\name{sigWin} \alias{sigWin} \alias{sigWin_chr} %- Also NEED an '\alias' for EACH other topic documented here. \title{Calculate regions of read-enrichment} \description{ Calculate regions of read-enrichment } \usage{ sigWin(experiment, t = 1, g = 100) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{experiment}{ Output of the function \code{ChIPseqScore}} \item{t}{ Numeric value. Read-enriched regions are calculated as genomic regions with score values bigger than \code{t} } \item{g}{ Integer value. The maximum gap allowed between regions. Regions that are less than \code{g} bps away will be merged. } } \value{ An object of type'GRange' with its values being: \item{seqnames}{Chromosome name} \item{ranges }{An IRanges object indicating start and end of the read-enriched region} \item{posPeak }{Position of the maximum score value on the read-enriched region} \item{score }{Maximum score value on the read-enriched region} } \references{ Muino et al. (submitted). Plant ChIP-seq Analyzer: An R package for the statistcal detection of protein-bound genomic regions. \cr Kaufmann et al.(2009).Target genes of the MADS transcription factor SEPALLATA3: integration of developmental and hormonal pathways in the Arabidopsis flower. PLoS Biology; 7(4):e1000090.} \author{ Jose M Muino, \email{jose.muino@wur.nl}} \seealso{ CSAR-package } \examples{ ##For this example we will use the a subset of the SEP3 ChIP-seq data (Kaufmann, 2009) data("CSAR-dataset"); ##We calculate the number of hits for each nucleotide posotion for the control and sample. We do that just for chromosome chr1, and for positions 1 to 10kb nhitsS<-mappedReads2Nhits(sampleSEP3_test,file="sampleSEP3_test",chr=c("CHR1v01212004"),chrL=c(10000)) nhitsC<-mappedReads2Nhits(controlSEP3_test,file="controlSEP3_test",chr=c("CHR1v01212004"),chrL=c(10000)) ##We calculate a score for each nucleotide position test<-ChIPseqScore(control=nhitsC,sample=nhitsS) ##We calculate the candidate read-enriched regions win<-sigWin(test) }