ChIPpeakAnno (version 2.16.4)

ChIPpeakAnno-package: Batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments.

Description

The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites leveraging biomaRt, IRanges, Biostrings, BSgenome, GO.db, hypergeometric test phyper and multtest package.

Details

Package:

ChIPpeakAnno

Type:

Package

Version:

2.9.6

Date:

2013-08-22

License:

LGPL

LazyLoad:

yes

References

1. Y. Benjamini and Y. Hochberg (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B. Vol. 57: 289-300. 2. Y. Benjamini and D. Yekutieli (2001). The control of the false discovery rate in multiple hypothesis testing under dependency. Annals of Statistics. Accepted. 3. S. Durinck et al. (2005) BioMart and Bioconductor: a powerful link between biological biomarts and microarray data analysis. Bioinformatics, 21, 3439-3440. 4. S. Dudoit, J. P. Shaffer, and J. C. Boldrick (Submitted). Multiple hypothesis testing in microarray experiments. 5. Y. Ge, S. Dudoit, and T. P. Speed. Resampling-based multiple testing for microarray data hypothesis, Technical Report #633 of UCB Stat. http://www.stat.berkeley.edu/~gyc 6. Y. Hochberg (1988). A sharper Bonferroni procedure for multiple tests of significance, Biometrika. Vol. 75: 800-802. 7. S. Holm (1979). A simple sequentially rejective multiple test procedure. Scand. J. Statist.. Vol. 6: 65-70. 8. N. L. Johnson,S. Kotz and A. W. Kemp (1992) Univariate Discrete Distributions, Second Edition. New York: Wiley 9. Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237.

See Also

getAnnotation, annotatePeakInBatch, getAllPeakSequence, write2FASTA, convert2EntrezID, addAncestors, getEnrichedGO,BED2RangedData, GFF2RangedData, makeVennDiagram,findOverlappingPeaks, addGeneIDs, peaksNearBDP,summarizePatternInPeaks)

Examples

if (interactive())
{
	data(myPeakList)
        data(TSS.human.NCBI36)
	
	myPeakList1 = myPeakList[1:6,]

	annotatedPeak = annotatePeakInBatch(myPeakList1, AnnotationData=TSS.human.NCBI36)

	peaks = RangedData(IRanges(start=c(100, 500), end=c(300, 600), 
	names=c("peak1", "peak2")), space=c("NC_008253", "NC_010468"))
	library(BSgenome.Ecoli.NCBI.20080805)
	
	peaksWithSequences = getAllPeakSequence(peaks, upstream = 20,
	downstream = 20, genome = Ecoli)
	write2FASTA(peaksWithSequences, file="testseq.fasta", width=50)
	
	filepath =system.file("extdata", "examplePattern.fa", package="ChIPpeakAnno")
	summarizePatternInPeaks(patternFilePath=filepath, format="fasta", skip=0L, BSgenomeName=Ecoli, peaks=peaks)


	library(org.Hs.eg.db)
	annotatedPeak.withSymbol =addGeneIDs(annotatedPeak,"org.Hs.eg.db",c("symbol"))
	enrichedGO = getEnrichedGO(annotatedPeak, orgAnn ="org.Hs.eg.db", maxP=0.01, 
	multiAdj=FALSE, minGOterm=10, multiAdjMethod="")

	enriched.biologicalprocess = enrichedGO$bp
	enriched.molecularfunction = enrichedGO$mf
	enriched.cellularcomponent = enrichedGO$cc

	data(annotatedPeak)
	y = annotatedPeak$distancetoFeature[!is.na(annotatedPeak$distancetoFeature)]
	hist(y, xlab="Distance To Nearest TSS", main="", breaks=1000, 
	xlim=c(min(y)-100, max(y)+100))

	annotatedBDP = peaksNearBDP(myPeakList1, AnnotationData=TSS.human.NCBI36,
	MaxDistance=5000,PeakLocForDistance =  "middle", FeatureLocForDistance = "TSS")
	c(annotatedBDP$percentPeaksWithBDP, annotatedBDP$n.peaks, annotatedBDP$n.peaksWithBDP)
}