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.
See Also
genesWithPeaks, 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)
##We calculate relative positions of read-enriched regions regarding gene position
d<-distance2Genes(win=win,gff=TAIR8_genes_test)
Results
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> library(CSAR)
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/CSAR/distance2Genes.Rd_%03d_medium.png", width=480, height=480)
> ### Name: distance2Genes
> ### Title: Calculate relative positions of read-enriched regions regarding
> ### gene position
> ### Aliases: distance2Genes
>
> ### ** 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))
mappedReads2Nhits has just finished CHR1v01212004 ...
> nhitsC<-mappedReads2Nhits(controlSEP3_test,file="controlSEP3_test",chr=c("CHR1v01212004"),chrL=c(10000))
mappedReads2Nhits has just finished CHR1v01212004 ...
>
>
> ##We calculate a score for each nucleotide position
> test<-ChIPseqScore(control=nhitsC,sample=nhitsS)
CHR1v01212004 done...
>
> ##We calculate the candidate read-enriched regions
> win<-sigWin(test)
CHR1v01212004 done...
>
>
> ##We calculate relative positions of read-enriched regions regarding gene position
> d<-distance2Genes(win=win,gff=TAIR8_genes_test)
Starting CHR1v01212004 ...
>
>
>
>
>
>
> dev.off()
null device
1
>