distance2Genes: Calculate relative positions of read-enriched regions regarding gene position

Description

Calculate relative positions of read-enrichment regions regarding gene position

Usage

distance2Genes(win, gff, t = 1, d1 = -3000, d2 = 1000)

Arguments

win

GRange structure obtained with the function sigWin

gff

Data.frame structure obtained after loading a desired gff file

Integer. Only distances of read-enriched regions with a score bigger than t will be considered

Negative integer. Minimum relative position regarding the start of the gene to be considered

Positive integer. Maximum relative position regarding the end of the gene to be considered

Value

peakName: read-enriched region name
p1: relative position regarding the start of the gene
p2: relative position regarding the end of the gene
gene: name of the gene
le: length (bp) of the gene

References

Muino et al. (submitted). Plant ChIP-seq Analyzer: An R package for the statistcal detection of protein-bound genomic regions. 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.

Examples

Run this code



##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)


##We calculate relative positions of read-enriched regions regarding gene position
d<-distance2Genes(win=win,gff=TAIR8_genes_test)