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source("http://bioconductor.org/biocLite.R")
biocLite("RIPSeeker")
library(RIPSeeker)
bamFiles = list.files(".", "\\.bam$", recursive=TRUE, full.names=TRUE)
ripGal=combineAlignGals(bamFiles[2], genomeBuild="hg19")
ctlGal=combineAlignGals(bamFiles[1], genomeBuild="hg19")

cNAME="input"
binSize=200
genomeBuild = "hg19"
outDir <- file.path(getwd())
reverseComplement = FALSE
uniqueHit = FALSE
assignMultihits = TRUEFALSE
rerunWithDisambiguatedMultihits = TRUEFALSE

seekOut.AGO2 <- ripSeek(bamPath = bamFiles, cNAME = cNAME, reverseComplement = FALSE, genomeBuild = "hg19", \
uniqueHit = TRUEFALSE, assignMultihits = TRUEFALSE, rerunWithDisambiguatedMultihits = FALSE, binSize=binSize, outDir=outDir)

> binSize=NULL
> cNAME="input"  #Which files are input
> genomeBuild = "hg19"   #Genome to use
> outDir <- file.path(getwd())  #Output file prefix (here, the current directory)
> reverseComplement = FALSE   #Do not reverse complement BAM entries
> uniqueHit = FALSE   #If TRUE, use only unique hits to train initial model
> assignMultihits = TRUEFALSE  #If TRUE and uniqueHit is TRUE, assign multi-hits to regions based on initial model
> rerunWithDisambiguatedMultihits = TRUEFALSE   #If TRUE and prior two options are TRUE, re-train model after multi-hit assignment
 
> seekOut.AGO2 <- ripSeek(bamPath = bamFiles, cNAME = cNAME, reverseComplement = FALSE, genomeBuild = "hg19", \
uniqueHit = TRUEFALSE, assignMultihits = TRUEFALSE, rerunWithDisambiguatedMultihits = FALSE, binSize=binSize, outDir=outDir)