\documentclass[portrait,final]{baposter}
%\documentclass[a4shrink,portrait,final]{baposter}
% Usa a4shrink for an a4 sized paper.

\tracingstats=2

%\usepackage{beamerarticle}
\usepackage{multicol}
\usepackage[english]{babel}
\usepackage{blindtext}

\usepackage{times}
\usepackage{calc}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{relsize}
\usepackage{multirow}
\usepackage{subfigure}
\usepackage{ctable}
\usepackage{bm}

\usepackage{enumitem}

\usepackage{graphicx}
\usepackage{multicol}

\usepackage{pgfbaselayers}
\pgfdeclarelayer{background}
\pgfdeclarelayer{foreground}
\pgfsetlayers{background,main,foreground}

\usepackage{helvet}
%\usepackage{bookman}
\usepackage{palatino}

\newcommand{\captionfont}{\footnotesize}

\selectcolormodel{cmyk}

\graphicspath{{image/}}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% Some math symbols used in the text
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Format 
\newcommand{\Matrix}[1]{\begin{bmatrix} #1 \end{bmatrix}}
\newcommand{\Vector}[1]{\Matrix{#1}}
\newcommand*{\SET}[1]  {\ensuremath{\mathcal{#1}}}
\newcommand*{\MAT}[1]  {\ensuremath{\mathbf{#1}}}
\newcommand*{\VEC}[1]  {\ensuremath{\bm{#1}}}
\newcommand*{\CONST}[1]{\ensuremath{\mathit{#1}}}
\newcommand*{\norm}[1]{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
\newcommand*{\abs}[1]{\mathopen| #1 \mathclose|}% use instead of $\|x\|$
\newcommand*{\absLR}[1]{\left| #1 \right|}% use instead of $\|x\|$

\def\norm#1{\mathopen\| #1 \mathclose\|}% use instead of $\|x\|$
\newcommand{\normLR}[1]{\left\| #1 \right\|}% use instead of $\|x\|$

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Multicol Settings
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\setlength{\columnsep}{0.7em}
\setlength{\columnseprule}{0mm}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Save space in lists. Use this after the opening of the list
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\newcommand{\compresslist}{%
\setlength{\itemsep}{1pt}%
\setlength{\parskip}{0pt}%
\setlength{\parsep}{0pt}%
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Begin of Document
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{document}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Here starts the poster
%%%---------------------------------------------------------------------------
%%% Format it to your taste with the options
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Define some colors

%\definecolor{silver}{cmyk}{0,0,0,0.3}
%\definecolor{yellow}{cmyk}{0,0,0.9,0.0}
%\definecolor{reddishyellow}{cmyk}{0,0.22,1.0,0.0}
%\definecolor{black}{cmyk}{0,0,0.0,1.0}
%\definecolor{darkYellow}{cmyk}{0,0,1.0,0.5}
%\definecolor{darkSilver}{cmyk}{0,0,0,0.1}

%\definecolor{lightyellow}{cmyk}{0,0,0.0,0.0}
%\definecolor{lighteryellow}{cmyk}{0,0,0.1,0.0}
%\definecolor{lighteryellow}{cmyk}{0,0,0.1,0.0}
%\definecolor{lightestyellow}{cmyk}{0,0,0.05,0.0}


%\definecolor{silver}{cmyk}{0,0,0,0.3}
%\definecolor{yellow}{cmyk}{0.2,0.2,0.8,0.0}
%\definecolor{reddishyellow}{cmyk}{0,1,0.0,0.8}
%\definecolor{black}{cmyk}{0,0,0.0,1.0}
%\definecolor{darkYellow}{cmyk}{0,1.0,0.0,0.5}
%\definecolor{darkSilver}{cmyk}{0,0,0,0.1}

%\definecolor{lightyellow}{cmyk}{0,0,0.0,0.0}
%\definecolor{lighteryellow}{cmyk}{0,0.1,0.,0.0}
%\definecolor{lightestyellow}{cmyk}{0,0.05,0.0,0.0}


\definecolor{silver}{cmyk}{0,0,0,0.3}
%\definecolor{yellow}{cmyk}{0.9,0.0,0,0.1}
\definecolor{yellow}{cmyk}{1,0.0,0,0.1}

%\definecolor{reddishyellow}{cmyk}{1.0,0.22,0.0,0.0}
\definecolor{reddishyellow}{cmyk}{1.0,0.0,0.0,0.6}


\definecolor{black}{cmyk}{0,0,0.0,1.0}
\definecolor{darkYellow}{cmyk}{1.0,0.0,0.0,0.5}
\definecolor{darkSilver}{cmyk}{0,0,0,0.1}

\definecolor{lightyellow}{cmyk}{0,0,0.0,0.0}
\definecolor{lighteryellow}{cmyk}{0.1,0,0.0,0.0}
%\definecolor{lightestyellow}{cmyk}{0.05,0,0.0,0.0} %Hintergrund
\definecolor{lightestyellow}{cmyk}{0.00,0,0.0,0.0}

%%
\typeout{Poster Starts}
\background{
  \begin{tikzpicture}[remember picture,overlay]%
    \draw (current page.north west)+(-2em,2em) node[anchor=north west] {\includegraphics[height=1.1\textheight]{silhouettes_background}};
  \end{tikzpicture}%
}

\newlength{\leftimgwidth}
\begin{poster}%
  % Poster Options
  {
  % Show grid to help with alignment
  grid=no,
  % Column spacing
  colspacing=1em,
  % Color style
%  bgColorOne=lightyellow,
  bgColorOne=lightestyellow,
  bgColorTwo=lightestyellow,
  borderColor=reddishyellow,
  headerColorOne=yellow,
  headerColorTwo=reddishyellow,
  headerFontColor=black,
  boxColorOne=lightyellow,
  boxColorTwo=lighteryellow,
  % Format of textbox
  textborder=roundedleft,
  % Format of text header
  %eyecatcher=no,
  headerborder=open,
 % headerheight=0.12\textheight,
 headerheight=0.12\textheight,
  headershape=roundedright,
  headershade=plain,
  headerfont=\Large\textsf, %Sans Serif
  boxshade=plain,
%  background=shade-tb,
  background=plain,
  linewidth=2pt  
  }
  % Eye Catcher
 {\includegraphics[scale=0.18]{url}} % No eye catcher for this poster. (eyecatcher=no above). If an eye catcher is present, the title is centered between eye-catcher and logo.
  % Title
  {\sf %Sans Serif
  %\bf% Serif\vspace{0.3em}
\vspace{-0.3em}
  \textbf{
	NextGenMap:\\ \vspace{0.3em}
	{\Large An ultra fast and sensitive tool for aligning \\Next Generation Sequencing reads}
	}}
  % Authors
  {\sf %Sans Serif
  % Serif
\vspace{0.3em}
 \begin{small} Philipp Rescheneder, Fritz Sedlazeck, Arndt von Haeseler\\                                                                       \end{small}

  \scriptsize  
\vspace{0.3em}
\textbf{Contact: philipp.rescheneder@univie.ac.at}\\
\vspace{0.3em}
  \begin{tiny}Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, Dr.-Bohr-Gasse 9, A-1030 Vienna, Austria                                                                                                                                                                                                                                                 \end{tiny}
	
  }
  % University logo
  {% The makebox allows the title to flow into the logo, this is a hack because of the L shaped logo.
    \makebox[10em][r]{%
      \begin{minipage}{12em}
        \includegraphics[height=5em]{cibiv-20cm-high}\\
	%\hfill
	\vfill
  %      \includegraphics[height=3.0em]{dfg_logo}
      \end{minipage}
    }
  }

  \tikzstyle{light shaded}=[top color=baposterBGtwo!30!white,bottom color=baposterBGone!30!white,shading=axis,shading angle=30]

  % Width of left inset image
     \setlength{\leftimgwidth}{0.78em+8.0em}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Now define the boxes that make up the poster
%%%---------------------------------------------------------------------------
%%% Each box has a name and can be placed absolutely or relatively.
%%% The only inconvenience is that you can only specify a relative position 
%%% towards an already declared box. So if you have a box attached to the 
%%% bottom, one to the top and a third one which should be in between, you 
%%% have to specify the top and bottom boxes before you specify the middle 
%%% box.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %
    % A coloured circle useful as a bullet with an adjustably strong filling
    \newcommand{\colouredcircle}[1]{%
      \tikz{\useasboundingbox (-0.2em,-0.32em) rectangle(0.2em,0.32em); \draw[draw=black,fill=baposterBGone!80!black!#1!white,line width=0.03em] (0,0) circle(0.18em);}}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{Abstract}}{name=intro,column=0,span=2,row=0}{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



\setlength{\columnseprule}{1pt}
\setlength{\columnsep}{20pt}
\begin{multicols}{2}[]
Due to the increasing throughput of Next Generation Sequencing technologies, mapping reads to a reference genome requires highly efficient software. 

Here we present \textbf{NextGenMap}, a flexible and fast read mapping program that is more than twice as fast as BWA while achieving a mapping sensitivity similar to Stampy. NextGenMap uses a memory efficient index structure (hash table) to store the positions of all 13-mers present in the reference genome. This index enables a quick identification of potential mapping regions for every read. Unlike other methods, NextGenMap dynamically determines for each read individually how many of the potential mapping regions have to be evaluated by a pairwise sequence alignment. Moreover, NextGenMap uses fast SIMD instructions (SSE) to accelerate the alignment calculations on the CPU. If available NextGenMap calculates the alignments on the GPU (using OpenCL/CUDA) resulting in a runtime reduction of another 20 - 50 \%.

Our results show that NextGenMap using only the CPU is on average twice as fast as BWA. Using the GPU for the alignment calculations increases the speedup to a factor of three.  NextGenMap (GPU) even outperforms Bowtie2 by 10 - 20 \% in terms of runtime. More importantly, the number of correctly mapped reads is similar to Stampy, one of the most sensitive methods available. 
\end{multicols}


%Due to the increasing throughput of Next Generation Sequencing technologies, mapping reads to a reference genome requires highly efficient software. 

%%There are two main categories of read mapping programs. (1) Tree based methods (e.g. BWA) are very fast but optimized for short Illumina reads and genomes with a low number polymorphisms. (2) Hash or pairwise sequence alignment based methods (e.g. SHRiMP2)  are suited for longer reads and a higher number of differences between reads and the reference genome and thus map more reads. However, this comes at the cost of an increased runtime.

%Here we present NextGenMap a flexible and fast read mapping program that matches the speed of tree based methods while achieving a mapping sensitivity similar to hash based methods. NextGenMap uses a memory efficient index structure (hash table) to store the positions of the 13-mers present in the reference genome. This index enables a quick identification of potential mapping regions for every read. To evaluate these regions NextGenMap uses pairwise sequence alignments. If available NextGenMap calculates the alignments on the GPU (using OpenCL/CUDA).

%Our results show that NextGenMap using only the CPU is on average twice as fast as BWA. Using the GPU for the alignment calculations increases the speedup to a factor of three.  NextGenMap (GPU) even outperforms Bowtie2 by 10 - 20 \% in terms of runtime. Moreover, the number of correctly mapped reads is similar to Stampy, one of the most sensitive methods. 




% High throughput sequencing provides access to important information on genes, their function and their genetic variation in genomes. The avalanche of newly generated information per run is enormous and will even further increase in the next future. To analyse the data it seems inevitable to invest in expensive high performance computing. A less expensive and more economic way is the application of modern computing technology adapted to special problems. Here we demonstrate the practical use of the graphic card as an inexpensive coprocessor unit, which outperform a multi core PC, by costs and run time. This allows every biologist to run its own simulations and analysis on next generation sequencing data and being independent on expensive hardware infrastructure.
% 
% We compare our implementation of the Smith-Waterman local optimal alignment to current assembly programs with respect to speed and mapping precision. For the latter, a new evaluation schema is presented, which evaluates the mapping precision for each nucleotide per read. This down to the base-pair evaluation allows a finer evaluation of reference mappings and helps to recognize potential biases and also to visualize the quality of the data.



  \vspace{0.1em}
 }

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{NextGenMap (1)}}{name=nextgenmap,column=0, span=1, below=intro}{

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

\vspace{0.4em}

\begin{small}
\begin{centering}
     \includegraphics[width=0.95\linewidth]{workflow}\\
\end{centering}

\vspace{0.4em}

NextGenMap consists of three main steps: 
\begin{description}[leftmargin=0.5cm] 
 \item[Step 1:] Indexing the reference genome (once)
 \item[Step 2:] Identification of possible mapping regions on the reference genome (candidate search)
 \item[Step 3:] Computation of alignment scores for all regions found in step 1 
 \item[Step 4:] Computation of the full alignment for the best scoring region
\end{description}

%(1) First, NextGenMap identifies possible mapping regions on the reference genome that show a high similarity to a given read. (2) Then, alignment scores are calculated for all regions found in step 1. (3) Finally, the full alignment is computed for the best scoring region and reported as result.
\end{small}

\vspace{0.3em}

}

 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{Indexing the genome (2)}}{name=index,column=1, span=1, below=intro}{

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

\vspace{0.6em}

\begin{small}
\begin{centering}
     \includegraphics[width=1\linewidth]{NgmHashTableTemplate_15}\\
\end{centering}

\vspace{0.3em}
\textbf{Step 1:}
\begin{itemize}[leftmargin=0.5cm]
 \item Determine the frequency of each 13-mer in the reference genome
 \item Create an optimal hash-table layout based on the frequencies
 \item Store the locations of all 13-mers in the hash-table
\end{itemize}
This procedure results in a highly reduced memory usage. For example only 3.3 GB are required for the index of the human genome.
\end{small}

\vspace{0.8em}

}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{Candidate search (3)}}{name=dist,column=0, span=2, below=nextgenmap}{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\vspace{1.5em}

\begin{small}
 
\begin{centering}
\begin{minipage}{0.33\linewidth}
  \centering
  \includegraphics[width=0.9\linewidth]{CandidateSearchTemplate_8}
\end{minipage}%
\begin{minipage}{0.33\linewidth}
  \centering
  \includegraphics[width=0.9\linewidth]{easy_read_3}
\end{minipage}%
\begin{minipage}{0.33\textwidth}
  \centering
  \includegraphics[width=0.9\linewidth]{hard_read_3}
\end{minipage}
\end{centering}

\vspace{1.5em}

During \textbf{Step 2} (candidate search) NextGenMap retrieves the genomic positions of all 13-mers of a given read from the index. A small hash-table finds short regions on the genome where the number of matching 13-mers from the read exceeds a threshold $t$. For other programs (e.g. SHRiMP2) $t$ is \textcolor{red}{fixed for all reads (red)}. In contrast, NextGenMap \textcolor{green}{estimates $t$ individually for each read (green)} based on the number of 13-mers $b$ that match to the best matching position (the position with the highest number of 13-mer matches) on the genome: $$t = b * s, 0 \le s \le 1$$
This reduces the number of possible mapping locations by a factor of 10.
\end{small}

\vspace{0.4em}

}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  

 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{Alignment computation (4)}}{name=alignment,row=0, column=2,span=1}{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{small}
\begin{centering}
     \includegraphics[width=0.85\linewidth]{mason}\\
\end{centering}
\vspace{0.3em}
To compute the alignment scores for \textbf{Step 3} and the full alignment for \textbf{Step 4} NextGenMap uses MASon, a C++ library that exploits graphic cards and CPU vector instructions to efficiently process billions of short pairwise sequence alignments. 
\end{small}
\vspace{0.1em}
}
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{Comparison: simulated data}}{name=simdata,row=0, column=2,span=1, below=alignment}{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

	
\begin{centering}
\begin{normalsize}
 \textbf{Human: 72bp reads}\\
\end{normalsize}
\begin{tiny}	
	10 million reads, SNP rate 0.1 \%, sequecing error $\sim$ 1 \%
\end{tiny}

\vspace{0.3em}

 \resizebox{1\textwidth}{!}{%
	\begin{tabular}{lcccc}  
	  \textbf{Program} & \textbf{Time} & \textbf{Correct (\%)} & \textbf{Wrong (\%)} & \textbf{Not mapped(\%)}\\
	\toprule
	  BWA & 30:34 & 93.51 & \textbf{3.57} & 2.92 \\	  
	  Bowtie2 & 12:48 & 95.03 & 4.69 & \textbf{0.28}\\
	  Stampy & 02:28:41 & 94.35 & 3.89 & 1.76 \\	
	  NextGenMap & \textbf{08:06}/08:44 & \textbf{95.41} & 3.99 & 0.60\\  
	\end{tabular}
}

	\vspace{0.3em}
	
\begin{tiny}
	
\end{tiny}
\begin{normalsize}
 \textbf{Human: 150bp reads}\\
\end{normalsize}
\begin{tiny}	
	10 million reads, SNP rate 0.1 \%, \textbf{sequecing error $\sim$  2\%}
\end{tiny}

\vspace{0.3em}

	\centering
	\resizebox{1\textwidth}{!}{%
	\begin{tabular}{lcccccc}  
	  \textbf{Program} & \textbf{Time} & \textbf{Correct (\%)} & \textbf{Wrong (\%)} & \textbf{Not mapped (\%)}\\
	\toprule
	  BWASW & 01:35:48 & \textbf{98.30} & 1.68 & 0.02 \\	  
	  Bowtie2 & 31:08 & 97.64 & 2.06 & 0.30\\
	  Stampy & 08:03:29 & 97.79 & 2.10 & 0.11 \\	  
	  NextGenMap & \textbf{15:43}/17:49 & 98.09 & 1.89 & 0.02\\
	\end{tabular}
	}

\begin{normalsize}
 \textbf{Drosophila: 72bp reads}\\
\end{normalsize}
\begin{tiny}		
	38 million reads, \textbf{SNP rate 1 \%}, sequecing error $\sim$ 1\%
\end{tiny}

\vspace{0.3em}

	\centering
	\resizebox{1\textwidth}{!}{%
	\begin{tabular}{lcccccc}  
	  \textbf{Program} & \textbf{Time} & \textbf{Correct (\%)} & \textbf{Wrong (\%)} & \textbf{Not mapped (\%)}\\
	\toprule
	  BWA & 41:40 & 79.29 & 16.29 & 4.42 \\	  
	  Bowtie2 & 42:02 & 82.02 & 16.99 & 0.99\\
	  Stampy & 03:21:22 & 82.95 & \textbf{15.49} & 1.56 \\	  
	  NextGenMap & \textbf{13:06}/18:17 & \textbf{82.99} & 16.86 & \textbf{0.15}\\
	\end{tabular}
	}
	

\end{centering}

\vspace{0.3em}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{Comparison: real human data}}{name=realdata,row=0, column=2,span=1, below=simdata}{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{centering}
\begin{minipage}{0.5\linewidth}
  \centering
  \includegraphics[angle=-0,width=0.9\linewidth]{real_data_mapped_backup}
\end{minipage}%
\begin{minipage}{0.5\linewidth}
  \centering
  \includegraphics[angle=0,width=0.9\linewidth]{real_data_time_backup}
\end{minipage}%

\begin{tiny}
 \textbf{Runtime (hh:mm:ss)}\\
\end{tiny}
\vspace{0.3em}
 \resizebox{1.0\textwidth}{!}{%
	  \begin{tabular}{lcccccc}  
	    \textbf{Program} & \textbf{36bp} & \textbf{76bp} & \textbf{101bp} & \textbf{Ion} & \textbf{454} \\
	    (\#reads)        & (30m) & (10m) & (10m) & (610k) & (5.5m)\\
	    \toprule
	    Bowtie2 & 14:13 & 11:30 & 17:49 & 31:37 & 1:10:05 \\
	    BWA & 29:24 & 19:16 & 1:15:00 & 3:04:14 & 56:38 \\
	    BWA-SW & 35:25 & 34:42 & 48:07 & 1:44:20 & 51:03 \\
	    Stampy & 4:29:52 & 2:07:46 & 4:21:50 & 13:32:09 & 4:50:20 \\	  	    	    
	    NextGenMap (CPU) & 14:44 & 8:22 & 31:25 & 28:54 & 33:57 \\
	    NextGenMap (GPU) & \textbf{11:10} & \textbf{7:09} & \textbf{17:37} & \textbf{19:15} & \textbf{13:26} \\	    	      
	  \end{tabular}
	  }
	  \end{centering}
\vspace{0.3em}
}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  \headerbox{\textcolor{white}{Features}}{name=features,row=2, column=2,span=1, below=realdata}{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{small}
\begin{itemize}[leftmargin=0.5cm] 
 \item Supports local and end-to-end alignments
 \item Bisulfite mapping 
 \item Estimates important parameters from input data
 \item Alignment calculation on the GPU (if available)  
 \item Memory footprint < 6 GB for human
 \item Supports reads from 36 bp to 1000 bp   
 \item Input formats: FASTQ/A, SAM, BAM
 \item Output format: SAM, BAM 
\end{itemize}
NextGenMap is freely available on our web page:
\end{small}
\begin{centering}
 \textbf{http://www.cibiv.at/software/ngm/NGM}
\end{centering}
\vspace{0.3em}
 }

  
  

\end{poster}%
\end{document}
