1 Four steps for peak annotation

The purpose of this quick start is to introduce the four newly implemented functions, toRanges, annoGO, annotatePeakInBatch, and addGeneIDs in the new version of the ChIPpeakAnno. With those wrapper functions, the annotation of ChIP-Seq peaks becomes streamlined into four major steps:

1 Read peak data with toGRanges 2 Generate annotation data with toGRanges 3 Annotate peaks with annotatePeakInBatch 4 Add additional informations with addGeneIDs

Most of time user can use the default settings of the arguments of those functions. This makes the annotation pipeline straightforward and easy to use.

2 Use case 1: Four steps to annotate peak data with EnsDb

2.1 Step 1: Convert the peak data to `GRanges` with `toGRanges`

## First, load the ChIPpeakAnno package
library(ChIPpeakAnno)

path <- system.file("extdata", "Tead4.broadPeak", package="ChIPpeakAnno")
peaks <- toGRanges(path, format="broadPeak")
peaks[1:2]

## GRanges object with 2 ranges and 4 metadata columns:
##             seqnames           ranges strand |     score signalValue
##                <Rle>        <IRanges>  <Rle> | <integer>   <numeric>
##   peak12338     chr2 [175473, 176697]      * |       206      668.42
##   peak12339     chr2 [246412, 246950]      * |        31      100.23
##                pValue    qValue
##             <numeric> <numeric>
##   peak12338        -1        -1
##   peak12339        -1        -1
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

2.2 Step 2: Prepare annotation data with `toGRanges`

library(EnsDb.Hsapiens.v75)
annoData <- toGRanges(EnsDb.Hsapiens.v75)
annoData[1:2]

## GRanges object with 2 ranges and 1 metadata column:
##                   seqnames         ranges strand |   gene_name
##                      <Rle>      <IRanges>  <Rle> | <character>
##   ENSG00000223972     chr1 [11869, 14412]      + |     DDX11L1
##   ENSG00000227232     chr1 [14363, 29806]      - |      WASH7P
##   -------
##   seqinfo: 273 sequences from GRCh37 genome

2.3 Step 3: Annotate the peaks with `annotatePeakInBatch`

## keep the seqnames in the same style
seqlevelsStyle(peaks) <- seqlevelsStyle(annoData)
## do annotation by nearest TSS
anno <- annotatePeakInBatch(peaks, AnnotationData=annoData)
anno[1:2]

## GRanges object with 2 ranges and 13 metadata columns:
##                             seqnames           ranges strand |     score
##                                <Rle>        <IRanges>  <Rle> | <integer>
##   peak12338.ENSG00000227061     chr2 [175473, 176697]      * |       206
##   peak12339.ENSG00000143727     chr2 [246412, 246950]      * |        31
##                             signalValue    pValue    qValue        peak
##                               <numeric> <numeric> <numeric> <character>
##   peak12338.ENSG00000227061      668.42        -1        -1   peak12338
##   peak12339.ENSG00000143727      100.23        -1        -1   peak12339
##                                     feature start_position end_position
##                                 <character>      <integer>    <integer>
##   peak12338.ENSG00000227061 ENSG00000227061         197569       202605
##   peak12339.ENSG00000143727 ENSG00000143727         264140       278283
##                             feature_strand insideFeature distancetoFeature
##                                <character>      <factor>         <numeric>
##   peak12338.ENSG00000227061              +      upstream            -22096
##   peak12339.ENSG00000143727              +      upstream            -17728
##                             shortestDistance fromOverlappingOrNearest
##                                    <integer>              <character>
##   peak12338.ENSG00000227061            20872          NearestLocation
##   peak12339.ENSG00000143727            17190          NearestLocation
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

# A pie chart can be used to demonstrate the overlap features of the peaks.
pie1(table(anno$insideFeature))

## Step 4: Add additional annotation with addGeneIDs

library(org.Hs.eg.db)
anno <- addGeneIDs(anno, orgAnn="org.Hs.eg.db", 
                   feature_id_type="ensembl_gene_id",
                   IDs2Add=c("symbol"))
head(anno)

## GRanges object with 6 ranges and 14 metadata columns:
##                             seqnames           ranges strand |     score
##                                <Rle>        <IRanges>  <Rle> | <integer>
##   peak12338.ENSG00000227061     chr2 [175473, 176697]      * |       206
##   peak12339.ENSG00000143727     chr2 [246412, 246950]      * |        31
##   peak12340.ENSG00000143727     chr2 [249352, 250233]      * |       195
##   peak12341.ENSG00000143727     chr2 [259896, 261404]      * |       510
##   peak12342.ENSG00000143727     chr2 [261931, 263148]      * |        48
##   peak12343.ENSG00000236856     chr2 [378232, 378871]      * |       132
##                             signalValue    pValue    qValue        peak
##                               <numeric> <numeric> <numeric> <character>
##   peak12338.ENSG00000227061      668.42        -1        -1   peak12338
##   peak12339.ENSG00000143727      100.23        -1        -1   peak12339
##   peak12340.ENSG00000143727      630.65        -1        -1   peak12340
##   peak12341.ENSG00000143727     1649.19        -1        -1   peak12341
##   peak12342.ENSG00000143727      155.56        -1        -1   peak12342
##   peak12343.ENSG00000236856      426.52        -1        -1   peak12343
##                                     feature start_position end_position
##                                 <character>      <integer>    <integer>
##   peak12338.ENSG00000227061 ENSG00000227061         197569       202605
##   peak12339.ENSG00000143727 ENSG00000143727         264140       278283
##   peak12340.ENSG00000143727 ENSG00000143727         264140       278283
##   peak12341.ENSG00000143727 ENSG00000143727         264140       278283
##   peak12342.ENSG00000143727 ENSG00000143727         264140       278283
##   peak12343.ENSG00000236856 ENSG00000236856         388412       416885
##                             feature_strand insideFeature distancetoFeature
##                                <character>      <factor>         <numeric>
##   peak12338.ENSG00000227061              +      upstream            -22096
##   peak12339.ENSG00000143727              +      upstream            -17728
##   peak12340.ENSG00000143727              +      upstream            -14788
##   peak12341.ENSG00000143727              +      upstream             -4244
##   peak12342.ENSG00000143727              +      upstream             -2209
##   peak12343.ENSG00000236856              +      upstream            -10180
##                             shortestDistance fromOverlappingOrNearest
##                                    <integer>              <character>
##   peak12338.ENSG00000227061            20872          NearestLocation
##   peak12339.ENSG00000143727            17190          NearestLocation
##   peak12340.ENSG00000143727            13907          NearestLocation
##   peak12341.ENSG00000143727             2736          NearestLocation
##   peak12342.ENSG00000143727              992          NearestLocation
##   peak12343.ENSG00000236856             9541          NearestLocation
##                                  symbol
##                             <character>
##   peak12338.ENSG00000227061        <NA>
##   peak12339.ENSG00000143727        ACP1
##   peak12340.ENSG00000143727        ACP1
##   peak12341.ENSG00000143727        ACP1
##   peak12342.ENSG00000143727        ACP1
##   peak12343.ENSG00000236856        <NA>
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

3 Use case 2: Annotate the peaks with promoters provided by TxDb

This section demonstrates how to annotate the same peak data as in quick start 1 using a new annotation based on TxDb with toGRanges.

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
annoData <- toGRanges(TxDb.Hsapiens.UCSC.hg19.knownGene)
annoData[1:2]

## GRanges object with 2 ranges and 0 metadata columns:
##      seqnames               ranges strand
##         <Rle>            <IRanges>  <Rle>
##    1    chr19 [58858172, 58874214]      -
##   10     chr8 [18248755, 18258723]      +
##   -------
##   seqinfo: 93 sequences (1 circular) from hg19 genome

seqlevelsStyle(peaks) <- seqlevelsStyle(annoData)

The same annotatePeakInBatch function is used to annotate the peaks using annotation data just created. This time we want the peaks within 2kb upstream and up to 300bp downstream of TSS within the gene body.

anno <- annotatePeakInBatch(peaks, AnnotationData=annoData, 
                  output="overlapping", 
                  FeatureLocForDistance="TSS",
                  bindingRegion=c(-2000, 300))
anno$symbol <- xget(anno$feature, org.Hs.egSYMBOL)
head(anno)

## GRanges object with 6 ranges and 12 metadata columns:
##             seqnames             ranges strand |     score signalValue
##                <Rle>          <IRanges>  <Rle> | <integer>   <numeric>
##   peak12342     chr2 [ 261931,  263148]      * |        48      155.56
##   peak12345     chr2 [ 677052,  677862]      * |       103      334.74
##   peak12348     chr2 [3380709, 3382315]      * |       110      357.22
##   peak12348     chr2 [3380709, 3382315]      * |       110      357.22
##   peak12349     chr2 [3383131, 3384541]      * |       199      645.56
##   peak12349     chr2 [3383131, 3384541]      * |       199      645.56
##                pValue    qValue        peak     feature     feature.ranges
##             <numeric> <numeric> <character> <character>          <IRanges>
##   peak12342        -1        -1   peak12342          52 [ 264869,  278282]
##   peak12345        -1        -1   peak12345      129787 [ 667973,  677439]
##   peak12348        -1        -1   peak12348        7260 [3192741, 3381653]
##   peak12348        -1        -1   peak12348       51112 [3383446, 3488857]
##   peak12349        -1        -1   peak12349        7260 [3192741, 3381653]
##   peak12349        -1        -1   peak12349       51112 [3383446, 3488857]
##             feature.strand  distance insideFeature distanceToSite
##                      <Rle> <integer>      <factor>      <integer>
##   peak12342              +      1720      upstream           1720
##   peak12345              -         0  overlapStart              0
##   peak12348              -         0  overlapStart              0
##   peak12348              +      1130      upstream           1130
##   peak12349              -      1477      upstream           1477
##   peak12349              +         0  overlapStart              0
##                  symbol
##             <character>
##   peak12342        ACP1
##   peak12345      TMEM18
##   peak12348       TSSC1
##   peak12348    TRAPPC12
##   peak12349       TSSC1
##   peak12349    TRAPPC12
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

4 Use case 3: Annotate the peaks in both sides with nearest transcription start sites within 5K bps.

This section demonstrates the flexibility of the annotaition functions in the ChIPpeakAnno. Instead of building a new annotation data, the argument bindingTypes and bindingRegion in annoPeak function can be set to find the peaks within 5000 bp upstream and downstream of the TSS, which could be the user defined promoter region.

anno <- annotatePeakInBatch(peaks, AnnotationData=annoData, 
                  output="nearestBiDirectionalPromoters", 
                  bindingRegion=c(-5000, 500))
anno$symbol <- xget(anno$feature, org.Hs.egSYMBOL)
anno[anno$peak=="peak12725"]

## GRanges object with 2 ranges and 12 metadata columns:
##             seqnames               ranges strand |     score signalValue
##                <Rle>            <IRanges>  <Rle> | <integer>   <numeric>
##   peak12725     chr2 [28112981, 28113476]      * |        34      110.72
##   peak12725     chr2 [28112981, 28113476]      * |        34      110.72
##                pValue    qValue        peak     feature
##             <numeric> <numeric> <character> <character>
##   peak12725        -1        -1   peak12725        9577
##   peak12725        -1        -1   peak12725       64080
##                   feature.ranges feature.strand  distance insideFeature
##                        <IRanges>          <Rle> <integer>      <factor>
##   peak12725 [28113482, 28561767]              +         5      upstream
##   peak12725 [28004266, 28113223]              -         0  overlapStart
##             distanceToSite      symbol
##                  <integer> <character>
##   peak12725              5         BRE
##   peak12725              0        RBKS
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

The annotated peaks can be visualized with R/Bioconductor package trackViewer developed by our group.

library(trackViewer)
gr <- peak <- peaks["peak12725"]
start(gr) <- start(gr) - 5000
end(gr) <- end(gr) + 5000
if(.Platform$OS.type != "windows"){
    peak12725 <- importScore(file=system.file("extdata", "Tead4.bigWig", 
                                      package="ChIPpeakAnno"),
                    ranges=peak, format = "BigWig")
}else{## rtracklayer can not import bigWig files on Windows
    load(file.path(dirname(path), "cvglist.rds"))
    peak12725 <- Views(cvglists[["Tead4"]][[as.character(seqnames(peak))]],
                       start(peak),
                       end(peak))
    peak12725 <- viewApply(peak12725, as.numeric)
    tmp <- rep(peak, width(peak))
    width(tmp) <- 1
    tmp <- shift(tmp, shift=0:(width(peak)-1))
    mcols(tmp) <- peak12725
    colnames(mcols(tmp)) <- "score"
    peak12725 <- new("track", dat=tmp, 
                     name="peak12725", 
                     type="data", 
                     format="BED")
}

trs <- geneModelFromTxdb(TxDb.Hsapiens.UCSC.hg19.knownGene, 
                         org.Hs.eg.db, gr)
names(trs) <- paste(sapply(trs, function(.ele) .ele@name), names(trs), sep=":")
optSty <- optimizeStyle(trackList(peak12725, trs, heightDist = c(.3, .7)),
                        theme="bw")
viewTracks(optSty$tracks, gr=gr, viewerStyle=optSty$style)

Quick Start

Jianhong Ou, Jun Yu, Lihua Julie Zhu

4 October 2017

Package version: ChIPpeakAnno 3.10.2

Contents

1 Four steps for peak annotation

2 Use case 1: Four steps to annotate peak data with EnsDb

2.1 Step 1: Convert the peak data to `GRanges` with `toGRanges`

2.2 Step 2: Prepare annotation data with `toGRanges`

2.3 Step 3: Annotate the peaks with `annotatePeakInBatch`

3 Use case 2: Annotate the peaks with promoters provided by TxDb

4 Use case 3: Annotate the peaks in both sides with nearest transcription start sites within 5K bps.

Quick Start

Jianhong Ou, Jun Yu, Lihua Julie Zhu

4 October 2017

Package version: ChIPpeakAnno 3.10.2

Contents

1 Four steps for peak annotation

2 Use case 1: Four steps to annotate peak data with EnsDb

2.1 Step 1: Convert the peak data to GRanges with toGRanges

2.2 Step 2: Prepare annotation data with toGRanges

2.3 Step 3: Annotate the peaks with annotatePeakInBatch

3 Use case 2: Annotate the peaks with promoters provided by TxDb

4 Use case 3: Annotate the peaks in both sides with nearest transcription start sites within 5K bps.

2.1 Step 1: Convert the peak data to `GRanges` with `toGRanges`

2.2 Step 2: Prepare annotation data with `toGRanges`

2.3 Step 3: Annotate the peaks with `annotatePeakInBatch`