MEDIPS.CpGenrich: Calculates CpG enrichment of provided short reads compared to...
In chavez-lab/MEDIPS: DNA IP-seq data analysis

Description Usage Arguments Value Author(s) Examples

As a quality check for the enrichment of CpG rich DNA fragments obtained by the immunoprecipitation step of a MeDIP experiment, this function provides the functionality to calculate CpG enrichment values. The main idea is to check, how strong the regions are enriched for CpGs compared to the reference genome. For this, the function counts the number of Cs, the number of Gs, the number CpGs, and the total number of bases within the stated reference genome. Subsequently, the function calculates the relative frequency of CpGs and the observed/expected ratio of CpGs present in the reference genome. Additionally, the function calculates the same for the DNA sequences underlying the given regions. The final enrichment values result by dividing the relative frequency of CpGs (or the observed/expected value, respectively) of the regions by the relative frequency of CpGs (or the observed/expected value, respectively) of the reference genome.

1	MEDIPS.CpGenrich(file=NULL, BSgenome=NULL, extend=0, shift=0, uniq=1e-3, chr.select=NULL, paired=F)

`file`	Path and file name of the input data
`BSgenome`	The reference genome name as defined by BSgenome
`extend`	defines the number of bases by which the region will be extended before the genome vector is calculated. Regions will be extended along the plus or the minus strand as defined by their provided strand information.
`shift`	As an alternative to the extend parameter, the shift parameter can be specified. Here, the reads are not extended but shifted by the specified number of nucleotides with respect to the given strand infomation. One of the two parameters extend or shift has to be 0.
`uniq`	The uniq parameter determines, if all reads mapping to exactly the same genomic position should be kept (uniq = 0), replaced by only one representative (uniq = 1), or if the number of stacked reads should be capped by a maximal number of stacked reads per genomic position determined by a poisson distribution of stacked reads genome wide and by a given p-value (1 > uniq > 0) (deafult: 1e-3).
`chr.select`	only data at the specified chromosomes will be processed.
`paired`	option for paired end reads

`regions.CG`	the numbe of CpGs within the regions
`regions.C`	the number of Cs within the regions
`regions.G`	the number of Gs within the regions
`regions.relH`	the relative frequency of CpGs within the regions
`regions.GoGe`	the observed/expected ratio of CpGs within the regions
`genome.CG`	the numbe of CpGs within the reference genome
`genome.C`	the number of Cs within the reference genome
`genome.G`	the number of Gs within the reference genome
`genome.relH`	the relative frequency of CpGs within the reference genome
`genome.GoGe`	the observed/expected ratio of CpGs within the reference genome
`enrichment.score.relH`	regions.relH/genome.relH
`enrichment.score.GoGe`	regions.GoGe/genome.GoGe

Joern Dietrich and Matthias Lienhard

library(MEDIPSData)
library("BSgenome.Hsapiens.UCSC.hg19")
bam.file.hESCs.Rep1.MeDIP = system.file("extdata", "hESCs.MeDIP.Rep1.chr22.bam", package="MEDIPSData")

#er=MEDIPS.CpGenrich(file=bam.file.hESCs.Rep1.MeDIP, BSgenome="BSgenome.Hsapiens.UCSC.hg19", chr.select="chr22", extend=0, shift=0, uniq=1e-3)