score2wig {CSAR} | R Documentation |
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)
score2wig(experiment, file, t = 2, times = 1e6,description="", name="")
experiment |
Output of the function |
file |
Name of the output .wig file |
t |
Only nucleotide positions with a read-enrichment score bigger than |
times |
To be memory efficient, CSAR will only upload to the RAM memory fragments of length |
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. |
None. Results are printed in a file
Jose M Muino, jose.muino@wur.nl
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.
CSAR-package
##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")