ChIPanalyser-package: ChIPanalyser: Predicting Transcription Factor Binding Sites

In patrickCNMartin/ChIPanalyserDev: ChIPanalyser: Predicting Transcription Factor Binding Sites

Description

Based on a statistical thermodynamic framework, ChIPanalyser tries to produce ChIP-seq like profile. The model relies on four consideration: TF binding sites can be scored using a Position weight Matrix, DNA accessibility plays a role in Transcription Factor binding, binding profiles are dependant on the number of transcription factors bound to DNA and finally binding energy (another way of describing PWM's) or binding specificity should be modulated (hence the introduction of a binding specificity modulator). The end result of ChIPanalyser is to produce profiles simulating real ChIP-seq profile and provide accuracy measurements of these predicted profiles after being compared to real ChIP-seq data. The ultimate goal is to produce ChIP-seq like profiles predicting ChIP-seq like profile to circumvent the need to produce costly ChIP-seq experiments.

Details

The DESCRIPTION file: This package was not yet installed at build time.

Index: This package was not yet installed at build time.

Author(s)

Patrick C.N. Martin <[e-mail]>

And

Nicolae Radu Zabet <[e-mail]>

Maintainer: Patrick C.N. Martin <[e-mail]>

References

Zabet NR, Adryan B (2015) Estimating binding properties of transcription factors from genome-wide binding profiles. Nucleic Acids Res., 43, 84–94.

Examples

#Data extraction
data(ChIPanalyserData)
# path to Position Frequency Matrix
PFM <- file.path(system.file("extdata",package="ChIPanalyser"),"BCDSlx.pfm")
#As an example of genome, this example will run on the Drosophila genome

if(!require("BSgenome.Dmelanogaster.UCSC.dm3", character.only = TRUE)){
    if (!requireNamespace("BiocManager", quietly=TRUE))
        install.packages("BiocManager")
    BiocManager::install("BSgenome.Dmelanogaster.UCSC.dm3")
}
library(BSgenome.Dmelanogaster.UCSC.dm3)
DNASequenceSet <- getSeq(BSgenome.Dmelanogaster.UCSC.dm3)
#Building data objects
GPP <- genomicProfiles(PFM=PFM,PFMFormat="raw",BPFrequency=DNASequenceSet)

eveLocusChip<-processingChIP(eveLocusChip,eveLocus)
# Computing Genome Wide
GenomeWide <- computeGenomeWideScores(DNASequenceSet = DNASequenceSet,
    genomicsProfiles = GPP)

#Compute PWM Scores
PWMScores <- computePWMScore(genomicsProfiles = GenomeWide,
    DNASequenceSet = DNASequenceSet,
    loci = eveLocus, chromatinState = Access)
#Compute Occupnacy
Occupancy <- computeOccupancy(genomicsProfiles = PWMScores,
    parameterOptions = OPP)

#Compute ChIP profiles
chipProfile <- computeChIPProfile(genomicProfiles = Occupancy,
     loci = eveLocus,
    parameterOptions = OPP)
#Estimating accuracy estimate
AccuracyEstimate <- profileAccuracyEstimate(genomicProfiles = chipProfile,
     ChIPScore = eveLocusChip,
     parameterOptions = OPP)

    

patrickCNMartin/ChIPanalyserDev documentation built on Nov. 14, 2019, 4:39 p.m.