\name{syntheticNucMap}
\alias{syntheticNucMap}
\title{
	Generates a synthetic nucleosome map
}
\description{
	This function generates a synthetic nucleosome map using the parameters given by the user and returns the coverage (like NGS experiments) or a pseudo-hybdridization ratio (like Tiling Arrays) toghether with the perfect information about the well positioned and fuzzy nucleosome positions.
}
\usage{
syntheticNucMap(wp.num=100, wp.del=10, wp.var=20, fuz.num=50, fuz.var=50, max.cover=20, nuc.len=147, lin.len=20, rnd.seed=NULL, as.ratio=FALSE, show.plot=FALSE, ...)
}
\arguments{
  \item{wp.num}{
	Number of well-positioned (non overlapped) nucleosomes. They are placed uniformly every \code{nuc.len+lin.len} basepairs.
}
  \item{wp.del}{
	Number of well-positioned nucleosomes (the ones generated by \code{wp.num}) to remove. This will create an uncovered region.
}
  \item{wp.var}{
	Maximum variance in basepairs of the well-positioned nucleosomes. This will create some variation in the position of the reads describing the same nucleosome
}
	\item{fuz.num}{
	Number of fuzzy nucleosomes. They are distributed randomly over all the region. They could be overlapped with other well-positioned or fuzzy nucleosomes.
}
  \item{fuz.var}{
	Maximum variance of the fuzzy nucleosomes. This allow to set different variance in well-positioned and fuzzy nucleosome reads (using \code{wp.var} and \code{fuz.var})
}
  \item{max.cover}{
	Maximum coverage of a nucleosome, i.e., how many times a nucleosome read can be repeated. The final coverage probably will be higher by the addition of overlapping nucleosomes.
}
  \item{nuc.len}{
	Nucleosome length. It's not recomended change the default 147bp value.
}
	\item{lin.len}{
	Linker DNA length. Usually around 20 bp.
}
	\item{rnd.seed}{
	As this model uses random distributions for the placement, by setting the rnd.seed to a known value allows to reproduce maps in different executions or computers. If you don't need this, just left it in default value.
}
	\item{as.ratio}{
	If \code{as.ratio=TRUE} this will create and return a synthetic naked DNA control map and the ratio between it and the nucleosome coverage. This can be used to simulate hybridization ratio data, like the one in Tiling Arrays.
}
	\item{show.plot}{
	If \code{TRUE}, will plot the output coverage map, with the nucleosome calls and optionally the calculated ratio.
}
	\item{...}{
	Additional parameters to be passed to \code{plot} if \code{show.plot=TRUE}
}
}
\value{
A list with the following elements:
 \item{wp.starts}{Start points of well-positioned nucleosomes}
 \item{wp.nreads}{Number of repetitions of each well positioned read}
 \item{wp.reads}{Well positioned nucleosome reads (\code{IRanges} format), containing the repetitions}
 \item{fuz.starts}{Start points of the fuzzy nucleosomes}
 \item{fuz.nreads}{Number of repetitions of each fuzzy nucleosome read}
 \item{fuz.reads}{Fuzzy nucleosome reads (\code{IRanges} format), containing all the repetitions}
 \item{syn.reads}{All synthetic nucleosome reads togheter (\code{IRanges} format)}

The following elements will be only returned if \code{as.ratio=TRUE}:

 \item{ctr.reads}{The pseudo-naked DNA (control) reads (\code{IRanges} format)}
 \item{syn.ratio}{The calculated ratio nucleosomal/control (\code{Rle} format)}
}
\author{
  Oscar Flores \email{oflores@mmb.pcb.ub.es}
}
\keyword{ datagen }
\examples{
	
	#Generate a synthetic map with 50wp + 20fuzzy nucleosomes using fixed random seed=1
	res = syntheticNucMap(wp.num=50, fuz.num=20, show.plot=TRUE, rnd.seed=1)

	#Increase the fuzzyness
	res = syntheticNucMap(wp.num=50, fuz.num=20, wp.var=70, fuz.var=150, show.plot=TRUE, rnd.seed=1)

	#Calculate also a random map and get the ratio between random and nucleosomal
	res = syntheticNucMap(wp.num=50, wp.del=0, fuz.num=20, as.ratio=TRUE, show.plot=TRUE, rnd.seed=1)

	print(res)

	#Different reads can be accessed separately from results
	#Let's use this to plot the nucleosomal + the random map
	par(mfrow=c(3,1), mar=c(3,4,1,1))
	plot(as.vector(coverage(res$syn.reads)), type="h", col="red", ylab="nucleosomal", ylim=c(0,35))
	plot(as.vector(coverage(res$ctr.reads)), type="h", col="blue", ylab="random", ylim=c(0,35))
	plot(as.vector(res$syn.ratio), type="h", col="orange", ylab="ratio")	
}