annotateRIP {RIPSeeker} | R Documentation

## Annotate RIP peaks with genomic information and perform GO enrichement

### Description

Given the genomic coordinates of each predicted RIP regions, query the Ensembl database whether each region is nearby or overlaps any known (noncoding) genes.

### Usage

    annotateRIP(sigGRanges, biomaRt_dataset, featureType = "TSS",
    	goAnno, strandSpecific = FALSE, exportFormat = "txt",
    	hasGOdb = !missing(goAnno), goPval = 0.1, outDir, ...)

### Arguments

`sigGRanges` | `GRanges` object indicating the chromosomal coordinates of each RIP peaks.
`biomaRt_dataset` | Ensembl dataset available from biomaRt (See `listDatasets`). For instance, the human and mouse annotations are `hsapiens_gene_ensembl` and `mmusculus_gene_ensembl`, respectively.
`featureType` | TSS, miRNA, Exon, 5'UTR, 3'UTR, transcript or Exon plus UTR defined in `getAnnotation`.
`goAnno` | Optional argugment that specifies a GO dataset used for GO enrichement analysis performed by `getEnrichedGO`. For instance, the human and mouse GO datasets are `org.Hs.eg.db` and `org.Mm.eg.db`.
`strandSpecific` | Indicate whether the annotations should be strand-specific (Default: FALSE)
`exportFormat` | Format to export using `exportGRanges` (Default: "txt", i.e. tab-delim file).
`hasGOdb` | A binary flag that indicates whether GO enrichement is performed in order to export the results. `hasGOdb` can be FALSE either because `goAnno` is not specifiy or because the GO database does not exist.
`goPval` | P-value cutoff to determine the significance of enriched GO terms by `getEnrichedGO`.
`outDir` | Output directory.
`...` | Extra arguments passed to `useMart` to specify the database and to passed `getEnrichedGO` to specify the GO enrichment procedure.

### Details

To access the up-to-date Ensembl database, RIPSeeker employs `useMart` and `getAnnotation` from biomaRt and ChIPpeakAnno Bioconductor packages to dynamically establish internet connection to the database and retrieve the up-to-date annotations. Then, `annotatePeakInBatch` from ChIPpeakAnno is used to efficiently annotate all of the predicted regions based on the Ensembl annotation. A predicted region may overlap multiple genes, all of which will be reported as separate records. Moreover, `getEnrichedGO` from ChIPpeakAnno is applied to the annotated predictions to discover enriched Gene Ontology (GO) terms involving the protein-associated transcriptome.

In order to use old annotation (e.g., mm9 v.s. mm10), user also needs to specify the host and biomart arguments accepted within `useMart`. To access to mouse annotation from Ensembl version 65, for instance, user needs to call annotateRIP(..., dataset="mmusculus_gene_ensembl", biomart="ENSEMBL_MART_ENSEMBL", host="dec2011.archive.ensembl.org", ...), which will run useMart(dataset="mmusculus_gene_ensembl", biomart="ENSEMBL_MART_ENSEMBL", host="dec2011.archive.ensembl.org", ...) to get the mm9 annotation from Ensembl (v65).

### Value

`sigGRangesAnnotated` | `sigGRanges` augmented with genomic information including "ensembl_gene_id", "external_gene_id", and "description"
`enrichedGO` | Output from getEnrichedGO. All three main GO categories ("Biological Process", "Molecular Function", "Cellular Component") are combined together and returned. The argument is only returned when `hasGOdb` is TRUE.

If `outDir` is specified, then the above `sigGRangesAnnotated` is saved as RIPregions_annotated.txt and RIPregions_annotated.RData, and `enrichedGO` as RIPregions_enrichedGO.txt in the `outDir` directory.

### Author(s)

Yue Li

### References

Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Steffen Durinck, Paul T. Spellman, Ewan Birney and Wolfgang Huber, Nature Protocols 4, 1184-1191 (2009).

BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Steffen Durinck, Yves Moreau, Arek Kasprzyk, Sean Davis, Bart De Moor, Alvis Brazma and Wolfgang Huber, Bioinformatics 21, 3439-3440 (2005).

Lihua Julie Zhu, Herve Pages, Claude Gazin, Nathan Lawson, Jianhong Ou, Simon Lin, David Lapointe and Michael Green (2012). ChIPpeakAnno: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges.. R package version 2.4.0.

### See Also

`useMart, getAnnotation, getEnrichedGO`

### Examples

    if(interactive()) { # need internet connection
    # Retrieve system files
    extdata.dir <- system.file("extdata", package="RIPSeeker")

    bamFiles <- list.files(extdata.dir, ".bam$", recursive=TRUE, full.names=TRUE)

    bamFiles <- grep("PRC2", bamFiles, value=TRUE)

    # Parameters setting
    binSize <- NULL							# automatically determine bin size
    minBinSize <- 10000						# min bin size in automatic bin size selection
    maxBinSize <- 12000						# max bin size in automatic bin size selection
    multicore <- FALSE						# use multicore
    strandType <- "-"							# set strand type to minus strand

    biomart <- "ENSEMBL_MART_ENSEMBL"		# use archive to get ensembl 65
    dataset <- "mmusculus_gene_ensembl"		# mouse dataset id name
    host <- "dec2011.archive.ensembl.org" 	# use ensembl 65 for annotation

    goAnno <- "org.Mm.eg.db"

    ################ run main function for HMM inference on all chromosomes ################
    mainSeekOutputRIP <- mainSeek(
        bamFiles=grep(pattern="SRR039214", bamFiles, value=TRUE, invert=TRUE),
    		binSize=binSize, minBinSize = minBinSize,
    		maxBinSize = maxBinSize, strandType=strandType,
    		reverseComplement=TRUE, genomeBuild="mm9",
    		uniqueHit = TRUE, assignMultihits = TRUE,
    		rerunWithDisambiguatedMultihits = TRUE,
    		multicore=multicore, silentMain=FALSE, verbose=TRUE)

    # use defined binSize from RIP
    RIPBinSize <- lapply(mainSeekOutputRIP$nbhGRList, function(x) median(width(x)))

    mainSeekOutputCTL <- mainSeek(
        bamFiles=grep(pattern="SRR039214", bamFiles, value=TRUE, invert=FALSE),
    		binSize=RIPBinSize, strandType=strandType,
    		reverseComplement=TRUE, genomeBuild="mm9",
    		uniqueHit = TRUE, assignMultihits = TRUE,
    		rerunWithDisambiguatedMultihits = TRUE,
    		multicore=multicore, silentMain=FALSE, verbose=TRUE)

    ################ significance test on Viterbi predicted peaks ################
    ripGR <- seekRIP(mainSeekOutputRIP$nbhGRList$chrX, mainSeekOutputCTL$nbhGRList)

    ################ Annotate peaks ################

    annotatedRIPGR <- annotateRIP(sigGRanges = ripGR,
    				biomaRt_dataset = dataset, goAnno = goAnno,
    				strandSpecific = !is.null(strandType),
    				host=host, biomart=biomart)

    head(annotatedRIPGR$sigGRangesAnnotated)
    }

[Package RIPSeeker version 1.24.0 Index]