}
\begin{Sinput}
> exOv <- interval_overlap(exAI, mgi.gi)
\end{Sinput}
\end{Schunk}

How many elements do read match positions generally overlap?
%
\begin{Schunk}
\begin{Sinput}
> table(listLen(exOv))
\end{Sinput}
\begin{Soutput}
  0   1   2  12 
815 340 130   1 
\end{Soutput}
\end{Schunk}

What are the 12~elements
overlapped by a single match position?
%
\begin{Schunk}
\begin{Sinput}
> mgi.gi$ID[exOv[[which.max(listLen(exOv))]]]
\end{Sinput}
\begin{Soutput}
 [1] "Pcdha1"  "Pcdha2"  "Pcdha3"  "Pcdha4"  "Pcdha5"  "Pcdha6"  "Pcdha7" 
 [8] "Pcdha8"  "Pcdha9"  "Pcdha10" "Pcdha11" "Pcdha12"
\end{Soutput}
\end{Schunk}

And in general,
what kinds of annotated genome elements are overlapped by reads?
%
\begin{Schunk}
\begin{Sinput}
> (tabOv <- table(as.character(mgi.gi$type)[unlist(exOv)]))
\end{Sinput}
\begin{Soutput}
      gene    lincRNA      miRNA      ncRNA pseudogene       rRNA     snoRNA 
       238         15        297         19         28         10          1 
      tRNA 
         4 
\end{Soutput}
\end{Schunk}

We display these overlap classes using a pie chart.
%
\begin{Schunk}
\begin{Sinput}
> my.cols <- brewer.pal(length(tabOv), "Set3")
> pie(tabOv, col=my.cols, radius=0.88)
\end{Sinput}
\end{Schunk}
See the result in Figure~\ref{girafe-displayPie}.

\myincfig{girafe-displayPie}{0.6\textwidth}{Pie chart
  showing what kinds of genome elements are overlapped
  by aligned reads. Note that the proportions of the pie
  chart are given by the proportions among all annotated
  genome elements that \mbox{have $\geq 1$ reads} mapped
  to them and not by the total numbers of reads mapped
  to elements of that class, in which case the proportion
  of the miRNA class would be significantly larger.}

Note that function \Rfunction{interval.overlap} only
determines whether two intervals are overlapping by
at least one base. For restricting the result to
intervals overlapping by at least a certain number
of bases or by a fraction of the interval's
length, see the function \Rfunction{fracOverlap}.

\section{Memory usage}

At the moment, \Rpackage{girafe} and the packages that it
depends on, retain all the information concerning the
read alignments in memory.
This allows quick access to and swift operations on the
data, but may limit the package's usability on machines
with low amounts of RAM.

The step with the highest RAM requirements is importing
the alignments and saving them as objects of the
\Rclass{AlignedRead} class using the functionality
in package \Rpackage{ShortRead}.
Usually, objects of the \Rclass{AlignedGenomeIntervals} class
are created starting from \Rclass{AlignedRead} objects
and the \Rclass{AlignedRead} objects can safely be discarded
after this step.
Since the data is summarised in that process, 
\Rclass{AlignedGenomeIntervals} objects require about
10--100 times less memory than the original
\Rclass{AlignedRead} object\footnote{%
  e.g., an \Rclass{AlignedRead} object for holding
  $10^6$~reads of length 36~bp aligned to the mouse reference
  genome occupies about 1.4~Gb in RAM but is processed into
  an \Rclass{AlignedGenomeIntervals} object of size 66.7~Mb}.
We recommend that the import of the
alignments and the generation of the
\Rclass{AlignedGenomeIntervals} are performed
using a separate script which only needs to be called
once on a machine with sufficient RAM.

A suggestion for limiting memory usage is to perform
the read alignments and import of the results in batches of
a few million reads each.
The batch-wise result \Rclass{AlignedGenomeIntervals} objects
can later be combined using the basic R function ''c'',
the standard way of combining objects, optionally followed
by calls of the \Rfunction{reduce} function.

For alignments in SAM/BAM format,
the \emph{Samtools} software suite~\citep{Li2009}
as well as the Bioconductor package 
\Rpackage{Rsamtools} allow the user to access and import
only selected subsets of the data, which also leads to
a lower memory footprint. For details, please
refer to the documentation of these packages.

Finally, while the processing of \Rclass{AlignedRead}
objects is the principal way of generating 
\Rclass{AlignedGenomeIntervals} objects, there is also
a convenience function called\\
\Rfunction{AlignedGenomeIntervals}, which can be used
to create these objects from simpler objects in the
work space, such as data read in using basic R functions
such as \Rfunction{scan}. This convenience function
may be easier to use for importing and processing the
data in manageable chunks.

When following these suggestions, most operations
with the \Rpackage{girafe} package should be possible
on a machine with 4~Gb of RAM, and we have not so far
encountered a situation that requires more than 12~Gb
(state as of the end of 2009).
However, increased throughput of sequencing machines and
longer reads will lead to increased memory requirements.
Future developments of this and other NGS-related
Bioconductor packages will therefore likely concern ways
to reduce the memory footprint. One idea is to make use
of packages like \Rpackage{ff}, which provide ways of
swapping data from RAM to flat files on the hard disk,
while still allowing fast and direct access to the data.

\section{A word about speed}

For improving the run time on machines with multiple
processors, some of the functions in the \Rpackage{girafe}
package have been implemented to make use of the functionality
in the \Rpackage{parallel} package.
If \Rpackage{parallel} has been attached
and initialised before calling these functions, the
functions will make use of \Rfunction{mclapply} instead of
the normal \Rfunction{lapply} function.
The number of cores to be used in parallel is
determined by the \texttt{mc.cores} option (see
the example below).

For example, if \Rpackage{parallel} is functional on
a given system\footnote{The \Rfunction{mclapply} function
currently does not support Windows operating systems.},
there should be an obvious speed
improvement in the following code example.

\begin{Schunk}
\begin{Sinput}
> library("parallel")
> options("mc.cores"=4) # adjust to your machine
> exAI.R <- reduce(exAI, mem.friendly=TRUE)
\end{Sinput}
\end{Schunk}

\section{Links to other Bioconductor packages}

The \Rpackage{girafe} package is mostly built upon the
interval notation and implementation provided by the
packages \Rpackage{intervals} and \Rpackage{genomeIntervals}.
Functions from the 
\Rpackage{ShortRead} package~\citep{Morgan2009}
are used for importing the data,
and \Rpackage{Biostrings} provides help for working
with the read nucleotide sequences.
\Rpackage{girafe} also makes limited use of the
\Rclass{Rle} and \Rclass{IRanges} classes
defined in the \Rpackage{IRanges} package.
Furthermore, the data can be converted between object
classes defined in \Rpackage{girafe} and \Rpackage{IRanges}.

We note that many of the interval operations in \Rpackage{girafe}
can also be performed using classes and functions defined in
the \Rpackage{IRanges} package.
However, the scope of the packages is slightly different.
While \Rpackage{IRanges} is meant to be a generic infrastructure
package of the Bioconductor project, the aim of \Rpackage{girafe}
is to provide a single, comparatively lightweight, object class
for working with reads aligned to the genome,
the \Rclass{AlignedGenomeIntervals}.
This class and its methods allow easy access to such data
and facilitate standard operations and workflows.

There is some overlap in functionality between
\Rpackage{girafe}, \Rpackage{IRanges}, \Rpackage{GenomicRanges}
and \Rpackage{tracklayer}.
The range of interactions between these packages and
new Bioconductor packages related to next-generation
sequencing is likely to increase over the releases.
Our aim is to provide users with a broad range of alternatives
for selecting the classes and functions 
that are most suited for their workflows.

\small
\section*{Package versions}
This vignette was generated using the following package versions:
%
\begin{itemize}\raggedright
  \item R version 3.6.0 (2019-04-26), \verb|x86_64-pc-linux-gnu|
  \item Running under: \verb|Ubuntu 18.04.2 LTS|
  \item Matrix products: default
  \item BLAS:   \verb|/home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so|
  \item LAPACK: \verb|/home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so|
  \item Base packages: base, datasets, grDevices, graphics, grid,
    methods, parallel, stats, stats4, utils
  \item Other packages: AnnotationDbi~1.46.0, Biobase~2.44.0,
    BiocGenerics~0.30.0, BiocParallel~1.18.0, Biostrings~2.52.0,
    DelayedArray~0.10.0, GenomeInfoDb~1.20.0, GenomicAlignments~1.20.0,
    GenomicRanges~1.36.0, IRanges~2.18.0, RColorBrewer~1.1-2,
    Rsamtools~2.0.0, S4Vectors~0.22.0, ShortRead~1.42.0,
    SummarizedExperiment~1.14.0, XVector~0.24.0,
    genomeIntervals~1.40.0, girafe~1.36.0, intervals~0.15.1,
    matrixStats~0.54.0, org.Mm.eg.db~3.8.2
  \item Loaded via a namespace (and not attached): DBI~1.0.0,
    GenomeInfoDbData~1.2.1, Matrix~1.2-17, RCurl~1.95-4.12,
    RSQLite~2.1.1, Rcpp~1.0.1, bit~1.1-14, bit64~0.9-7, bitops~1.0-6,
    blob~1.1.1, compiler~3.6.0, digest~0.6.18, hwriter~1.3.2,
    lattice~0.20-38, latticeExtra~0.6-28, memoise~1.1.0,
    pkgconfig~2.0.2, tools~3.6.0, zlibbioc~1.30.0
\end{itemize}
\section*{Acknowledgements}

Many thanks to Nicolas Servant, 
Val\'erie Cognat, Nicolas Delhomme,
and especially Patrick Aboyoun
for suggestions and feedback on the package.
Special thanks to Julien Gagneur and Richard Bourgon for
writing \Rpackage{genomeIntervals} and for rapidly answering
all my questions regarding the package.\\
The plotting functions in package \Rpackage{girafe} are
largely based on the function \Rfunction{plotAlongChrom}
and its auxiliary functions from package \Rpackage{tilingArray},
most of which were written by Wolfgang Huber.\\
\textbf{Funding:} This work was supported by
the Institut Curie, INCa "GepiG".

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