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In this guide, we illustrate here two common downstream analysis workflows for ChIP-seq experiments, one is for comparing and combining peaks for single transcription factor (TF) with replicates, and the other is for comparing binding profiles from ChIP-seq experiments with multiple TFs.

1 Workflow for ChIP-seq experiments of single transcription factor with replicates

This workflow shows how to convert BED/GFF files to GRanges, find overlapping peaks between two peak sets, and visualize the number of common and specific peaks with Venn diagram.

1.1 Import data and obtain overlapping peaks from replicates

The input for ChIPpeakAnno1 is a list of called peaks identified from ChIP-seq experiments or any other experiments that yield a set of chromosome coordinates. Although peaks are represented as GRanges in ChIPpeakAnno, other common peak formats such as BED, GFF and MACS can be converted to GRanges easily using a conversion toGRanges method. For detailed information on how to use this method, please type ?toGRanges.

The following examples illustrate the usage of this method to convert BED and GFF file to GRanges, add metadata from orignal peaks to the overlap GRanges using function addMetadata, and visualize the overlapping using function makeVennDiagram.

library(ChIPpeakAnno)
bed <- system.file("extdata", "MACS_output.bed", package="ChIPpeakAnno")
gr1 <- toGRanges(bed, format="BED", header=FALSE) 
## one can also try import from rtracklayer
gff <- system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno")
gr2 <- toGRanges(gff, format="GFF", header=FALSE, skip=3)
## must keep the class exactly same as gr1$score, i.e., numeric.
gr2$score <- as.numeric(gr2$score) 
ol <- findOverlapsOfPeaks(gr1, gr2)
## add metadata (mean of score) to the overlapping peaks
ol <- addMetadata(ol, colNames="score", FUN=mean) 
ol$peaklist[["gr1///gr2"]][1:2]
## GRanges object with 2 ranges and 2 metadata columns:
##       seqnames           ranges strand |
##          <Rle>        <IRanges>  <Rle> |
##   [1]     chr1 [713791, 715578]      * |
##   [2]     chr1 [724851, 727191]      * |
##                                           peakNames     score
##                                     <CharacterList> <numeric>
##   [1] gr1__MACS_peak_13,gr2__region_0,gr2__region_1  850.2033
##   [2]               gr2__region_2,gr1__MACS_peak_14   29.1700
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
makeVennDiagram(ol)
Figure 1. Venn diagram of overlaps for replicated experiments

Figure 1: Figure 1
Venn diagram of overlaps for replicated experiments

## $p.value
##      gr1 gr2 pval
## [1,]   1   1    0
## 
## $vennCounts
##      gr1 gr2 Counts
## [1,]   0   0      0
## [2,]   0   1     61
## [3,]   1   0     62
## [4,]   1   1    166
## attr(,"class")
## [1] "VennCounts"

1.2 Prepare annotation data

Annotation data should be an object of GRanges. Same as import peaks, we use the method toGRanges, which can return an object of GRanges, to represent the annotation data. An annotation data be constructed from not only BED, GFF or user defined readable text files, but also EnsDb or TxDb object, by calling the toGRanges method. Please type ?toGRanges for more information.

library(EnsDb.Hsapiens.v75) ##(hg19)
## create annotation file from EnsDb or TxDb
annoData <- toGRanges(EnsDb.Hsapiens.v75, feature="gene")
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

1.3 Visualize binding site distribution relative to features

After finding the overlapping peaks, the distribution of the distance of overlapped peaks to the nearest feature such as the transcription start sites (TSS) can be plotted by binOverFeature function. The sample code here plots the distribution of peaks around the TSS.

overlaps <- ol$peaklist[["gr1///gr2"]]
binOverFeature(overlaps, annotationData=annoData,
               radius=5000, nbins=20, FUN=length, errFun=0,
               ylab="count", 
               main="Distribution of aggregated peak numbers around TSS")
Figure 2. Distribution of peaks around transcript start sites.

Figure 2: Figure 2
Distribution of peaks around transcript start sites.

In addition, assignChromosomeRegion can be used to summarize the distribution of peaks over different type of features such as exon, intron, enhancer, proximal promoter, 5’ UTR and 3’ UTR. This distribution can be summarized in peak centric or nucleotide centric view using the function assignChromosomeRegion. Please note that one peak might span multiple type of features, leading to the number of annotated features greater than the total number of input peaks. At the peak centric view, precedence will dictate the annotation order when peaks span multiple type of features.

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
aCR<-assignChromosomeRegion(overlaps, nucleotideLevel=FALSE, 
                           precedence=c("Promoters", "immediateDownstream", 
                                         "fiveUTRs", "threeUTRs", 
                                         "Exons", "Introns"), 
                           TxDb=TxDb.Hsapiens.UCSC.hg19.knownGene)
barplot(aCR$percentage, las=3)
Figure 3. Peak distribution over different genomic features.

Figure 3: Figure 3
Peak distribution over different genomic features.

1.4 Annotate peaks

As shown from the distribution of aggregated peak numbers around TSS and the distribution of peaks in different of chromosome regions, most of the peaks locate around TSS. Therefore, it is reasonable to use annotatePeakInBatch or annoPeaks to annotate the peaks to the promoter regions of Hg19 genes. Promoters can be specified with bindingRegion. For the following example, promoter region is defined as upstream 2000 and downstream 500 from TSS (bindingRegion=c(-2000, 500)).

overlaps.anno <- annotatePeakInBatch(overlaps, 
                                     AnnotationData=annoData, 
                                     output="nearestBiDirectionalPromoters",
                                     bindingRegion=c(-2000, 500))
library(org.Hs.eg.db)
overlaps.anno <- addGeneIDs(overlaps.anno,
                            "org.Hs.eg.db",
                            IDs2Add = "entrez_id")
head(overlaps.anno)
## GRanges object with 6 ranges and 11 metadata columns:
##       seqnames           ranges strand |
##          <Rle>        <IRanges>  <Rle> |
##    X1     chr1 [713791, 715578]      * |
##    X1     chr1 [713791, 715578]      * |
##    X3     chr1 [839467, 840090]      * |
##    X4     chr1 [856361, 856999]      * |
##    X5     chr1 [859315, 860144]      * |
##   X10     chr1 [901118, 902861]      * |
##                                           peakNames     score        peak
##                                     <CharacterList> <numeric> <character>
##    X1 gr1__MACS_peak_13,gr2__region_0,gr2__region_1  850.2033          X1
##    X1 gr1__MACS_peak_13,gr2__region_0,gr2__region_1  850.2033          X1
##    X3               gr1__MACS_peak_16,gr2__region_3   73.1200          X3
##    X4               gr1__MACS_peak_17,gr2__region_4   54.6900          X4
##    X5               gr2__region_5,gr1__MACS_peak_18   81.4850          X5
##   X10              gr2__region_11,gr1__MACS_peak_23  119.9100         X10
##               feature   feature.ranges feature.strand  distance
##           <character>        <IRanges>          <Rle> <integer>
##    X1 ENSG00000228327 [700237, 714006]              -         0
##    X1 ENSG00000237491 [714150, 745440]              +         0
##    X3 ENSG00000272438 [840214, 851356]              +       123
##    X4 ENSG00000223764 [852245, 856396]              -         0
##    X5 ENSG00000187634 [860260, 879955]              +       115
##   X10 ENSG00000187583 [901877, 911245]              +         0
##       insideFeature distanceToSite     gene_name   entrez_id
##            <factor>      <integer>   <character> <character>
##    X1  overlapStart              0 RP11-206L10.2        <NA>
##    X1  overlapStart              0 RP11-206L10.9        <NA>
##    X3      upstream            123  RP11-54O7.16        <NA>
##    X4  overlapStart              0   RP11-54O7.3   100130417
##    X5      upstream            115        SAMD11      148398
##   X10  overlapStart              0       PLEKHN1       84069
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
write.csv(as.data.frame(unname(overlaps.anno)), "anno.csv")

The distribution of the common peaks around features can be visualized using a pie chart.

pie1(table(overlaps.anno$insideFeature))
Figure 4. Pie chart of the distribution of common peaks around features.

Figure 4: Figure 4
Pie chart of the distribution of common peaks around features.

1.5 Obtain enriched GO terms and Pathways

The following example shows how to use getEnrichedGO to obtain a list of enriched GO terms with annotated peaks. For pathway analysis, please use function getEnrichedPATH with reactome or KEGG database. Please note that by default feature_id_type is set as “ensembl_gene_id”. If you are using TxDb as annotation data, please set it to “entrez_id”.

over <- getEnrichedGO(overlaps.anno, orgAnn="org.Hs.eg.db", 
                     maxP=.05, minGOterm=10, 
                     multiAdjMethod="BH", condense=TRUE)
head(over[["bp"]][, -c(3, 10)])
## [1] go.id              go.term            Ontology          
## [4] pvalue             count.InDataset    count.InGenome    
## [7] totaltermInDataset totaltermInGenome  EntrezID          
## <0 rows> (or 0-length row.names)
library(reactome.db)
path <- getEnrichedPATH(overlaps.anno, "org.Hs.eg.db", "reactome.db", maxP=.05)
head(path)
##         path.id EntrezID count.InDataset count.InGenome     pvalue
## 1 R-HSA-1296041     2782               1             25 0.04002106
## 2 R-HSA-1296059     2782               1             25 0.04002106
## 3 R-HSA-1296065     2782               1             31 0.04938668
## 4  R-HSA-196854   375790               2            179 0.03495614
## 5  R-HSA-196854    55229               2            179 0.03495614
## 6 R-HSA-1971475   375790               1             26 0.04158834
##   totaltermInDataset totaltermInGenome PATH
## 1                174            106602   NA
## 2                174            106602   NA
## 3                174            106602   NA
## 4                174            106602   NA
## 5                174            106602   NA
## 6                174            106602   NA

1.6 Obtain the sequences surrounding the peaks

Here is an example to get the sequences of the peaks plus 20 bp upstream and downstream for PCR validation or motif discovery.

library(BSgenome.Hsapiens.UCSC.hg19)
seq <- getAllPeakSequence(overlaps, upstream=20, downstream=20, genome=Hsapiens)
write2FASTA(seq, "test.fa")

1.7 Output a summary of consensus in the peaks

Here is an example to get the Z-scores for short oligos3.

## summary of the short oligos
freqs <- oligoFrequency(Hsapiens$chr1, MarkovOrder=3)
os <- oligoSummary(seq, oligoLength=6, MarkovOrder=3, 
                   quickMotif=FALSE, freqs=freqs)
## plot the results
zscore <- sort(os$zscore)
h <- hist(zscore, breaks=100, xlim=c(-50, 50), main="Histogram of Z-score")
text(zscore[length(zscore)], max(h$counts)/10, 
     labels=names(zscore[length(zscore)]), adj=1)
Figure 5. Histogram of Z-score of 6-mer

Figure 5: Figure 5
Histogram of Z-score of 6-mer

## We can also try simulation data
seq.sim.motif <- list(c("t", "g", "c", "a", "t", "g"), 
                      c("g", "c", "a", "t", "g", "c"))
set.seed(1)
seq.sim <- sapply(sample(c(2, 1, 0), 1000, replace=TRUE, prob=c(0.07, 0.1, 0.83)), 
                  function(x){
    s <- sample(c("a", "c", "g", "t"), 
                sample(100:1000, 1), replace=TRUE)
    if(x>0){
        si <- sample.int(length(s), 1)
        if(si>length(s)-6) si <- length(s)-6
        s[si:(si+5)] <- seq.sim.motif[[x]]
    }
    paste(s, collapse="")
})
os <- oligoSummary(seq.sim, oligoLength=6, MarkovOrder=3, 
                   quickMotif=TRUE)
zscore <- sort(os$zscore, decreasing=TRUE)
h <- hist(zscore, breaks=100, main="Histogram of Z-score")
text(zscore[1:2], rep(5, 2), 
     labels=names(zscore[1:2]), adj=0, srt=90)
Figure 6. Histogram of Z-score of simulation data

Figure 6: Figure 6
Histogram of Z-score of simulation data

## generate the motifs
library(motifStack)
pfms <- mapply(function(.ele, id)
    new("pfm", mat=.ele, name=paste("SAMPLE motif", id)), 
    os$motifs, 1:length(os$motifs))
motifStack(pfms[[1]])
Figure 7. Motif of simulation data

(#fig:simulation.motif)Figure 7. Motif of simulation data

1.8 Find peaks with bi-directional promoters

Bidirectional promoters are the DNA regions located between TSS of two adjacent genes that are transcribed on opposite directions and often co-regulated by this shared promoter region5. Here is an example to find peaks near bi-directional promoters.

bdp <- peaksNearBDP(overlaps, annoData, maxgap=5000)
c(bdp$percentPeaksWithBDP, 
  bdp$n.peaks, 
  bdp$n.peaksWithBDP)
## [1]   0.1084337 166.0000000  18.0000000
bdp$peaksWithBDP[1:2]
## GRangesList object of length 2:
## $1 
## GRanges object with 2 ranges and 11 metadata columns:
##      seqnames           ranges strand |
##         <Rle>        <IRanges>  <Rle> |
##   X1     chr1 [713791, 715578]      * |
##   X1     chr1 [713791, 715578]      * |
##                                          peakNames     score   bdp_idx
##                                    <CharacterList> <numeric> <integer>
##   X1 gr1__MACS_peak_13,gr2__region_0,gr2__region_1  850.2033         1
##   X1 gr1__MACS_peak_13,gr2__region_0,gr2__region_1  850.2033         1
##             peak         feature   feature.ranges feature.strand  distance
##      <character>     <character>        <IRanges>          <Rle> <integer>
##   X1          X1 ENSG00000228327 [700237, 714006]              -         0
##   X1          X1 ENSG00000237491 [714150, 745440]              +         0
##      insideFeature distanceToSite     gene_name
##           <factor>      <integer>   <character>
##   X1  overlapStart              0 RP11-206L10.2
##   X1  overlapStart              0 RP11-206L10.9
## 
## $4 
## GRanges object with 2 ranges and 11 metadata columns:
##      seqnames           ranges strand |                       peakNames
##   X4     chr1 [856361, 856999]      * | gr1__MACS_peak_17,gr2__region_4
##   X4     chr1 [856361, 856999]      * | gr1__MACS_peak_17,gr2__region_4
##      score bdp_idx peak         feature   feature.ranges feature.strand
##   X4 54.69       4   X4 ENSG00000223764 [852245, 856396]              -
##   X4 54.69       4   X4 ENSG00000187634 [860260, 879955]              +
##      distance insideFeature distanceToSite   gene_name
##   X4        0  overlapStart              0 RP11-54O7.3
##   X4     3260      upstream           3260      SAMD11
## 
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths

1.9 Find possible enhancers with DNA interaction data

There are several techniques available to determine the spatial organization of chromosomes at high resolution such as 3C, 5C and HiC6. These techniques make it possible to search peaks binding to the potential enhancer regions. Here is an example to find peaks binding to the potential enhancer regions.

DNA5C <- system.file("extdata", 
                     "wgEncodeUmassDekker5CGm12878PkV2.bed.gz",
                     package="ChIPpeakAnno")
DNAinteractiveData <- toGRanges(gzfile(DNA5C))
findEnhancers(overlaps, annoData, DNAinteractiveData)
## GRanges object with 5 ranges and 14 metadata columns:
##      seqnames                 ranges strand |
##         <Rle>              <IRanges>  <Rle> |
##   X1     chr1 [151591700, 151591800]      * |
##   X1     chr1 [151591700, 151591800]      * |
##   X1     chr1 [151591700, 151591800]      * |
##   X1     chr1 [151591700, 151591800]      * |
##   X1     chr1 [151630186, 151630286]      * |
##                               peakNames     score         feature
##                         <CharacterList> <numeric>     <character>
##   X1 gr2__region_228,gr1__MACS_peak_229    78.675 ENSG00000207606
##   X1 gr2__region_228,gr1__MACS_peak_229    78.675 ENSG00000143390
##   X1 gr2__region_228,gr1__MACS_peak_229    78.675 ENSG00000143376
##   X1 gr2__region_228,gr1__MACS_peak_229    78.675 ENSG00000143367
##   X1 gr2__region_229,gr1__MACS_peak_230    78.675 ENSG00000143393
##              feature.ranges feature.strand   feature.shift.ranges
##                   <IRanges>          <Rle>              <IRanges>
##   X1 [151518272, 151518367]              + [151594534, 151594629]
##   X1 [151313116, 151319833]              - [151595209, 151601927]
##   X1 [151584541, 151671567]              + [151500588, 151587615]
##   X1 [151512781, 151556059]              + [151595902, 151639180]
##   X1 [151264273, 151300191]              - [151594247, 151630165]
##      feature.shift.strand  distance insideFeature distanceToSite   gene_name
##                     <Rle> <integer>      <factor>      <integer> <character>
##   X1                    +      2733      upstream           2733      MIR554
##   X1                    +      3408      upstream           3408        RFX5
##   X1                    -      4084      upstream           4084       SNX27
##   X1                    +      4101      upstream           4101       TUFT1
##   X1                    -        20      upstream             20       PI4KB
##             peak  DNAinteractive.ranges             DNAinteractive.blocks
##      <character>              <IRanges>                     <IRangesList>
##   X1          X1 [151516086, 151603110]     [    1, 19082] [76263, 87025]
##   X1          X1 [151309062, 151603110] [     1,  13633] [283287, 294049]
##   X1          X1 [151546428, 151636526]     [    1,  6978] [72324, 90099]
##   X1          X1 [151546428, 151636526]     [    1,  6978] [72324, 90099]
##   X1          X1 [151283079, 151636526] [     1,   5699] [335673, 353448]
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

2 Workflow for comparing binding profiles from multiple transcription factors (TFs)

Given two or more peak lists from different TFs, one may be interested in finding whether DNA binding profile of those TFs are correlated, and if correlated, what is the common binding pattern. The workflow here shows how to test the correlation of binding profiles of three TFs and how to discover the common binding pattern.

2.1 Import data

path <- system.file("extdata", package="ChIPpeakAnno")
files <- dir(path, "broadPeak")
data <- sapply(file.path(path, files), toGRanges, format="broadPeak")
names(data) <- gsub(".broadPeak", "", files)

2.2 Determine if there is a significant overlap among multiple sets of peaks

2.2.1 Hypergeometric test

When we test the association between two sets of data based on hypergeometric distribution, the number of all potential binding sites is required. The parameter totalTest in the function makeVennDiagram indicates how many potential peaks in total will be used in the hypergeometric test. It should be larger than the largest number of peaks in the peak list. The smaller it is set, the more stringent the test is. The time used to calculate p-value does not depend on the value of the totalTest. For practical guidance on how to choose totalTest, please refer to the post. The following example makes an assumption that there are 3% of coding region plus promoter region. Because the sample data is only a subset of chromosome 2, we estimate that the total binding sites is 1/24 of possible binding region in the genome.

ol <- findOverlapsOfPeaks(data, connectedPeaks="keepAll")
averagePeakWidth <- mean(width(unlist(GRangesList(ol$peaklist))))
tot <- ceiling(3.3e+9 * .03 / averagePeakWidth / 24)
makeVennDiagram(ol, totalTest=tot, connectedPeaks="keepAll")