score2wig: Save the read-enrichment scores at each nucleotide position in a .wig file format

Description

Save the read-enrichment scores at each nucleotide position in a .wig file format that can be visualize by a genome browser (eg: Integrated Genome Browser)

Usage

score2wig(experiment, file, t = 2, times = 1e6,description="", name="")

Arguments

experiment

Output of the function ChIPseqScore

file

Name of the output .wig file

Only nucleotide positions with a read-enrichment score bigger than t will be reported

times

To be memory efficient, CSAR will only upload to the RAM memory fragments of length times. A bigger value means more RAM memory needed but whole process will be faster

description

Character. It adds a description to the wig file. The description will be shown by the genome browser used to visualize the wig file.

name

Character. It adds a wig to the wig file. The name will be shown by the genome browser used to visualize the wig file.

Value

None. Results are printed in a file

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 position 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))

##Since we will not need the raw data anymore, we could delete it from the RAM memory
rm(sampleSEP3_test,controlSEP3_test);gc(verbose=FALSE)
##We calculate a score for each nucleotide position
test<-ChIPseqScore(control=nhitsC,sample=nhitsS)

##We generate a wig file of the results to visualize them in a genome browser
score2wig(test,file="test.wig")