%\VignetteDepends{}
%\VignetteIndexEntry{ChIP-Seq}
%\VignettePackage{EMBL2010}

\documentclass{beamer}

\usepackage{BioconductorSlides}
\AtBeginSection[]
{
  \begin{frame}<beamer>{Outline}
    \tableofcontents
  \end{frame}
}

\usepackage[authoryear,round]{natbib}
\bibliographystyle{plainnat}
\SweaveOpts{keep.source=TRUE}

\title{ChIP-Seq Concepts and Applications}
\author{Martin Morgan}
\institute[FHCRC]{Fred Hutchinson Cancer Research Center} 
\date{14-18 June, 2010}

\begin{document}

\frame{\titlepage}

\section{Experimental Setup}

\subsection{ChIP-Seq}

\begin{frame}{Chromatin Immunoprecipitation}
  \begin{columns}
    \column{.5\textwidth}
    \begin{itemize}
    \item Cross-link
    \item Cell lysis and fragmentation
      \begin{itemize}
      \item Sonicate (transcription factors)
      \item Micrococcal nuclease (nucleosomes)
      \end{itemize}
    \item Enrichment by immuno-precipitation
      \begin{itemize}
      \item Antibody $+$ magnetic beads
      \item DNA purification
      \end{itemize}
    \item Adaptor-mediated PCR amplification
    \end{itemize}
    \column{.5\textwidth}
    \includegraphics[height=.7\textheight,width=!]{Barski-Zhao-2009-1A}
    \par\cite{pmid19173299}
  \end{columns}
\end{frame}

\begin{frame}{Experimental Setup: ChIP and Seq}
  \begin{center}
    \includegraphics[width=\textwidth]{chip}
    \par\cite{Park}
  \end{center}
\end{frame}

\subsection{Biological Questions}

\begin{frame}{Biological Questions: Nucleosomes}
  \begin{columns}
    \column{.5\textwidth}
    Example: Human CD4${}^+$T cells
    \begin{itemize}
    \item Phasing tightly correlated with Pol II binding
    \item Differential positioning of first nucleosome
    \end{itemize}
    \column{.5\textwidth}
    \includegraphics{Nucleosome-1}\par
    \includegraphics{Nucleosome-2}
    \par\cite{Schones2008887}
  \end{columns}
\end{frame}

\begin{frame}{Biological Questions: Transcription Enhancers}
  \begin{columns}
    \column{.5\textwidth} Example: tissue-specific enhancers in mouse
    embryonic forebrain
    \begin{itemize}
    \item ChIP of enhancer associated protein p300 identifies 1000's
      of binding sites
    \item \emph{In vivo} effects reproducible in transgenic mice
    \item Enriched binding near expressed genes
    \end{itemize}
    \column{.5\textwidth}
    \includegraphics[width=\textwidth, height=!]{Visel-et-al}
    \par\cite{pmid19212405}
  \end{columns}
\end{frame}

\subsection{Experimental Designs}

\begin{frame}{Experimental Designs}
  \begin{columns}
    \column{.5\textwidth}
    \begin{itemize}
    \item Single sample
    \item Control lane \citep{pmid19736561}
      \begin{itemize}
      \item `Input' control (DNA prior to IP)
      \item Mock IP (no antibodies)
      \item Non-specific IP
      \end{itemize}
    \item Designed experiment -- factor(s) with 2 or more levels.
    \end{itemize}
    \column{.5\textwidth} 
    ``ChIP experiment depends on many intractable parameters, likely
    including\ldots phase of the moon'' \citep{pmid19173299}
  \end{columns}
\end{frame}

\begin{frame}{Depth of Coverage}
    Heuristic, modified from \cite{pmid19173299}
    \[ S \propto \frac{G}{W} \times \frac{N}{E} \]
    \begin{description}
    \item [S] Amount of sequencing
    \item [G] Non-repetitive genome size
    \item [W] Window size (resolution)
    \item [N] Fraction of genome after `ChIP'
    \item [E] Enrichment factor (antibody immunoprecipitation)
    \end{description}
    \begin{itemize}
    \item $E$ implies that the input lane is well-characterized, and
       this implies extensive sequencing of the input, where $N$ is
      large
    \end{itemize}
\end{frame}

\begin{frame}{Depth of Coverage}
  \begin{columns}
    \column{.5\textwidth}
    \includegraphics[width=\textwidth,height=!]{Kharchenko-Power}
    \par\cite{Park}
    \column{.5\textwidth}
    \begin{itemize}
    \item No saturation at specified FDR: more peaks continually
      found, because larger read counts increase statistical power
    \item Imposing a fixed fold enrichment criterion established
    \item Multiplexing increasingly attractive, especially for small
      genomes / well defined ChIP targets
    \end{itemize}
  \end{columns}
\end{frame}

\begin{frame}{Sequencing}
  Longer reads
  \begin{itemize}
  \item Better mapping in repetitive regions
  \end{itemize}
  Paired end reads
  \begin{itemize}
  \item Easier transcription factor binding site identification?
  \item Better mapping (on the borders of) repetitive regions?
  \end{itemize}
\end{frame}

\section{Pre-Processing \& Analysis}

\subsection{Alignment and Quality Assessment}

\begin{frame}{Pre-Processing: Alignment \& Quality Assessment}
  Library construction
  \begin{itemize}
  \item Under-representation of AT-rich regions with low melting
    temperature, GC-rich regions due to PCR bias
  \item MNase sequence preference (nucleosomes)
  \item Antibody variability
  \item Optical and PCR duplicate reads
  \end{itemize}
  Alignment
  \begin{itemize}
  \item Micro-repetitive genomic regions (non-alignment)
  \item Non-specific enrichment, often reads on a single strand
  \end{itemize}
  \cite{pmid19173299}, \cite{pmid19736561}
\end{frame}

\begin{frame}{Analysis}
  Simple
  \begin{itemize}
  \item View aligned reads in a browser (or \Biocpkg{HilbertVisGUI}!)
  \end{itemize}
  Binned
  \begin{itemize}
  \item Divide genome into bins
  \item Identify bins with greater-than-expected (e.g., Poisson)
    counts
  \end{itemize}
  Model-based
  \begin{itemize}
  \item Exploit $+/-$ strand asymmetry to more narrowly define binding
    sites
  \end{itemize}
  \cite{pmid19844228} and \cite{pmid19275939} offer comprehensive
  enumerations of available software
\end{frame}

\subsection{Peak Identification \& Quantification}

\begin{frame}{Peak Identification}
  \begin{columns}
    \column{.5\textwidth}
    Strand asymmetry
    \begin{itemize}
    \item 5' end of fragments
    \item Distinct distributions on $+$, $-$ strands
    \end{itemize}
    Smoothed profile
    \begin{itemize}
    \item Shift each distribution toward center, or\ldots
    \item Extend each read by estimated fragment length
    \item Other algorithms: \cite{Park}
    \end{itemize}
    \column{.5\textwidth}
    \includegraphics[height=.7\textheight, width=!]{Park-2009-peaks}
    \par\cite{pmid19736561}
  \end{columns}
\end{frame}

\begin{frame}{Peak Quantification}
  Fold ratio
  \begin{itemize}
  \item Enrichment relative to control
  \item But: 5-fold change from 10 to 50 has different statistical
    significance from 100 to 500
  \end{itemize}
  Model-based
  \begin{itemize}
  \item Poisson (or other) description of count data, e.g.,
    \software{MACS}, \cite{pmid18798982}
  \item Adjust for regional bias in tag density from library
    construction / micro-repetitive regions, e.g.,
    \software{PeakSeq}, \cite{pmid19122651}
  \end{itemize}
\end{frame}

\begin{frame}{An Example: MACS, \cite{pmid18798982}}
  Pre-processing
  \begin{itemize}
  \item Remove duplicate tags if more than expected based on sequence
    depth
  \item Scale control lane to same total tag count as experiment
  \end{itemize}
  Peak identification
  \begin{itemize}
  \item Use `high-quality' peaks to estimate fragment width $d$
  \item Shift all peaks $d/2$ toward 3' end
  \end{itemize}
  Peak quantification
  \begin{itemize}
  \item Whole genome Poisson expectation $\lambda_{BG}$, local
    processes based on control $\lambda_{1k},\lambda_{5k},
    \lambda_{10k}$; 
  \item Local Poisson process $\lambda_{\mathrm{local}} =
    \mathrm{max}(\lambda_{BG}, \lambda_{1k},\lambda_{5k},
    \lambda_{10k})$
  \item Score is Poisson probability based on
    $\lambda_{\mathrm{local}}$
  \end{itemize}
\end{frame}

\begin{frame}{Peak Problems}
  Three types of peak
  \begin{itemize}
  \item Sharp: protein-DNA binding; histone modification of regulatory
    elements
  \item Broad: histone modifications marking domains
  \item Mixed
  \end{itemize}
  Algorithms generally satisfactory for sharp peaks; adopt \emph{ad
    hoc} approaches for broad / mixed peaks
\end{frame}

\subsection{Additional Considerations}

\begin{frame}{Additional Considerations}
  Assessing algorithm performance
  \begin{itemize}
  \item Validate using quantitative PCR
  \item Distribution of distances from peaks to nearest known motif
  \end{itemize}
  Statistical significance
  \begin{itemize}
  \item Adjusted to reflect multiple comparisons
  \item Usually reported as false discovery rate
  \item Requires realistic null model, e.g., capturing local
    variations in input control
  \end{itemize}
\end{frame}

\begin{frame}{Designed Experiments}
  Much like microarray data
  \begin{itemize}
  \item Rectangular data; `features' $\times$ `samples'
  \item Easier to compare across samples than features (?)
  \end{itemize}
  Important application-specific issues
  \begin{itemize}
  \item Counts: distinct properties require appropriate error model
  \item Measurement `features' discovered rather than determined
    \emph{a priori}
  \end{itemize}
\end{frame}

\section{Annotation and Integration}

\begin{frame}{Annotation and Integration}
  Annotation
  \begin{itemize}
  \item Relate peak locations to known genomic features, e.g.,
    transcription start sites
  \item Gene set enrichment-style analyses
  \item Motif discovery from high-scoring peaks
  \end{itemize}
  Integration
  \begin{itemize}
  \item Expression levels of genes
  \item SNPs and allele-specific binding
  \end{itemize}
\end{frame}

\section{Resources}

\begin{frame}{Resources}
  \Bioconductor{} packages
  \begin{itemize}
  \item \Biocpkg{BayesPeak}, \Biocpkg{CSAR}, \Biocpkg{chipseq}
  \item \Biocpkg{ChIPpeakAnno}
  \item \Biocpkg{MotIV}, \Biocpkg{rGADEM}
  \item \Biocpkg{ChIPsim}
  \end{itemize}
\end{frame}

\begin{frame}[allowframebreaks]{References}
  \def\newblock{}\bibliography{ChIP-Seq}
\end{frame}

\end{document}
