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\title{High-throughput sequence analysis with \R{} and
  \Bioconductor{}} \author{Martin
  Morgan\footnote{\href{mailto:mtmorgan@fhcrc.org}{mtmorgan@fhcrc.org}},
  Herv\'e
  Pag\`es\footnote{\href{mailto:hpages@fhcrc.org}{hpages@fhcrc.org}}}
\date{August 15, 2011}

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\begin{document}

\maketitle

\tableofcontents

\section{Introduction}

\subsection{\Bioconductor{}}

\Bioconductor{} is a collection of \R{} packages for the analysis and
comprehension of high-throughput genomic data. \Bioconductor{} started
more than 10 years ago. It gained credibility for its statistically
rigorous approach to microarray pre-preprocessing and designed
experiments, and integrative and reproducible approaches to
bioinformatic tasks. There are now more than 460 \Bioconductor{}
packages for expression and other microarrays, sequence analysis, flow
cytometry, imaging, and other domains.  The \Bioconductor{}
\href{http://bioconductor.org}{web site} provides installation,
package repository, help, and other documentation.

\subsection{High-throughput sequence analysis}

Recent technological developments introduce high-throughput sequencing
approaches. A variety of experimental protocols and analysis work
flows address gene expression, regulation, and encoding of genic
variants. Experimental protocols produces a large number (millions per
sample) of short (e.g., 35-100, single or paired-end) nucleotide
sequences. These are aligned to a reference or other genome. Analysis
work flows use the alignments to infer levels of gene expression
(RNA-seq), binding of regulatory elements to genomic locations
(ChIP-seq), or prevalence of structural variants (e.g., SNPs, short
indels, large-scale genomic rearrangements). Sample sizes range from
minimal replication (e.g,. 2 samples per treatment group) to thousands
of individuals.

\subsection{This workshop}

This workshop introduces use of \R{} and \Bioconductor{} for analysis
of high-throughput sequence data. The workshop is structured as a
series of short remarks followed by group exercises. The exercises
explore the diversity of tasks for which \R{} / \Bioconductor{} are
appropriate for, but are far from comprehensive.

The goals of the workshop are to: (1) develop familiarity with \R{} /
\Bioconductor{} software for high-throughput analysis; (2) expose key
statistical issues in the analysis of sequence data; and (3) provide
inspiration and a framework for further independent exploration.

\include{RBioc}

\include{RangesAndStrings}

\include{DataExploration}

\include{RNASeq}

\include{Annotation}

\include{Appendix}

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\bibliography{References}

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