@@ -25,12 +25,13 @@

 \newcommand{\Rdata}[1]{{\texttt{#1}}}

 \newcommand{\Rpackage}[1]{{\textit{#1}}}

-\author{Supat Thongjuea \footnote{Bergen Center for Computational Science, Bergen,  Norway}}

+\author{Supat Thongjuea \footnote{The Weatherall Institute of Molecular

+Medicine, University of Oxford, UK}}

 \title{\textsf{r3Cseq: an R package for the discovery of long-range genomic interactions with 

 chromosome conformation capture and next-generation sequencing data}}

-\date{October 28, 2012}

+\date{January 26, 2015}

 \begin{document}

 <<setup, echo=FALSE>>=

@@ -195,10 +196,13 @@ be used to replace 'mm9\_ref\_chr01.fa' to 'chr1'.

 \section{Getting started}

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 The 3C-seq data  generated by \cite{Ralph:2011} will be used for the demonstration. 

-The current version of r3Cseq supports mouse and human genome. 

+The current version of r3Cseq supports mouse, human, and rat genomes. 

 Therefore, the package requires one of the followings \Rpackage{BSgenome} 

-packages to be installed;\Rpackage{BSgenome.Mmusculus.UCSC.mm9.masked}, 

-\Rpackage{BSgenome.Hsapiens.UCSC.hg18.masked}, and \Rpackage{BSgenome.Hsapiens.UCSC.hg19.masked}.

+packages to be installed;\Rpackage{BSgenome.Mmusculus.UCSC.mm9.masked},

+\Rpackage{BSgenome.Mmusculus.UCSC.mm10.masked},

+\Rpackage{BSgenome.Hsapiens.UCSC.hg18.masked},

+\Rpackage{BSgenome.Hsapiens.UCSC.hg19.masked, and

+\Rpackage{BSgenome.Rnorvegicus.UCSC.rn5.masked}}.

 Loading the \Rpackage{r3Cseq} package into R.

 <<>>=

@@ -197,8 +197,8 @@ be used to replace 'mm9\_ref\_chr01.fa' to 'chr1'.

 The 3C-seq data  generated by \cite{Ralph:2011} will be used for the demonstration. 

 The current version of r3Cseq supports mouse and human genome. 

 Therefore, the package requires one of the followings \Rpackage{BSgenome} 

-packages to be installed;\Rpackage{BSgenome.Mmusculsu.UCSC.mm9}, 

-\Rpackage{BSgenome.Hsapiens.UCSC.hg18}, and \Rpackage{BSgenome.Hsapiens.UCSC.hg19}.

+packages to be installed;\Rpackage{BSgenome.Mmusculus.UCSC.mm9.masked}, 

+\Rpackage{BSgenome.Hsapiens.UCSC.hg18.masked}, and \Rpackage{BSgenome.Hsapiens.UCSC.hg19.masked}.

 Loading the \Rpackage{r3Cseq} package into R.

 <<>>=

@@ -343,7 +343,7 @@ head(detected_genes)

 \Robject{RangedData} to the bedGraph format, which simply upload to the UCSC 

 genome browser.

 <<>>=

-export3Cseq2bedGraph(my3Cseq.obj)	

+#export3Cseq2bedGraph(my3Cseq.obj)	

 @

 \subsection{Summary report}

 \Rfunction{generate3CseqReport} function generates the summary report

new file mode 100644

@@ -0,0 +1,379 @@

+%\VignetteIndexEntry{r3Cseq}

+%\VignetteKeywords{r3Cseq}

+%\VignettePackage{r3Cseq}

+

+%documentclass[12pt, a4paper]{article}

+\documentclass[12pt]{article}

+

+\usepackage{amsmath}

+\usepackage{hyperref}

+\usepackage[authoryear,round]{natbib}

+

+\textwidth=6.2in

+\textheight=8.5in

+%\parskip=.3cm

+\oddsidemargin=.1in

+\evensidemargin=.1in

+\headheight=-.3in

+

+\newcommand{\scscst}{\scriptscriptstyle}

+\newcommand{\scst}{\scriptstyle}

+\newcommand{\Rfunction}[1]{{\texttt{#1}}}

+\newcommand{\Rcode}[1]{{\texttt{#1}}}

+\newcommand{\Rparameter}[1]{{\texttt{#1}}}

+\newcommand{\Robject}[1]{{\texttt{#1}}}

+\newcommand{\Rdata}[1]{{\texttt{#1}}}

+\newcommand{\Rpackage}[1]{{\textit{#1}}}

+

+\author{Supat Thongjuea \footnote{Bergen Center for Computational Science, Bergen,  Norway}}

+

+\title{\textsf{r3Cseq: an R package for the discovery of long-range genomic interactions with 

+chromosome conformation capture and next-generation sequencing data}}

+

+\date{October 28, 2012}

+

+\begin{document}

+<<setup, echo=FALSE>>=

+options(width = 60)

+olocale=Sys.setlocale(locale="C")

+@ 

+\maketitle

+

+\tableofcontents

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\begin{abstract}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+The coupling of chromosome conformation capture (3C)-based and next-generation sequencing (NGS) enable high-throughput detection

+of long-range genomic interactions via the generation of novel ligation products between DNA sequences that are closely juxtaposed 

+in vivo. These interactions may involve promoter regions, enhacers and other regulatory and structural elements of chromosomes, and 

+can reveal key details in the regulation of gene expression. 3C-seq is a a variant of the method for the detection of interactions 

+between one chosen genomic element (viewpoint) and the rest of the genome. We present an R/Bioconductor package called \Rpackage{r3Cseq}, designed to perform 

+3C-seq data analysis in a number of different experimental designs, with or without a control experiment. The package can also be used to perform 

+data analysis for the experiment with replicates. The package provides functions to perform 3C-seq data normalization, statistical analysis for cis/trans 

+interactions and visualization to facilitate the identification of genomic regions that physically interact with the given viewpoints of interest.

+The r3Cseq package greatly facilitates hypothesis generation and the interpretation of experimental results.

+\end{abstract}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\section{Introduction}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+This vignette describes how to use the \Rpackage{r3Cseq} package. 

+\Rpackage{r3Cseq} is a Bioconductor-compliant R package designed to facilitate the 

+identification of interaction regions generated by chromosome conformation capture 

+and next-generation sequencing (3C-seq). The fundamental principles of 3C-seq briefly described in 

+the following \cite{Soler:2010} (Figure~\ref{fig:3CseqProcedures}), isolated cells are treated with a cross-linking agent 

+to preserve in vivo nuclear proximity between DNA sequences. The DNA isolated from these 

+cells is then digested using a primary restriction enzyme, typically a 6-base pairs cutting 

+enzyme such as HindIII, EcoRI or BamHI. The digested product is then ligated  under dilute conditions

+to favor intra-molecular over inter-molecular ligation events. This digested and ligated chromatin yields composite

+sequences representing (distal) genomic regions that are in close physical proximity in the cell nucleus.

+The digested and ligated chromatin is then de-crosslinked and subjected to a second restriction digest

+using either Nla III or Dpn II (a 4-cutter) as a secondary restriction enzyme to decrease the fragment sizes.

+The resulting digested DNA is then ligated again under diluted conditions, creating small circular fragments.

+These fragments are inverse PCR-amplified using primers specific for a genomic region of interest (eg. promoter,

+enhancer, or any other element potentially involved in long-range interactions), termed the "viewpoint".

+The amplified fragments are then sequenced using massively parallel high-throughput sequencing. 

+Because the 3C-seq procedure hybrid DNA molecules being a combination of viewpoint-specific  primers

+followed by sequences dervied from the ligated interaction fragments. As such, these composite sequences are

+unmappable and need to be trimmed to removed the viewpoint sequences, thus leaving only the capture sequence fragments

+for mapping. After trimming, reads are mapped against a reference genome using alignment software such as Bowtie.

+A mapped read file generated by the mapping software is then transformed to the BAM file and analyzed 

+by using \Rpackage{r3Cseq} package.  

+

+\begin{figure}

+\centering

+\includegraphics{images/3CseqProcedures.png}

+\caption{3C-seq procedures}

+\label{fig:3CseqProcedures}

+\end{figure}

+

+\Rpackage{r3Cseq} package is built on, and extends the functionality of Bioconductor package such as \Rpackage{GenomicRanges},

+\Rpackage{BSgenome}, \Rpackage{Rsamtools}, and \Rpackage{rtracklayer}. The package provides classes and 

+methods to facilitate single-end reads, which are generated by the next-generation sequencing. The package can perform 

+data analysis on both single input file (single lane from one experiment) and two input files from an experiment and a control.

+The package also provides a class and functions to perform 3C-seq data analysis from replicates (see working with replicates section).  

+The key features workflow of \Rpackage{r3Cseq} depicts in the following figure. (Figure~\ref{fig:r3CseqFlowChart})

+

+\begin{figure}

+\centering

+\includegraphics{images/r3CseqFlowChart.png}

+\caption{r3Cseq key features workflow}

+\label{fig:r3CseqFlowChart}

+\end{figure}

+

+\Rpackage{r3Cseq} analysis workflow starts from the class initialization. 

+There are two classes found in this package. One is \Robject{r3Cseq} class that 

+is designed to support a single experiment in both with and without a control experiment.

+Another is \Robject{r3CseqInBatch} class that is designed to support analysis with replicates.

+To initialize the class both \Robject{r3Cseq} and \Robject{r3CseqInBatch}, 

+a user gives the input parameters for example the input file name, 

+genome assembly version, primary restriction fragment name and so on 

+(see more details in the manual page of \Robject{r3Cseq} and \Robject{r3CseqInBatch}). 

+The class is then will be created and it is ready to perform 3C-seq analysis. 

+The \Rfunction{getRawReads} and \Rfunction{getRawReadsInBatch} functions can be next used to read in the BAM files.

+It may take a few minutes for data processing depending on the size of the input BAM files and 

+the speed of CPU and the size of the RAM of a computer that performs analysis.

+To run the \Rfunction{getRawReadsInBatch} function for replicates, a user might have a powerful computer server. 

+\Rfunction{getRawReads} function reads in aligned reads from input BAM files 

+and transforms aligned reads to the  \Robject{GRanged} objects that can be stored in the r3Cseq object, whereas 

+\Rfunction{getRawReadsInBatch} processes the data in batch and stores the aligned reads \Robject{GRanges}

+in the R files (.rdata). To count number of reads preparing for downstream analysis, \Robject{r3Cseq}

+provides two ways to count number of reads per region; 1) count number of reads per 

+resitrction fragments, using the function \Rfunction{getReadCountPerRestrictionFragment} and 2)

+count the number of reads per non-overlapping window size, using function \Rfunction{getReadCountPerWindow},

+whereas \Rfunction{getBatchReadCountPerRestrictionFragment} and \Rfunction{getBatchReadCountPerWindow} can do

+the same for replicates. \Rpackage{r3Cseq} provides \Rfunction{calculateRPM} and \Rfunction{calculateBatchRPM} 

+functions to calculate reads per million per restriction fragment size (RPM) as normalized interaction frequency values. 

+There are two methods to calculate RPM. which are described in the r3Cseq paper \cite{Supat:2012}.

+After data normalization, \Rfunction{getInteractions} and \Rfunction{getBatchInteractions} will be performed 

+to identify candidate interactions.  Statistical analysis for both cis and trans interactions is described in the 

+r3Cseq paper \cite{Supat:2012}. \Rpackage{r3Cseq} provides functions \Rfunction{export3CseqRawReads2BedGraph},

+\Rfunction{export3Cseq2bedGraph}, \Rfunction{exportInteractions2text}, and \Rfunction{exportBatchInteractions2text}

+to export raw reads and all identified interactions to the bedGraph file format, which is simply uploaded to the UCSC genome browser. 

+The package also provides functionalities for plotting to show data analysis result of interaction regions.

+Plotting functions consist of \Rfunction{plotOverviewInteractions},

+\Rfunction{plotInteractionPerChromosome},

+\Rfunction{plotInteractionNearViewpoint}, 

+and \Rfunction{plotDomainogramNearViewpoint}. 

+These functions will be demonstrated in the visualization of 3C-seq data section. 

+

+Here is a list of some of its most important functions.

+

+\begin{enumerate}

+\item

+\Rfunction{getRawReads}: a function to read in BAM files.

+\item

+\Rfunction{getBatchRawReads}: a function to read in multiple BAM files for replicates.

+\item

+\Rfunction{getReadCountPerRestrictionFragment} : a function to count the number of reads per restriction

+fragment. A user has to specify the name of restriction enzyme. The package

+will then automatically generate the genome-wide restriction fragments and counts how many 3C-seq reads

+are mapped into that particular restriction fragments.

+\item

+\Rfunction{getBatchReadCountPerRestrictionFragment} : Similar to \Rfunction{getReadCountPerRestrictionFragment}

+using it for replicates

+\item

+\Rfunction{getReadCountPerWindow} : a function to count the number of reads per defined non-overalapping window size. 

+A user has to specify the window size of interest. 

+The package will then automatically generate the genome-wide windows and counts how many 3C-seq reads

+are mapped into that particular windows.

+\item

+\Rfunction{getBatchReadCountPerWindow} : similar to \Rfunction{getReadCountPerWindow} using for replicates

+\item

+\Rfunction{calculateRPM} : a function to calcuate reads per million (RPM) per each restriction fragment

+\item

+\Rfunction{calculateBatchRPM} : similar to \Rfunction{calculateRPM} using for replicates 

+\item

+\Rfunction{getInteractions} : a function to perform statistical analysis to identify candidate interactions

+\item

+\Rfunction{getBatchInteractions} : similar to \Rfunction{getInteractions} using for replicates

+\item

+\emph{Visualiztion} : the package contains functions for visualizing the interaction regions with

+the powerful plotting facilities. These functions are \Rfunction{plotOverviewInteractions}, 

+\Rfunction{plotInteractionsNearViewpoint},\Rfunction{plotInteractionsPerChromosome}, 

+and \Rfunction{plotDomainogramNearViewpoint}.

+\item

+\emph{Data export} : the package contains functions to export the data into 

+tab-delimited text format, which can be easily uploaded to the UCSC

+genome browser for further visualization and exploration. Currently it supports

+the bedGraph format. These functions are \Rfunction{export3CseqRawReads2bedGraph},

+\Rfunction{export3Cseq2bedGraph}, \Rfunction{exportInteractions2text}, 

+\Rfunction{exportBatchInteractions2text}. The package can also generate a summary report in PDF format by 

+\Rfunction{generate3CseqReport} function.

+\end{enumerate}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\section{Preparation input files for r3Cseq}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+The required input file for \Rpackage{r3Cseq} package is the BAM file, obtained as an output from 

+the mapping software. The represented identifier for a reference genome shown in each input BAM file is important

+to run \Rpackage{r3Cseq} properly. The represented identifier for each chromosome must be

+in "chr[1..19XYM]" format for the mouse reference genome and "chr[1..22XYM]" format for the 

+human reference genome. Therefore, before using \Rpackage{r3Cseq} package, a user has to check the identifier for the reference

+genome. If the identifier for each chromosome found in the mapped file

+is not in a proper format for example 'mm9\_ref\_chr01.fa', the Unix command like 'sed' might

+be used to replace 'mm9\_ref\_chr01.fa' to 'chr1'.

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\section{Getting started}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+The 3C-seq data  generated by \cite{Ralph:2011} will be used for the demonstration. 

+The current version of r3Cseq supports mouse and human genome. 

+Therefore, the package requires one of the followings \Rpackage{BSgenome} 

+packages to be installed;\Rpackage{BSgenome.Mmusculsu.UCSC.mm9}, 

+\Rpackage{BSgenome.Hsapiens.UCSC.hg18}, and \Rpackage{BSgenome.Hsapiens.UCSC.hg19}.

+

+Loading the \Rpackage{r3Cseq} package into R.

+<<>>=

+library(r3Cseq)

+@

+There are 2 data sets found in the package.

+<<>>=

+data(Myb_prom_FL)

+data(Myb_prom_FB)

+@

+\begin{enumerate}

+\item

+\Rdata{Myb\_prom\_FL}, the 3C-seq data contains the aligned reads of

+the Myb promoter interactions signal in fetal liver. 

+It was stored in the \Robject{GRanges} object processed by the \Rpackage{Rsamtools} package.

+

+\item

+\Rdata{Myb\_prom\_FB}, the 3C-seq data contains the aligned reads of Myb promoter interactions signal in fetal brain.

+\end{enumerate}

+We will perform \Rpackage{r3Cseq} to discover interaction regions, 

+which possibly interact with the promoter region of Myb gene in both fetal liver and brain \cite{Ralph:2011}.

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\subsection{r3Cseq object initialization}

+In this section, we will analyze 3C-seq data, which were derived from fetal liver (expressing high levels of Myb)

+and fetal brain (expression low level of Myb). The latter will be used as a negative control.

+More examples of r3Cseq data analysis can be found at the official r3Cseq 

+website \url{http://r3cseq.genereg.net}. We firstly initialized the r3Cseq object.

+<<>>=

+my3Cseq.obj<-new("r3Cseq",organismName='mm9',isControlInvolved=TRUE,

+viewpoint_chromosome='chr10',viewpoint_primer_forward='TCTTTGTTTGATGGCATCTGTT',

+viewpoint_primer_reverse='AAAGGGGAGGAGAAGGAGGT',expLabel="Myb_prom_FL",

+contrLabel="MYb_prom_FB",restrictionEnzyme='HindIII')

+@

+Definition of input parameters is described in the \Robject{r3Cseq} help page.

+We next add raw reads from Myb\_prom\_FL and Myb\_prom\_FB to the existing \Robject{my3Cseq.obj}.

+<<>>=

+expRawData(my3Cseq.obj)<-exp.GRanges

+contrRawData(my3Cseq.obj)<-contr.GRanges	

+@

+Type \Robject{my3Cseq.obj} to see the r3Cseq object:

+<<>>=

+my3Cseq.obj	

+@

+\subsection{Getting reads per restriction fragments/user defined window size}

+To get number of reads per restriction fragement, function \Rfunction{getReadCountPerRestrictionFragment}

+will be performed.

+<<>>=

+getReadCountPerRestrictionFragment(my3Cseq.obj)

+@

+The package provides the function \Rfunction{getReadCountPerWindow} to count number of reads

+per non-overlapping window size defined by a user.  

+

+\subsection{Normalization}

+

+We next perform normalization. 

+<<>>=

+calculateRPM(my3Cseq.obj)	

+@

+\subsection{Getting interaction regions}

+After normalization, the \Rfunction{getInteractions} function will be performed.

+<<>>=

+getInteractions(my3Cseq.obj,fdr=0.05)	

+@

+In order to see the result of interaction regions, Two functions \Rfunction{expInteractionRegions} and \Rfunction{contrInteractionRegions} 

+need to be used to access the slot of r3Cseq object.

+To get the result of interaction regions for the experiment, \Rfunction{expInteractionRegions} will be performed.

+<<>>=

+fetal.liver.interactions<-expInteractionRegions(my3Cseq.obj)

+fetal.liver.interactions

+@

+To get the result of interaction regions for the control, \Rfunction{contrInteractionRegions} will be performed.

+<<>>=

+fetal.brain.interactions<-contrInteractionRegions(my3Cseq.obj)

+fetal.brain.interactions

+@

+\subsection{Getting the viewpoint information}

+To see the viewpoint information,  \Rfunction{getViewpoint} function can be used. 

+\Rfunction{getViewpoint} will return the RangedData object of the viewpoint information.

+<<>>=

+viewpoint<-getViewpoint(my3Cseq.obj)

+viewpoint

+@

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\section{Visualization of 3C-seq data}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\Rpackage{r3Cseq} package provides visualization functions.

+These functions are \Rfunction{plotOverviewInteractions}, \Rfunction{plotInteractionsNearViewpoint},

+\Rfunction{plotInteractionsPerChromosome}, and \Rfunction{PlotDomainogramNearViewpoint}.

+

+\subsection{The overview plot of interactions}

+\Rfunction{plotOverviewInteractions} function shows the overview of interaction regions distributed across genome.

+

+<<plotOverviewInteractions,fig=TRUE,height=8,width=12,eps=FALSE,include=FALSE>>=

+plotOverviewInteractions(my3Cseq.obj)	

+@

+\begin{figure}

+\centering

+\includegraphics{r3Cseq-plotOverviewInteractions}

+\caption{Distribution of interaction regions across genome}

+\end{figure}

+

+\subsection{Plot of interactions in cis}

+\Rfunction{plotInteractionsNearViewpoint} function shows the zoom in of interaction regions located close to the viewpoint.

+<<plotInteractionsNearViewpoint,fig=TRUE,height=8,width=12,eps=FALSE,include=FALSE>>=

+plotInteractionsNearViewpoint(my3Cseq.obj)	

+@

+\begin{figure}

+\centering

+\includegraphics{r3Cseq-plotInteractionsNearViewpoint}

+\caption{Zoom in interaction regions near the viewpoint}

+\end{figure}

+

+\subsection{Plot of interactions in each selected chromosome}

+\Rfunction{plotInteractionsPerChromosome} function shows the interaction regions found in the chromosome10.

+

+<<plotInteractionsPerChromosome,fig=TRUE,height=8,width=12,eps=FALSE,include=FALSE>>=

+plotInteractionsPerChromosome(my3Cseq.obj,"chr10")	

+@

+

+\begin{figure}

+\centering

+\includegraphics{r3Cseq-plotInteractionsPerChromosome}

+\caption{Distribution of interaction regions across chromosome 10}

+\end{figure}

+

+\subsection{Domainogram of interactions}

+\Rfunction{plotDomainogramNearViewpoint} function shows the domainogram of interactions found in cis. 

+This function may takes several minutes to produce domainograms. We therefore, skip this command to produce plots for the vignette.

+You can see the example of the plots and find more details at \url{http://r3Cseq.genereg.net}.

+<<>>=

+#plotDomainogramNearViewpoint(my3Cseq.obj)

+@

+\subsection{Associate interaction signals to the Refseq genes}

+\Rfunction{getExpInteractionsInRefseq} and \Rfunction{getContrInteractionsInRefseq} functions can be used to detect the list of

+genes that contain significant interaction signals in their proximity.

+<<>>=

+detected_genes<-getExpInteractionsInRefseq(my3Cseq.obj)

+head(detected_genes)

+@

+\subsection{Export interactions to the bedGraph format}

+\Rfunction{export3Cseq2bedGraph} function exports all interactions from the

+\Robject{RangedData} to the bedGraph format, which simply upload to the UCSC 

+genome browser.

+<<>>=

+export3Cseq2bedGraph(my3Cseq.obj)	

+@

+\subsection{Summary report}

+\Rfunction{generate3CseqReport} function generates the summary report

+from \Rpackage{r3Cseq} analysis results. The report contains

+a pdf file for all plots and text files of interaction regions.

+This function may takes several minutes to produce the report. 

+We therefore, skip this command during the vignette creation.

+<<>>=

+#generate3CseqReport(my3Cseq.obj)	

+@

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\section{Working with replicates}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+The example of how to work with replicats can be found at

+\url{http://r3cseq.genereg.net/}.

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\section{r3Cseq website}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+We have developed the website \url{http://r3cseq.genereg.net}. 

+The website provides more details of \Rpackage{r3Cseq} analysis pipeline.

+The example data sets and the current version of \Rpackage{r3Cseq}

+package can be downloaded from the website. 

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+\section{Session Info}

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+<<>>=

+sessionInfo()

+@

+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

+%\newpage

+\bibliographystyle{apalike}

+\bibliography{r3Cseq}

+\end{document}