An example ChIP-seq analysis workflow using ChIPpeakAnno
We illustrate here a common downstream analysis workflow for ChIP-seq experiments. The input of ChIPpeakAnno is a list of called peaks identified from ChIP-seq experiments. The peaks are represented by by GRanges in ChIPpeakAnno. We implemented a conversion functions toGRanges to convert commonly used peak file formats, such as BED, GFF, or other user defined formats such as MACS (a popular peak calling program) output file to GRanges. Type ?toGRanges for more information.
The workflow here exemplifies converting the BED and GFF files to GRanges, finding the overlapping peaks between the two peak sets, and visualizing the number of common and specific peaks with Venn diagram.
bed <- system.file("extdata", "MACS_output.bed", package="ChIPpeakAnno")
gr1 <- toGRanges(bed, format="BED", header=FALSE)
## one can also try import from rtracklayer
library(rtracklayer)
gr1.import <- import(bed, format="BED")
identical(start(gr1), start(gr1.import))
## [1] TRUE
gr1[1:2]
## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <numeric>
## MACS_peak_1 chr1 [28341, 29610] * | 160.81
## MACS_peak_2 chr1 [90821, 91234] * | 133.12
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
gr1.import[1:2] #note the name slot is different from gr1
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 [28341, 29610] * | MACS_peak_1 160.81
## [2] chr1 [90821, 91234] * | MACS_peak_2 133.12
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
gff <- system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno")
gr2 <- toGRanges(gff, format="GFF", header=FALSE, skip=3)
ol <- findOverlapsOfPeaks(gr1, gr2)
makeVennDiagram(ol)
## $p.value
## gr1 gr2 pval
## [1,] 1 1 0
##
## $vennCounts
## gr1 gr2 Counts
## [1,] 0 0 0
## [2,] 0 1 61
## [3,] 1 0 62
## [4,] 1 1 164
## attr(,"class")
## [1] "VennCounts"
A pie chart is used to demonstrate the overlap features of the common peaks.
pie1(table(ol$overlappingPeaks[["gr1///gr2"]]$overlapFeature))
After finding the overlapping peaks, you can use annotatePeakInBatch to annotate all the genomic features in the AnnotationData within 5kb from those peaks.
overlaps <- ol$peaklist[["gr1///gr2"]]
## ============== old style ===========
## data(TSS.human.GRCh37)
## overlaps.anno <- annotatePeakInBatch(overlaps, AnnotationData=annoData,
## output="overlapping", maxgap=5000L)
## overlaps.anno <- addGeneIDs(overlaps.anno, "org.Hs.eg.db", "symbol")
## ============== new style ===========
library(EnsDb.Hsapiens.v75) ##(hg19)
## create annotation file from EnsDb or TxDb
annoData <- annoGR(EnsDb.Hsapiens.v75, feature="gene")
info(annoData)
## annoGR object;
## # source: EnsDb.Hsapiens.v75
## # create at: Thu Feb 11 12:00:00 AM 2016 UTC
## # feature: gene
## # Db type: EnsDb
## # Type of Gene ID: Ensembl Gene ID
## # Supporting package: ensembldb
## # Db created by: ensembldb package from Bioconductor
## # script_version: 0.1.2
## # Creation time: Wed Mar 18 09:30:54 2015
## # ensembl_version: 75
## # ensembl_host: manny.i-med.ac.at
## # Organism: homo_sapiens
## # genome_build: GRCh37
## # DBSCHEMAVERSION: 1.0
annoData[1:2]
## annoGR object with 2 ranges and 1 metadata column:
## seqnames ranges strand | gene_name
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000000003 chrX [99883667, 99894988] - | TSPAN6
## ENSG00000000005 chrX [99839799, 99854882] + | TNMD
## -------
## seqinfo: 273 sequences from GRCh37 genome
overlaps.anno <- annotatePeakInBatch(overlaps, AnnotationData=annoData,
output="overlapping", maxgap=5000L)
overlaps.anno$gene_name <-
annoData$gene_name[match(overlaps.anno$feature,
names(annoData))]
head(overlaps.anno)
## GRanges object with 6 ranges and 11 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## X001.ENSG00000228327 chr1 [713791, 715578] * |
## X001.ENSG00000237491 chr1 [713791, 715578] * |
## X002.ENSG00000237491 chr1 [724851, 727191] * |
## X003.ENSG00000272438 chr1 [839467, 840090] * |
## X004.ENSG00000187634 chr1 [856361, 856999] * |
## X004.ENSG00000223764 chr1 [856361, 856999] * |
## peakNames
## <CharacterList>
## X001.ENSG00000228327 gr1__MACS_peak_13,gr2__region_0,gr2__region_1
## X001.ENSG00000237491 gr1__MACS_peak_13,gr2__region_0,gr2__region_1
## X002.ENSG00000237491 gr2__region_2,gr1__MACS_peak_14
## X003.ENSG00000272438 gr1__MACS_peak_16,gr2__region_3
## X004.ENSG00000187634 gr1__MACS_peak_17,gr2__region_4
## X004.ENSG00000223764 gr1__MACS_peak_17,gr2__region_4
## peak feature start_position
## <character> <character> <integer>
## X001.ENSG00000228327 001 ENSG00000228327 700237
## X001.ENSG00000237491 001 ENSG00000237491 714150
## X002.ENSG00000237491 002 ENSG00000237491 714150
## X003.ENSG00000272438 003 ENSG00000272438 840214
## X004.ENSG00000187634 004 ENSG00000187634 860260
## X004.ENSG00000223764 004 ENSG00000223764 852245
## end_position feature_strand insideFeature
## <integer> <character> <factor>
## X001.ENSG00000228327 714006 - overlapStart
## X001.ENSG00000237491 745440 + overlapStart
## X002.ENSG00000237491 745440 + inside
## X003.ENSG00000272438 851356 + upstream
## X004.ENSG00000187634 879955 + upstream
## X004.ENSG00000223764 856396 - overlapStart
## distancetoFeature shortestDistance
## <numeric> <integer>
## X001.ENSG00000228327 215 215
## X001.ENSG00000237491 -359 359
## X002.ENSG00000237491 10701 10701
## X003.ENSG00000272438 -747 124
## X004.ENSG00000187634 -3899 3261
## X004.ENSG00000223764 35 35
## fromOverlappingOrNearest gene_name
## <character> <character>
## X001.ENSG00000228327 Overlapping RP11-206L10.2
## X001.ENSG00000237491 Overlapping RP11-206L10.9
## X002.ENSG00000237491 Overlapping RP11-206L10.9
## X003.ENSG00000272438 Overlapping RP11-54O7.16
## X004.ENSG00000187634 Overlapping SAMD11
## X004.ENSG00000223764 Overlapping RP11-54O7.3
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
Once the peaks are annotated, the distribution of the distance to the nearest feature such as the transcription start sites (TSS) can be plotted. The sample code here plots the distribution of the aggregated peak scores and the number of peaks around the TSS.
gr1.copy <- gr1
gr1.copy$score <- 1
binOverFeature(gr1, gr1.copy, annotationData=annoData,
radius=5000, nbins=10, FUN=c(sum, length),
ylab=c("score", "count"),
main=c("Distribution of aggregated peak scores around TSS",
"Distribution of aggregated peak numbers around TSS"))
The distribution of the peaks over exon, intron, enhancer, proximal promoter, 5’ UTR and 3’ UTR can be summarized in peak centric or nucleotide centric view using the function assignChromosomeRegion. Note: setting nucleotideLevel = TRUE will give a nucleotide level distribution over different features.
if(require(TxDb.Hsapiens.UCSC.hg19.knownGene)){
aCR<-assignChromosomeRegion(gr1, nucleotideLevel=FALSE,
precedence=c("Promoters", "immediateDownstream",
"fiveUTRs", "threeUTRs",
"Exons", "Introns"),
TxDb=TxDb.Hsapiens.UCSC.hg19.knownGene)
barplot(aCR$percentage)
}
Detailed Use Cases and Scenarios
Here we describe some details in using different functions in ChIPpeakAnno for different tasks. As shown in the last section, the common workflow includes: loading called peaks from BED, GFF, or other formats; evaluating and visualizing the concordance among the biological replicates; combining peaks from replicates; preparing genomic annotation(s) as GRanges; associating/annotating peaks with the annotation(s); summarizing peak distributions over exon, intron, enhancer, proximal promoter, 5’UTR and 3’UTR regions; retrieving the sequences around the peaks; and enrichment analysis of GO and biological pathway. We also implement the functions to plot the heatmap of given peak ranges, and perform permutation test to determine if there is an association between two sets of peaks.
Determine the overlapping peaks and visualize the overlaps with Venn diagram
It is important to evaluate the concordance among the peaks of biological replicates. Prior to associating features of interest with the peaks, it is a common practice to combine the peaks from biological replicates. Also, it is biologically interesting to obtain overlapping peaks from different ChIP-seq experiments to imply the potential formation of transcription factor complexes. ChIPpeakAnno implemented functions to achieve those goals and quantitatively determine the significance of peak overlaps and generate a Venn diagram for visualization.
Here is the sample code to obtain the overlapping peaks with maximum gap of 1kb for two peak ranges.
peaks1 <- GRanges(seqnames=c("1", "2", "3", "4", "5", "6",
"2", "6", "6", "6", "6", "5"),
ranges=IRanges(start=c(967654, 2010897, 2496704, 3075869,
3123260, 3857501, 201089, 1543200,
1557200, 1563000, 1569800, 167889600),
end= c(967754, 2010997, 2496804, 3075969,
3123360, 3857601, 201089, 1555199,
1560599, 1565199, 1573799, 167893599),
names=paste("Site", 1:12, sep="")),
strand="+")
peaks2 <- GRanges(seqnames=c("1", "2", "3", "4", "5", "6", "1", "2", "3",
"4", "5", "6", "6", "6", "6", "6", "5"),
ranges=IRanges(start=c(967659, 2010898, 2496700,
3075866, 3123260, 3857500,
96765, 201089, 249670, 307586,
312326, 385750, 1549800,
1554400, 1565000, 1569400,
167888600),
end=c(967869, 2011108, 2496920,
3076166,3123470, 3857780,
96985, 201299, 249890, 307796,
312586, 385960, 1550599, 1560799,
1565399, 1571199, 167888999),
names=paste("t", 1:17, sep="")),
strand=c("+", "+", "+", "+", "+", "+", "-", "-", "-",
"-", "-", "-", "+", "+", "+", "+", "+"))
ol <- findOverlapsOfPeaks(peaks1, peaks2, maxgap=1000)
peaklist <- ol$peaklist
The function findOverlapsOfPeaks returns an object of overlappingPeaks, which contains there elements: venn_cnt, peaklist (a list consists of all overlapping peaks or unique peaks), and overlappingPeaks (a list of data frame consists of the annotation of all the overlapping peaks).
Within the overlappingPeaks element of the overlappingPeaks object ol (which is also a list), the element “peaks1///peaks2” is a data frame representing the overlapping peaks with maximum gap of 1kb between the two peak lists. Using the overlapFeature column in this data frame, a pie graph can be generated to describe the distribution of the features of the relative positions of peaks1 to peaks2 for the overlapping peaks.
overlappingPeaks <- ol$overlappingPeaks
names(overlappingPeaks)
## [1] "peaks1///peaks2"
dim(overlappingPeaks[["peaks1///peaks2"]])
## [1] 13 14
overlappingPeaks[["peaks1///peaks2"]][1:2, ]
## peaks1 seqnames start end width strand
## peaks1__Site1_peaks2__t1 peaks1__Site1 1 967654 967754 101 +
## peaks1__Site7_peaks2__t8 peaks1__Site7 2 201089 201089 1 +
## peaks2 seqnames start end width strand
## peaks1__Site1_peaks2__t1 peaks2__t1 1 967659 967869 211 +
## peaks1__Site7_peaks2__t8 peaks2__t8 2 201089 201299 211 -
## overlapFeature shortestDistance
## peaks1__Site1_peaks2__t1 overlapStart 5
## peaks1__Site7_peaks2__t8 overlapEnd 0
pie1(table(overlappingPeaks[["peaks1///peaks2"]]$overlapFeature))
The following code returns the merged overlapping peaks from the peaklist object.
peaklist[["peaks1///peaks2"]]
## GRanges object with 11 ranges and 1 metadata column:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## [1] 1 [ 967654, 967869] + |
## [2] 2 [ 201089, 201299] * |
## [3] 2 [ 2010897, 2011108] + |
## [4] 3 [ 2496700, 2496920] + |
## [5] 4 [ 3075866, 3076166] + |
## [6] 5 [ 3123260, 3123470] + |
## [7] 5 [167888600, 167893599] + |
## [8] 6 [ 1543200, 1560799] + |
## [9] 6 [ 1563000, 1565399] + |
## [10] 6 [ 1569400, 1573799] + |
## [11] 6 [ 3857500, 3857780] + |
## peakNames
## <CharacterList>
## [1] peaks1__Site1,peaks2__t1
## [2] peaks1__Site7,peaks2__t8
## [3] peaks1__Site2,peaks2__t2
## [4] peaks2__t3,peaks1__Site3
## [5] peaks2__t4,peaks1__Site4
## [6] peaks1__Site5,peaks2__t5
## [7] peaks2__t17,peaks1__Site12
## [8] peaks1__Site8,peaks2__t13,peaks2__t14,...
## [9] peaks1__Site10,peaks2__t15
## [10] peaks2__t16,peaks1__Site11
## [11] peaks2__t6,peaks1__Site6
## -------
## seqinfo: 6 sequences from an unspecified genome; no seqlengths
The peaks in peaks1 but not overlap with the peaks in peaks2 can be obtained with:
peaklist[["peaks1"]]
## NULL
The peaks in peaks2 but not overlap with the peaks in peaks1 can be obtained with:
peaklist[["peaks2"]]
## GRanges object with 5 ranges and 1 metadata column:
## seqnames ranges strand | peakNames
## <Rle> <IRanges> <Rle> | <CharacterList>
## [1] 1 [ 96765, 96985] - | peaks2__t7
## [2] 3 [249670, 249890] - | peaks2__t9
## [3] 4 [307586, 307796] - | peaks2__t10
## [4] 5 [312326, 312586] - | peaks2__t11
## [5] 6 [385750, 385960] - | peaks2__t12
## -------
## seqinfo: 6 sequences from an unspecified genome; no seqlengths
Venn diagram can be generated by the function makeVennDiagram using the output of findOverlapsOfPeaks as an input.
The makeVennDiagram also outputs p-values indicating whether the overlapping is significant.
makeVennDiagram(ol, totalTest=1e+2)
## $p.value
## peaks1 peaks2 pval
## [1,] 1 1 5.890971e-12
##
## $vennCounts
## peaks1 peaks2 Counts
## [1,] 0 0 83
## [2,] 0 1 5
## [3,] 1 0 0
## [4,] 1 1 12
## attr(,"class")
## [1] "VennCounts"
Alternatively, users have the option to use other tools to plot Venn diagram. The following code demonstrates how to use a third party R package Vernerable with the output from the function findOverlapsOfPeaks.
# install.packages("Vennerable", repos="http://R-Forge.R-project.org",
# type="source")
# library(Vennerable)
# venn_cnt2venn <- function(venn_cnt){
# n <- which(colnames(venn_cnt)=="Counts") - 1
# SetNames=colnames(venn_cnt)[1:n]
# Weight=venn_cnt[,"Counts"]
# names(Weight) <- apply(venn_cnt[,1:n], 1, paste, collapse="")
# Venn(SetNames=SetNames, Weight=Weight)
# }
#
# v <- venn_cnt2venn(ol$venn_cnt)
# plot(v)
The findOverlapsOfPeaks function accepts up to 5 peak lists for overlapping peaks. The following code is an example for 3 peak lists.
peaks3 <- GRanges(seqnames=c("1", "2", "3", "4", "5",
"6", "1", "2", "3", "4"),
ranges=IRanges(start=c(967859, 2010868, 2496500, 3075966,
3123460, 3851500, 96865, 201189,
249600, 307386),
end= c(967969, 2011908, 2496720, 3076166,
3123470, 3857680, 96985, 201299,
249890, 307796),
names=paste("p", 1:10, sep="")),
strand=c("+", "+", "+", "+", "+",
"+", "-", "-", "-", "-"))
ol <- findOverlapsOfPeaks(peaks1, peaks2, peaks3, maxgap=1000,
connectedPeaks="min")
makeVennDiagram(ol, totalTest=1e+2)
## $p.value
## peaks1 peaks2 peaks3 pval
## [1,] 0 1 1 1.123492e-09
## [2,] 1 0 1 5.131347e-06
## [3,] 1 1 0 5.890971e-12
##
## $vennCounts
## peaks1 peaks2 peaks3 Counts
## [1,] 0 0 0 83
## [2,] 0 0 1 0
## [3,] 0 1 0 2
## [4,] 0 1 1 3
## [5,] 1 0 0 0
## [6,] 1 0 1 0
## [7,] 1 1 0 5
## [8,] 1 1 1 7
## attr(,"class")
## [1] "VennCounts"
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 replicates. 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. We also implement a permTest function, in which the number of totalTest is not required. For more details about the permTest, go to section 4.11.
Generate annotation data
One main function of the ChIPpeakAnno package is to annotate the positional relationship between the peaks and the known genomic features, such as TSS, 5’UTR, 3’UTR etc. Constructing and choosing the appropriate annotation data is crucial for this process.
To simplify this process, we precompiled the annotation data for the transcriptional starting sites (TSS) of various species (with different genome assembly versions). Those TSS annotations include TSS.human.NCBI36, TSS.human.GRCh37, TSS.human.GRCh38, TSS.mouse.NCBIM37, TSS.mouse.GRCm38, TSS.rat.RGSC3.4, TSS.rat.Rnor_5.0, TSS.zebrafish.Zv8, and TSS.zebrafish.Zv9. Those precompiled annotations can be loaded by R data() function.
To annotate the peaks with other genomic features, we provide the function getAnnotation with the argument featureType, e.g., “Exon” to obtain the nearest exon, “miRNA” to find the nearest miRNA, and “5utr” or “3utr” to locate the overlapping “5’UTR” or “3’UTR”. Another parameter for getAnnotation is the name of the appropriate biomaRt dataset, for example, drerio_gene_ensembl for zebrafish genome, mmusculus_gene_ensembl for mouse genome and rnorvegicus_gene_ensembl for rat genome. For a list of available biomaRt and dataset, please refer to the biomaRt package documentation2. For the detailed usage of getAnnotation, you can type ?getAnnotation in R.
You can also determine to use the biologically appropriate database that is related with your biological questions. For example, if you have a list of transcription factor binding sites from literatures and are interested in locating the nearest TSS and the distance to it for the peak lists. You can pass the custom annotation dataset into the function annotatePeakInBatch as GRanges. Use toGRanges function for conversion if necessary.
To facilitate the creation and documentation of the annotation data, we implement an annoGR class, which is an extension of GRanges class, to represent the annotation data. An annoGR object can be constructed from EnsDb, TxDb, or the user defined GRanges object by calling the annoGR function. The advantage of this class is that it contains the meta data such as the source and the timestamp (date) of the data source. Use ?annoGR for more information.
One sample use case is that you want to annotate only the known genes, not other transcript products, such as pseudo genes. A code snippet to build this annotation data using TranscriptDb TxDb.Hsapiens.UCSC.hg19.knownGene with annoGR is:
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
annoData <- annoGR(TxDb.Hsapiens.UCSC.hg19.knownGene, feature="gene")
info(annoData)
## annoGR object;
## # source: TxDb.Hsapiens.UCSC.hg19.knownGene
## # create at: Thu Feb 11 12:00:00 AM 2016 UTC
## # feature: gene
## # Db type: TxDb
## # Supporting package: GenomicFeatures
## # Data source: UCSC
## # Genome: hg19
## # Organism: Homo sapiens
## # Taxonomy ID: 9606
## # UCSC Table: knownGene
## # Resource URL: http://genome.ucsc.edu/
## # Type of Gene ID: Entrez Gene ID
## # Full dataset: yes
## # miRBase build ID: GRCh37
## # transcript_nrow: 82960
## # exon_nrow: 289969
## # cds_nrow: 237533
## # Db created by: GenomicFeatures package from Bioconductor
## # Creation time: 2015-10-07 18:11:28 +0000 (Wed, 07 Oct 2015)
## # GenomicFeatures version at creation time: 1.21.30
## # RSQLite version at creation time: 1.0.0
## # DBSCHEMAVERSION: 1.1
annoData
## annoGR object with 23056 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## 1 chr19 [ 58858172, 58874214] -
## 10 chr8 [ 18248755, 18258723] +
## 100 chr20 [ 43248163, 43280376] -
## 1000 chr18 [ 25530930, 25757445] -
## 10000 chr1 [243651535, 244006886] -
## ... ... ... ...
## 9991 chr9 [114979995, 115095944] -
## 9992 chr21 [ 35736323, 35743440] +
## 9993 chr22 [ 19023795, 19109967] -
## 9994 chr6 [ 90539619, 90584155] +
## 9997 chr22 [ 50961997, 50964905] -
## -------
## seqinfo: 93 sequences (1 circular) from hg19 genome
Find the nearest feature and the distance to the feature for the peaklists
With the annotation data, you can annotate the peaks identified from ChIP-seq or ChIP-chip experiments to retrieve the nearest gene and distance to the corresponding TSS of the gene.
For example, using the annoGR object generated in previous section as AnnotationData, the first 6 peaks in the myPeakList are annotated with the following code:
data(myPeakList)
annotatedPeak <- annotatePeakInBatch(myPeakList[1:6],
AnnotationData = annoData)
annotatedPeak[1:3]
## GRanges object with 3 ranges and 9 metadata columns:
## seqnames ranges strand | peak
## <Rle> <IRanges> <Rle> | <character>
## X1_93_556427.100288069 chr1 [556660, 556760] * | X1_93_556427
## X1_41_559455.100288069 chr1 [559774, 559874] * | X1_41_559455
## X1_12_703729.100288069 chr1 [703885, 703985] * | X1_12_703729
## feature start_position end_position
## <character> <integer> <integer>
## X1_93_556427.100288069 100288069 700245 714068
## X1_41_559455.100288069 100288069 700245 714068
## X1_12_703729.100288069 100288069 700245 714068
## feature_strand insideFeature distancetoFeature
## <character> <factor> <numeric>
## X1_93_556427.100288069 - downstream 157408
## X1_41_559455.100288069 - downstream 154294
## X1_12_703729.100288069 - inside 10183
## shortestDistance fromOverlappingOrNearest
## <integer> <character>
## X1_93_556427.100288069 143485 NearestLocation
## X1_41_559455.100288069 140371 NearestLocation
## X1_12_703729.100288069 3640 NearestLocation
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths
As discussed in the previous section, all the genomic locations of the human genes have been precompiled, such as TSS.human.NCBI36 dataset, using function getAnnotation. You can pass it to the argument annotaionData of the annotatePeakInBatch function.
data(TSS.human.NCBI36)
annotatedPeak <- annotatePeakInBatch(myPeakList[1:6],
AnnotationData=TSS.human.NCBI36)
annotatedPeak[1:3]
## GRanges object with 3 ranges and 9 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## X1_93_556427.ENSG00000212875 chr1 [556660, 556760] * |
## X1_41_559455.ENSG00000212678 chr1 [559774, 559874] * |
## X1_12_703729.ENSG00000197049 chr1 [703885, 703985] * |
## peak feature start_position
## <character> <character> <integer>
## X1_93_556427.ENSG00000212875 X1_93_556427 ENSG00000212875 556318
## X1_41_559455.ENSG00000212678 X1_41_559455 ENSG00000212678 559620
## X1_12_703729.ENSG00000197049 X1_12_703729 ENSG00000197049 711184
## end_position feature_strand insideFeature
## <integer> <character> <factor>
## X1_93_556427.ENSG00000212875 557859 + inside
## X1_41_559455.ENSG00000212678 560165 + inside
## X1_12_703729.ENSG00000197049 712376 + upstream
## distancetoFeature shortestDistance
## <numeric> <integer>
## X1_93_556427.ENSG00000212875 342 342
## X1_41_559455.ENSG00000212678 154 154
## X1_12_703729.ENSG00000197049 -7299 7199
## fromOverlappingOrNearest
## <character>
## X1_93_556427.ENSG00000212875 NearestLocation
## X1_41_559455.ENSG00000212678 NearestLocation
## X1_12_703729.ENSG00000197049 NearestLocation
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths
You can also pass the user defined features as annotationData. A pie chart can be plotted to show the peak distribution among the features after annotation.
myPeak1 <- GRanges(seqnames=c("1", "2", "3", "4", "5", "6",
"2", "6", "6", "6", "6", "5"),
ranges=IRanges(start=c(967654, 2010897, 2496704, 3075869,
3123260, 3857501, 201089, 1543200,
1557200, 1563000, 1569800, 167889600),
end= c(967754, 2010997, 2496804, 3075969,
3123360, 3857601, 201089, 1555199,
1560599, 1565199, 1573799, 167893599),
names=paste("Site", 1:12, sep="")))
TFbindingSites <- GRanges(seqnames=c("1", "2", "3", "4", "5", "6", "1", "2",
"3", "4", "5", "6", "6", "6", "6", "6",
"5"),
ranges=IRanges(start=c(967659, 2010898, 2496700,
3075866, 3123260, 3857500,
96765, 201089, 249670, 307586,
312326, 385750, 1549800,
1554400, 1565000, 1569400,
167888600),
end=c(967869, 2011108, 2496920,
3076166,3123470, 3857780,
96985, 201299, 249890, 307796,
312586, 385960, 1550599, 1560799,
1565399, 1571199, 167888999),
names=paste("t", 1:17, sep="")),
strand=c("+", "+", "+", "+", "+", "+", "-", "-", "-",
"-", "-", "-", "+", "+", "+", "+", "+"))
annotatedPeak2 <- annotatePeakInBatch(myPeak1, AnnotationData=TFbindingSites)
annotatedPeak2[1:3]
## GRanges object with 3 ranges and 9 metadata columns:
## seqnames ranges strand | peak feature
## <Rle> <IRanges> <Rle> | <character> <character>
## Site1.t1 chr1 [ 967654, 967754] * | Site1 t1
## Site2.t2 chr2 [2010897, 2010997] * | Site2 t2
## Site3.t3 chr3 [2496704, 2496804] * | Site3 t3
## start_position end_position feature_strand insideFeature
## <integer> <integer> <character> <factor>
## Site1.t1 967659 967869 + overlapStart
## Site2.t2 2010898 2011108 + overlapStart
## Site3.t3 2496700 2496920 + inside
## distancetoFeature shortestDistance fromOverlappingOrNearest
## <numeric> <integer> <character>
## Site1.t1 -5 5 NearestLocation
## Site2.t2 -1 1 NearestLocation
## Site3.t3 4 4 NearestLocation
## -------
## seqinfo: 6 sequences from an unspecified genome; no seqlengths
pie1(table(as.data.frame(annotatedPeak2)$insideFeature))
Another example of user specific AnnotationData is to annotate peaks by promoters, defined as upstream 5K to downstream 500bp from TSS. The sample code here demonstrates using the GenomicFeatures::promoters function to build a custom annotation dataset and annotate the peaks with this user defined promoter annotations.
library(ChIPpeakAnno)
data(myPeakList)
data(TSS.human.NCBI36)
annotationData <- promoters(TSS.human.NCBI36, upstream=5000, downstream=500)
annotatedPeak <- annotatePeakInBatch(myPeakList[1:6,],
AnnotationData=annotationData,
output="overlapping")
annotatedPeak[1:3]
## GRanges object with 3 ranges and 9 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## X1_93_556427.ENSG00000209341 chr1 [556660, 556760] * |
## X1_93_556427.ENSG00000209344 chr1 [556660, 556760] * |
## X1_93_556427.ENSG00000209346 chr1 [556660, 556760] * |
## peak feature start_position
## <character> <character> <integer>
## X1_93_556427.ENSG00000209341 X1_93_556427 ENSG00000209341 554314
## X1_93_556427.ENSG00000209344 X1_93_556427 ENSG00000209344 555569
## X1_93_556427.ENSG00000209346 X1_93_556427 ENSG00000209346 555643
## end_position feature_strand insideFeature
## <integer> <character> <factor>
## X1_93_556427.ENSG00000209341 559813 - inside
## X1_93_556427.ENSG00000209344 561068 - inside
## X1_93_556427.ENSG00000209346 561142 - inside
## distancetoFeature shortestDistance
## <numeric> <integer>
## X1_93_556427.ENSG00000209341 3153 2346
## X1_93_556427.ENSG00000209344 4408 1091
## X1_93_556427.ENSG00000209346 4482 1017
## fromOverlappingOrNearest
## <character>
## X1_93_556427.ENSG00000209341 Overlapping
## X1_93_556427.ENSG00000209344 Overlapping
## X1_93_556427.ENSG00000209346 Overlapping
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths
In the function annotatyePeakInBatch, various parameters can be adjusted to specify the way to calculate the distance and how the features are selected. For example, PeakLocForDistance is to specify the location of the peak for distance calculation: “middle” (recommended) means using the middle of the peak, and “start” (default, for backward compatibility) means using the start of the peak to calculate the distance to the features. Similarly, FeatureLocForDistance is to specify the location of the feature for distance calculation: “middle” means using the middle of the feature, “start” means using the start of the feature to calculate the distance from the peak to the feature; “TSS” (default) means using the start of the feature when the feature is on plus strand and using the end of feature when the feature is on minus strand; “geneEnd” means using end of the feature when feature is on plus strand and using start of feature when feature is on minus strand.
The argument “output” specifies the characteristics of the output of the annotated features. The default is “nearestLocation”, which means to output the nearest features calculated as PeakLocForDistance-FeatureLocForDistance; “overlapping” will output the overlapping features within the maximum gap specified as maxgap between the peak range and feature range; “shortestDistance” will output the nearest features; “both” will output all the nearest features, in addition, will output any features that overlap the peak that are not the nearest features. other options see ?annotatePeakInBatch.
Find the overlapping and flanking features
In addition to annotate peaks to nearest genes, ChIPpeakAnno can also reports all overlapping and flanking genes by setting output=“both” and maxgap in annotatePeakInBatch. For example, it outputs all overlapping and flanking genes within 5kb plus nearest genes if set maxgap = 5000 and output =“both”.
annotatedPeak <- annotatePeakInBatch(myPeakList[1:6],
AnnotationData = annoData,
output="both", maxgap=5000)
head(annotatedPeak)
## GRanges object with 6 ranges and 9 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## X1_93_556427.100288069 chr1 [ 556660, 556760] * |
## X1_41_559455.100288069 chr1 [ 559774, 559874] * |
## X1_12_703729.100288069 chr1 [ 703885, 703985] * |
## X1_20_925025.84808 chr1 [ 926058, 926158] * |
## X1_11_1041174.54991 chr1 [1041646, 1041746] * |
## X1_14_1269014.1855 chr1 [1270239, 1270339] * |
## peak feature start_position
## <character> <character> <integer>
## X1_93_556427.100288069 X1_93_556427 100288069 700245
## X1_41_559455.100288069 X1_41_559455 100288069 700245
## X1_12_703729.100288069 X1_12_703729 100288069 700245
## X1_20_925025.84808 X1_20_925025 84808 910579
## X1_11_1041174.54991 X1_11_1041174 54991 1017198
## X1_14_1269014.1855 X1_14_1269014 1855 1270658
## end_position feature_strand insideFeature
## <integer> <character> <factor>
## X1_93_556427.100288069 714068 - downstream
## X1_41_559455.100288069 714068 - downstream
## X1_12_703729.100288069 714068 - inside
## X1_20_925025.84808 917473 - upstream
## X1_11_1041174.54991 1051736 - inside
## X1_14_1269014.1855 1284492 - downstream
## distancetoFeature shortestDistance
## <numeric> <integer>
## X1_93_556427.100288069 157408 143485
## X1_41_559455.100288069 154294 140371
## X1_12_703729.100288069 10183 3640
## X1_20_925025.84808 -8585 8585
## X1_11_1041174.54991 10090 9990
## X1_14_1269014.1855 14253 319
## fromOverlappingOrNearest
## <character>
## X1_93_556427.100288069 NearestLocation
## X1_41_559455.100288069 NearestLocation
## X1_12_703729.100288069 NearestLocation
## X1_20_925025.84808 NearestLocation
## X1_11_1041174.54991 NearestLocation
## X1_14_1269014.1855 Overlapping
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths
Add other feature IDs to the annotated peaks
Additional annotations features such as entrez ID, gene symbol and gene name can be added with the function addGeneIDs. The annotated peaks can be saved as an Excel file or plotted for visualizing the peak distribution relative to the genomic features of interest. Here is an example to add gene symbol to the annotated peaks. Use ?addGeneIDs for more information.
data(annotatedPeak)
library(org.Hs.eg.db)
addGeneIDs(annotatedPeak[1:6], orgAnn="org.Hs.eg.db", IDs2Add=c("symbol"))
## GRanges object with 6 ranges and 9 metadata columns:
## seqnames ranges strand |
## <Rle> <IRanges> <Rle> |
## X1_11_100272487.ENSG00000202254 1 [100272801, 100272900] + |
## X1_11_108905539.ENSG00000186086 1 [108906026, 108906125] + |
## X1_11_110106925.ENSG00000065135 1 [110107267, 110107366] + |
## X1_11_110679983.ENSG00000197106 1 [110680469, 110680568] + |
## X1_11_110681677.ENSG00000197106 1 [110682125, 110682224] + |
## X1_11_110756560.ENSG00000116396 1 [110756823, 110756922] + |
## peak feature
## <character> <character>
## X1_11_100272487.ENSG00000202254 1_11_100272487 ENSG00000202254
## X1_11_108905539.ENSG00000186086 1_11_108905539 ENSG00000186086
## X1_11_110106925.ENSG00000065135 1_11_110106925 ENSG00000065135
## X1_11_110679983.ENSG00000197106 1_11_110679983 ENSG00000197106
## X1_11_110681677.ENSG00000197106 1_11_110681677 ENSG00000197106
## X1_11_110756560.ENSG00000116396 1_11_110756560 ENSG00000116396
## start_position end_position
## <numeric> <numeric>
## X1_11_100272487.ENSG00000202254 100257218 100257309
## X1_11_108905539.ENSG00000186086 108918435 109013624
## X1_11_110106925.ENSG00000065135 110091233 110136975
## X1_11_110679983.ENSG00000197106 110693108 110744824
## X1_11_110681677.ENSG00000197106 110693108 110744824
## X1_11_110756560.ENSG00000116396 110753965 110776666
## insideFeature distancetoFeature
## <character> <numeric>
## X1_11_100272487.ENSG00000202254 downstream 15582
## X1_11_108905539.ENSG00000186086 upstream -12410
## X1_11_110106925.ENSG00000065135 inside 16033
## X1_11_110679983.ENSG00000197106 upstream -12640
## X1_11_110681677.ENSG00000197106 upstream -10984
## X1_11_110756560.ENSG00000116396 inside 2857
## shortestDistance
## <numeric>
## X1_11_100272487.ENSG00000202254 15491
## X1_11_108905539.ENSG00000186086 12310
## X1_11_110106925.ENSG00000065135 16033
## X1_11_110679983.ENSG00000197106 12540
## X1_11_110681677.ENSG00000197106 10884
## X1_11_110756560.ENSG00000116396 2857
## fromOverlappingOrNearest symbol
## <character> <factor>
## X1_11_100272487.ENSG00000202254 NearestStart <NA>
## X1_11_108905539.ENSG00000186086 NearestStart NBPF6
## X1_11_110106925.ENSG00000065135 NearestStart GNAI3
## X1_11_110679983.ENSG00000197106 NearestStart SLC6A17
## X1_11_110681677.ENSG00000197106 NearestStart SLC6A17
## X1_11_110756560.ENSG00000116396 NearestStart KCNC4
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths
addGeneIDs(annotatedPeak$feature[1:6], orgAnn="org.Hs.eg.db",
IDs2Add=c("symbol"))
## ensembl_gene_id symbol
## 1 ENSG00000065135 GNAI3
## 2 ENSG00000116396 KCNC4
## 3 ENSG00000197106 SLC6A17
## 4 ENSG00000186086 NBPF6
## 5 ENSG00000202254 <NA>
Obtain the sequences surrounding the peaks
Here is an example to get the sequences of the peaks plus 20 bp upstream and downstream for PCR validation or motif discovery.
peaks <- GRanges(seqnames=c("NC_008253", "NC_010468"),
ranges=IRanges(start=c(100, 500),
end=c(300, 600),
names=c("peak1", "peak2")))
library(BSgenome.Ecoli.NCBI.20080805)
peaksWithSequences <- getAllPeakSequence(peaks, upstream=20,
downstream=20, genome=Ecoli)
The obtained sequences can be converted to fasta format for motif discovery by calling the function write2FASTA.
write2FASTA(peaksWithSequences,"test.fa")
Heatmap for given feature/peak ranges
You can easily visualize and compare the binding patterns of raw signals of multiple ChIP-Seq experiments using function featureAlignedHeatmap and featureAlignedDistribution.
path <- system.file("extdata", package="ChIPpeakAnno")
files <- dir(path, "broadPeak")
data <- sapply(file.path(path, files), toGRanges, format="broadPeak")
names(data) <- gsub(".broadPeak", "", files)
ol <- findOverlapsOfPeaks(data)
#makeVennDiagram(ol)
features <- ol$peaklist[[length(ol$peaklist)]]
wid <- width(features)
feature.recentered <- feature.center <- features
start(feature.center) <- start(features) + floor(wid/2)
width(feature.center) <- 1
start(feature.recentered) <- start(feature.center) - 2000
end(feature.recentered) <- end(feature.center) + 2000
## here we also suggest importData function in bioconductor trackViewer package
## to import the coverage.
## compare rtracklayer, it will save you time when handle huge dataset.
library(rtracklayer)
files <- dir(path, "bigWig")
cvglists <- sapply(file.path(path, files), import,
format="BigWig",
which=feature.recentered,
as="RleList")
names(cvglists) <- gsub(".bigWig", "", files)
sig <- featureAlignedSignal(cvglists, feature.center,
upstream=2000, downstream=2000)
heatmap <- featureAlignedHeatmap(sig, feature.center,
upstream=2000, downstream=2000,
upper.extreme=c(3,.5,4))
featureAlignedDistribution(sig, feature.center,
upstream=2000, downstream=2000,
type="l")
Output a summary of motif occurrences in the peaks.
Here is an example to search the motifs in the binding peaks. The motif patterns to be searched are saved in the file examplepattern.fa.
peaks <- GRanges(seqnames=c("NC_008253", "NC_010468"),
ranges=IRanges(start=c(100, 500),
end=c(300, 600),
names=c("peak1", "peak2")))
filepath <- system.file("extdata", "examplePattern.fa", package="ChIPpeakAnno")
readLines(filepath)
## [1] ">ExamplePattern" "GGNCCK" ">ExamplePattern" "AACCNM"
library(BSgenome.Ecoli.NCBI.20080805)
summarizePatternInPeaks(patternFilePath=filepath, format="fasta", skip=0L,
BSgenomeName=Ecoli, peaks=peaks)
## n.peaksWithPattern n.totalPeaks Pattern
## [1,] "0" "2" "GGNCCK"
## [2,] "1" "2" "AACCNM"
Obtain the enriched Gene Ontology (GO) terms or reactome/KEGG terms for the genes near the peaks
With the annotated peak data, you can call the function getEnrichedGO to obtain a list of enriched GO terms. For pathway analysis, you can call function getEnrichedPATH using reactome or KEGG database.
In the following sample code we used a subset of the annotatedPeak (the first 500 peaks) for demonstration. All annotated peaks should be used in the real situation.
library(org.Hs.eg.db)
over <- getEnrichedGO(annotatedPeak[1:500], orgAnn="org.Hs.eg.db",
maxP=0.01, multiAdj=FALSE, minGOterm=10,
multiAdjMethod="", condense=FALSE)
head(over[["bp"]][, -3])
## go.id go.term Ontology count.InDataset
## 1 GO:0006644 phospholipid metabolic process BP 8
## 2 GO:0006644 phospholipid metabolic process BP 8
## 3 GO:0006644 phospholipid metabolic process BP 8
## 4 GO:0006644 phospholipid metabolic process BP 8
## 5 GO:0006644 phospholipid metabolic process BP 8
## 6 GO:0006644 phospholipid metabolic process BP 8
## count.InGenome pvalue totaltermInDataset totaltermInGenome EntrezID
## 1 355 0.008592944 11331 1413832 27329
## 2 355 0.008592944 11331 1413832 55650
## 3 355 0.008592944 11331 1413832 9588
## 4 355 0.008592944 11331 1413832 347735
## 5 355 0.008592944 11331 1413832 9890
## 6 355 0.008592944 11331 1413832 255738
head(over[["cc"]][, -3])
## go.id go.term Ontology count.InDataset count.InGenome
## 1 GO:0001533 cornified envelope CC 9 46
## 2 GO:0001533 cornified envelope CC 9 46
## 3 GO:0001533 cornified envelope CC 9 46
## 4 GO:0001533 cornified envelope CC 9 46
## 5 GO:0001533 cornified envelope CC 9 46
## 6 GO:0001533 cornified envelope CC 9 46
## pvalue totaltermInDataset totaltermInGenome EntrezID
## 1 1.357295e-10 3118 381270 23254
## 2 1.357295e-10 3118 381270 353131
## 3 1.357295e-10 3118 381270 100129271
## 4 1.357295e-10 3118 381270 353137
## 5 1.357295e-10 3118 381270 149018
## 6 1.357295e-10 3118 381270 26239
head(over[["mf"]][, -3])
## go.id go.term Ontology count.InDataset
## 1 GO:0004622 lysophospholipase activity MF 2
## 2 GO:0004622 lysophospholipase activity MF 2
## 3 GO:0022841 potassium ion leak channel activity MF 2
## 4 GO:0022841 potassium ion leak channel activity MF 2
## 5 GO:0035198 miRNA binding MF 2
## 6 GO:0035198 miRNA binding MF 2
## count.InGenome pvalue totaltermInDataset totaltermInGenome EntrezID
## 1 14 0.007446405 2232 237608 127018
## 2 14 0.007446405 2232 237608 5321
## 3 16 0.009698147 2232 237608 3776
## 4 16 0.009698147 2232 237608 3775
## 5 13 0.006422469 2232 237608 192669
## 6 13 0.006422469 2232 237608 79727
Please note that the default setting of feature_id_type is “ensembl_gene_id”. If you are using TxDb as annotation data, please try to change it to “entrez_id”.
Please also note that org.Hs.eg.db is the GO gene mapping for Human, for other organisms, please refer to released organism annotations, or call function egOrgMap to get the name of annotation database.
egOrgMap("Mus musculus")
## [1] "org.Mm.eg.db"
egOrgMap("Homo sapiens")
## [1] "org.Hs.eg.db"
Perform permutation test to determine if there is an association between two sets of peaks
Given two peak lists from two transcript factors (TFs), one can ask whether DNA binding sites of the two TFs are correlated. Most tests are based on hypergeometric distribution, which require user to input an estimate of the total potential binding sites for a given TF. In contrast, peakPermTest implemented here is based on permutation test, which does not require user to supply an estimate of the total potential binding sites. The random peaks in the permutation test are generated using the distribution discovered from the input peaks for a given feature type (transcripts or exons) and relevant binding positions to the features (“TSS”, “geneEnd”). The width of the random peaks also follows the distribution of that of the input peaks.
Following are the sample codes to do the permTest:
if(interactive()){
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
cds <- unique(unlist(cdsBy(txdb)))
utr5 <- unique(unlist(fiveUTRsByTranscript(txdb)))
utr3 <- unique(unlist(threeUTRsByTranscript(txdb)))
set.seed(123)
utr3 <- utr3[sample.int(length(utr3), 1000)]
pt <- peakPermTest(utr3,
utr5[sample.int(length(utr5), 1000)],
maxgap=500,
TxDb=txdb, seed=1,
force.parallel=FALSE)
plot(pt)
## highly relevant peaks
ol <- findOverlaps(cds, utr3, maxgap=1)
pt1 <- peakPermTest(utr3,
c(cds[sample.int(length(cds), 500)],
cds[queryHits(ol)][sample.int(length(ol), 500)]),
maxgap=500,
TxDb=txdb, seed=1,
force.parallel=FALSE)
plot(pt1)
}
Alternatively, users can create a pool of peaks representing all potential binding sites with associated binding probabilities for random peak sampling (see ?preparePool). Here is an example to build a human pool of peaks using the transcription factor binding site clusters (V3) (see ?wgEncodeTfbsV3) downloaded from ENCODE with the HOT spots (?HOT.spots) removed. HOT spots are the genomic regions with high probability of being bound by many TFs in ChIP-seq experiments10. We suggest removing those HOT spots from the peak lists before performing permutation test to avoid the overestimation of the association between the two input peak lists. Users can also obtain the ENCODE blacklist for a given species. The blacklists were constructed by identifying consistently problematic regions over independent of cell line and type of experiment for each species in the ENCODE and modENCODE datasets11. Please note that some of those blacklist may need to be liftover-ed to the correct genome assembly.
if(interactive()){
data(HOT.spots)
data(wgEncodeTfbsV3)
hotGR <- reduce(unlist(HOT.spots))
removeOl <- function(.ele){
ol <- findOverlaps(.ele, hotGR)
if(length(ol)>0) .ele <- .ele[-unique(queryHits(ol))]
.ele
}
temp <- tempfile()
download.file(file.path("http://hgdownload.cse.ucsc.edu",
"goldenPath", "hg19", "encodeDCC",
"wgEncodeRegTfbsClustered",
"wgEncodeRegTfbsClusteredV3.bed.gz"), temp)
data <- toGRanges(gzfile(temp, "r"), header=FALSE, format="others",
colNames = c("seqnames", "start", "end", "TF"))
unlink(temp)
data <- split(data, data$TF)
TAF1 <- removeOl(data[["TAF1"]])
TEAD4 <- removeOl(data[["TEAD4"]])
pool <- new("permPool", grs=GRangesList(wgEncodeTfbsV3), N=length(TAF1))
pt <- peakPermTest(TAF1, TEAD4, pool=pool, ntimes=1000)
plot(pt)
}
