The ChIPpeakAnno user’s guide
26 July 2019
Abstract
The package is for facilitating the downstream analysis for ChIP-seq experiments. It includes functions to find the nearest gene, exon, miRNA or custom features such as the most conserved elements and other transcription factor binding sites supplied by users, retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms or pathways. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). Starting 3.4, permutation test has been added to determine whether there is a significant overlap between two sets of peaks. In addition, binding patterns of multiple transcription factors (TFs) or distributions of multiple epigenetic markers around genomic features could be visualized and compared easily using a side-by-side heatmap and density plot.Package
ChIPpeakAnno 3.18.2
Contents
- 1 Introduction
- 2 Quick start
- 3 An example of ChIP-seq analysis workflow using ChIPpeakAnno
- 4 Detailed Use Cases and Scenarios
- 4.1 Determine the overlapping peaks and visualize the overlaps with Venn diagram
- 4.2 Generate annotation data
- 4.3 Find the nearest feature and the distance to the feature for the peaklists
- 4.4 Find the overlapping and flanking features
- 4.5 Add other feature IDs to the annotated peaks
- 4.6 Obtain the sequences surrounding the peaks
- 4.7 Create heatmap for given feature/peak ranges
- 4.8 Output a summary of motif occurrences in the peaks.
- 4.9 Obtain the enriched Gene Ontology (GO) terms or reactome/KEGG terms for the genes near the peaks
- 4.10 Find peaks with bi-directional promoters
- 4.11 Perform permutation test to determine if there is a significant overlap between two sets of peaks
- 5 Citing ChIPpeakAnno
- 6 Session Info
- References
1 Introduction
Chromatin immunoprecipitation (ChIP) followed by DNA sequencing (ChIP-seq) and ChIP followed by genome tiling array analysis (ChIP-chip) have become prevalent high throughput technologies for identifying the binding sites of DNA-binding proteins genome-wise. A number of algorithms have been published to facilitate the identification of the binding sites of the DNA-binding proteins of interest. The identified binding sites as the list of peaks are usually converted to BED or bigwig files to be loaded to the UCSC genome browser as custom tracks for investigators to view the proximity to various genomic features such as genes, exons or conserved elements. However, clicking through the genome browser is a daunting task when the number of peaks gets large or the peaks spread widely across the genome.
Here we developed ChIPpeakAnno, a Bioconductor1 package, to facilitate the batch annotation of the peaks identified from ChIP-seq or ChIP-chip experiments. We implemented functionality to find the nearest gene, exon, miRNA or other custom features supplied by users such as the most conserved elements and other transcription factor binding sites leveraging GRanges. Since the genome annotation gets updated frequently, we have leveraged the biomaRt package to retrieve the annotation data on the fly. The users also have the flexibility to pass their own annotation data or annotation from GenomicFeatures as GRanges. We have also leveraged BSgenome and biomaRt to retrieve the sequences around the identified peak for peak validation or motif discovery2. To understand whether the identified peaks are enriched around genes with certain GO terms, we have implemented the Gene Ontology (GO) enrichment test in the ChIPpeakAnno package leveraging the hypergeometric test phyper in the stats package and integrated with the GO annotation from the GO.db package and multiplicity adjustment functions from the multtest package3–8. The pathway analysis using reactome or KEGG is also supported. Starting 3.4, we also implement the functions for permutation test to determine whether there is a significant overlap between two sets of peaks. In addition, binding patterns of multiple transcription factors (TFs) or distributions of multiple epigenetic markers around genomic features could be visualized and compared easily using a side-by-side heatmap and density plot.
2 Quick start
library(ChIPpeakAnno)
## import the MACS output
macs <- system.file("extdata", "MACS_peaks.xls", package="ChIPpeakAnno")
macsOutput <- toGRanges(macs, format="MACS")
## annotate the peaks with precompiled ensembl annotation
data(TSS.human.GRCh38)
macs.anno <- annotatePeakInBatch(macsOutput, AnnotationData=TSS.human.GRCh38)
## add gene symbols
library(org.Hs.eg.db)
macs.anno <- addGeneIDs(annotatedPeak=macs.anno,
orgAnn="org.Hs.eg.db",
IDs2Add="symbol")
if(interactive()){## annotate the peaks with UCSC annotation
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
ucsc.hg38.knownGene <- genes(TxDb.Hsapiens.UCSC.hg38.knownGene)
macs.anno <- annotatePeakInBatch(macsOutput,
AnnotationData=ucsc.hg38.knownGene)
macs.anno <- addGeneIDs(annotatedPeak=macs.anno,
orgAnn="org.Hs.eg.db",
feature_id_type="entrez_id",
IDs2Add="symbol")
}3 An example of 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 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. Please 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] TRUEgr1[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 seqlengthsgr1.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 seqlengthsgff <- system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno")
gr2 <- toGRanges(gff, format="GFF", header=FALSE, skip=3)
ol <- findOverlapsOfPeaks(gr1, gr2)
makeVennDiagram(ol)
Figure 1: venn diagram of overlaps for duplicated experiments
## $p.value
## gr1 gr2 pval
## [1,] 1 1 0
##
## $vennCounts
## gr1 gr2 Counts
## [1,] 0 0 0
## [2,] 0 1 61
## [3,] 1 0 62
## [4,] 1 1 166
## attr(,"class")
## [1] "VennCounts"A pie chart is used to demonstrate the overlap features of the common peaks.
pie1(table(ol$overlappingPeaks[["gr1///gr2"]]$overlapFeature))
Figure 2: Pie chart of common peaks among features
After finding the overlapping peaks, you can use annotatePeakInBatch to annotate the overlapping peaks with the genomic features in the AnnotationData within certain distance away specified by maxgap, which is 5kb in the following example.
overlaps <- ol$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 <- 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 genomeoverlaps.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.ENSG00000223764 chr1 856361-856999 * |
## X004.ENSG00000187634 chr1 856361-856999 * |
## peakNames peak
## <CharacterList> <character>
## X001.ENSG00000228327 gr1__MACS_peak_13,gr2__001,gr2__002 001
## X001.ENSG00000237491 gr1__MACS_peak_13,gr2__001,gr2__002 001
## X002.ENSG00000237491 gr2__003,gr1__MACS_peak_14 002
## X003.ENSG00000272438 gr1__MACS_peak_16,gr2__004 003
## X004.ENSG00000223764 gr1__MACS_peak_17,gr2__005 004
## X004.ENSG00000187634 gr1__MACS_peak_17,gr2__005 004
## feature start_position end_position
## <character> <integer> <integer>
## X001.ENSG00000228327 ENSG00000228327 700237 714006
## X001.ENSG00000237491 ENSG00000237491 714150 745440
## X002.ENSG00000237491 ENSG00000237491 714150 745440
## X003.ENSG00000272438 ENSG00000272438 840214 851356
## X004.ENSG00000223764 ENSG00000223764 852245 856396
## X004.ENSG00000187634 ENSG00000187634 860260 879955
## feature_strand insideFeature distancetoFeature
## <character> <factor> <numeric>
## X001.ENSG00000228327 - overlapStart 215
## X001.ENSG00000237491 + overlapStart -359
## X002.ENSG00000237491 + inside 10701
## X003.ENSG00000272438 + upstream -747
## X004.ENSG00000223764 - overlapStart 35
## X004.ENSG00000187634 + upstream -3899
## shortestDistance fromOverlappingOrNearest
## <integer> <character>
## X001.ENSG00000228327 215 Overlapping
## X001.ENSG00000237491 359 Overlapping
## X002.ENSG00000237491 10701 Overlapping
## X003.ENSG00000272438 124 Overlapping
## X004.ENSG00000223764 35 Overlapping
## X004.ENSG00000187634 3261 Overlapping
## gene_name
## <character>
## X001.ENSG00000228327 RP11-206L10.2
## X001.ENSG00000237491 RP11-206L10.9
## X002.ENSG00000237491 RP11-206L10.9
## X003.ENSG00000272438 RP11-54O7.16
## X004.ENSG00000223764 RP11-54O7.3
## X004.ENSG00000187634 SAMD11
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsOnce 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"))
Figure 3: Distribution of aggregated peak scores or peak numbers around transcript start sites
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.
Please note that 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)
}
Figure 4: Peak distribution over different genomic features
4 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 implemented the functions to plot the heatmap of given
peak ranges, and perform permutation test to determine if there is a significant overlap between two sets of peaks.
4.1 Determine the overlapping peaks and visualize the overlaps with Venn diagram
Prior to associating features of interest with the peaks, it is a common practice to evaluate the concordance among the peaks from biological replicates and 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$peaklistThe function findOverlapsOfPeaks returns an object of overlappingPeaks,
which contains there elements: venn_cnt, peaklist (a list of
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$overlappingPeaks
names(overlappingPeaks)## [1] "peaks1///peaks2"dim(overlappingPeaks[["peaks1///peaks2"]])## [1] 13 14overlappingPeaks[["peaks1///peaks2"]][1:2, ]## peaks1 seqnames start end width strand peaks2 seqnames
## Site1_t1 Site1 1 967654 967754 101 + t1 1
## Site2_t2 Site2 2 2010897 2010997 101 + t2 2
## start end width strand overlapFeature shortestDistance
## Site1_t1 967659 967869 211 + overlapStart 5
## Site2_t2 2010898 2011108 211 + overlapStart 1pie1(table(overlappingPeaks[["peaks1///peaks2"]]$overlapFeature))
Figure 5: Pie chart of common peaks among features
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 seqlengthsThe peaks in peaks1 but not overlap with the peaks in peaks2 can be obtained with:
peaklist[["peaks1"]]## NULLThe 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 seqlengthsVenn 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)
Figure 6: venn diagram of overlaps
## $p.value
## peaks1 peaks2 pval
## [1,] 1 1 5.890971e-12
##
## $vennCounts
## peaks1 peaks2 Counts
## [1,] 0 0 83
## [2,] 0 1 5
## [3,] 1 0 0
## [4,] 1 1 12
## attr(,"class")
## [1] "VennCounts"Alternatively, users have the option to use other tools to plot Venn diagram. The following code demonstrates how to use a third party R package Vernerable with the output from the function findOverlapsOfPeaks.
# install.packages("Vennerable", repos="http://R-Forge.R-project.org",
# type="source")
# library(Vennerable)
# venn_cnt2venn <- function(venn_cnt){
# n <- which(colnames(venn_cnt)=="Counts") - 1
# SetNames=colnames(venn_cnt)[1:n]
# Weight=venn_cnt[,"Counts"]
# names(Weight) <- apply(venn_cnt[,1:n], 1, base::paste, collapse="")
# Venn(SetNames=SetNames, Weight=Weight)
# }
#
# v <- venn_cnt2venn(ol$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)
Figure 7: venn diagram of overlaps for three input peak lists
## $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 the total number of potential peaks 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.
Hypergeometric test requires users to input an estimate of the total potential binding sites (peaks) for a given TF. To circumvent this requirement, we implemented a permutation test called permTest.
For more details about the permTest, go to section 4.11.
4.2 Generate annotation data
One main function of the ChIPpeakAnno package is to annotate peaks to 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 a list of annotation data for the
transcriptional starting sites (TSS) of various species (with
different genome assembly versions), such as
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. The precompiled annotations can be loaded by R data() function, e.g., data(TSS.human.GRCh38).
To annotate the peaks with other genomic features, please use 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, please
type ?getAnnotation in R.
In addition, a custom annotation dataset as GRanges, can be used in annotatePeakInBatch. We implemented toGRanges function for converting custom annotation dataset in other formats, such as UCSC BED/GFF format, or any user defined dataset such as RangedDate, to GRanges.
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.
An GRanges object can be also constructed from EnsDb or TxDb object by calling the toGRanges method. Use ?toGRanges for more information.
Here is the code snippet to build annotation data containing only the known genes, i.e., excluding other transcript products such as pseudo genes using TranscriptDb
TxDb.Hsapiens.UCSC.hg19.knownGene with toGRanges is:
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
annoData <- toGRanges(TxDb.Hsapiens.UCSC.hg19.knownGene, feature="gene")
annoData## GRanges 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 genome4.3 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 GRanges object generated in the 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 seqlengthsAs 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 | peak
## <Rle> <IRanges> <Rle> | <character>
## X1_93_556427.ENSG00000212875 chr1 556660-556760 * | X1_93_556427
## X1_41_559455.ENSG00000212678 chr1 559774-559874 * | X1_41_559455
## X1_12_703729.ENSG00000197049 chr1 703885-703985 * | X1_12_703729
## feature start_position end_position
## <character> <integer> <integer>
## X1_93_556427.ENSG00000212875 ENSG00000212875 556318 557859
## X1_41_559455.ENSG00000212678 ENSG00000212678 559620 560165
## X1_12_703729.ENSG00000197049 ENSG00000197049 711184 712376
## feature_strand insideFeature
## <character> <factor>
## X1_93_556427.ENSG00000212875 + inside
## X1_41_559455.ENSG00000212678 + inside
## X1_12_703729.ENSG00000197049 + 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 seqlengthsYou 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 seqlengthspie1(table(as.data.frame(annotatedPeak2)$insideFeature))
Figure 8: Pie chart of peak distribution among features
Another example of using user defined 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 | peak
## <Rle> <IRanges> <Rle> | <character>
## X1_93_556427.ENSG00000209341 chr1 556660-556760 * | X1_93_556427
## X1_93_556427.ENSG00000209344 chr1 556660-556760 * | X1_93_556427
## X1_93_556427.ENSG00000209346 chr1 556660-556760 * | X1_93_556427
## feature start_position end_position
## <character> <integer> <integer>
## X1_93_556427.ENSG00000209341 ENSG00000209341 554314 559813
## X1_93_556427.ENSG00000209344 ENSG00000209344 555569 561068
## X1_93_556427.ENSG00000209346 ENSG00000209346 555643 561142
## feature_strand insideFeature
## <character> <factor>
## X1_93_556427.ENSG00000209341 - inside
## X1_93_556427.ENSG00000209344 - inside
## X1_93_556427.ENSG00000209346 - 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 seqlengthsIn 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.
4.4 Find the overlapping and flanking features
In addition to annotating 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 | 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
## X1_20_925025.84808 chr1 926058-926158 * | X1_20_925025
## X1_11_1041174.54991 chr1 1041646-1041746 * | X1_11_1041174
## X1_14_1269014.1855 chr1 1270239-1270339 * | X1_14_1269014
## 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
## X1_20_925025.84808 84808 910579 917473
## X1_11_1041174.54991 54991 1017198 1051736
## X1_14_1269014.1855 1855 1270658 1284492
## 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
## X1_20_925025.84808 - upstream -8585
## X1_11_1041174.54991 - inside 10090
## X1_14_1269014.1855 - downstream 14253
## shortestDistance fromOverlappingOrNearest
## <integer> <character>
## X1_93_556427.100288069 143485 NearestLocation
## X1_41_559455.100288069 140371 NearestLocation
## X1_12_703729.100288069 3640 NearestLocation
## X1_20_925025.84808 8585 NearestLocation
## X1_11_1041174.54991 9990 NearestLocation
## X1_14_1269014.1855 319 Overlapping
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths4.5 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.
Please type ?addGeneIDs in a R session 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 insideFeature
## <numeric> <numeric> <character>
## X1_11_100272487.ENSG00000202254 100257218 100257309 downstream
## X1_11_108905539.ENSG00000186086 108918435 109013624 upstream
## X1_11_110106925.ENSG00000065135 110091233 110136975 inside
## X1_11_110679983.ENSG00000197106 110693108 110744824 upstream
## X1_11_110681677.ENSG00000197106 110693108 110744824 upstream
## X1_11_110756560.ENSG00000116396 110753965 110776666 inside
## distancetoFeature shortestDistance
## <numeric> <numeric>
## X1_11_100272487.ENSG00000202254 15582 15491
## X1_11_108905539.ENSG00000186086 -12410 12310
## X1_11_110106925.ENSG00000065135 16033 16033
## X1_11_110679983.ENSG00000197106 -12640 12540
## X1_11_110681677.ENSG00000197106 -10984 10884
## X1_11_110756560.ENSG00000116396 2857 2857
## fromOverlappingOrNearest symbol
## <character> <character>
## 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 seqlengthsaddGeneIDs(annotatedPeak$feature[1:6], orgAnn="org.Hs.eg.db",
IDs2Add=c("symbol"))## ensembl_gene_id symbol
## 1 ENSG00000065135 GNAI3
## 2 ENSG00000116396 KCNC4
## 3 ENSG00000197106 SLC6A17
## 4 ENSG00000186086 NBPF6
## 5 ENSG00000202254 <NA>4.6 Obtain the sequences surrounding the peaks
Here is an example to get the sequences of the peaks plus 20 bp upstream and downstream for PCR validation or motif discovery.
peaks <- GRanges(seqnames=c("NC_008253", "NC_010468"),
ranges=IRanges(start=c(100, 500),
end=c(300, 600),
names=c("peak1", "peak2")))
library(BSgenome.Ecoli.NCBI.20080805)
peaksWithSequences <- getAllPeakSequence(peaks, upstream=20,
downstream=20, genome=Ecoli)The obtained sequences can be converted to fasta format for motif
discovery by calling the function write2FASTA.
write2FASTA(peaksWithSequences,"test.fa")4.7 Create heatmap for given feature/peak ranges
You can easily visualize and compare the binding patterns of raw signals of multiple ChIP-Seq experiments using function
featureAlignedHeatmap and featureAlignedDistribution.
path <- system.file("extdata", package="ChIPpeakAnno")
files <- dir(path, "broadPeak")
data <- sapply(file.path(path, files), toGRanges, format="broadPeak")
names(data) <- gsub(".broadPeak", "", files)
ol <- findOverlapsOfPeaks(data)
#makeVennDiagram(ol)
features <- ol$peaklist[[length(ol$peaklist)]]
wid <- width(features)
feature.recentered <- feature.center <- features
start(feature.center) <- start(features) + floor(wid/2)
width(feature.center) <- 1
start(feature.recentered) <- start(feature.center) - 2000
end(feature.recentered) <- end(feature.center) + 2000
## here we also suggest importData function in bioconductor trackViewer package
## to import the coverage.
## compare rtracklayer, it will save you time when handle huge dataset.
library(rtracklayer)
files <- dir(path, "bigWig")
if(.Platform$OS.type != "windows"){
cvglists <- sapply(file.path(path, files), import,
format="BigWig",
which=feature.recentered,
as="RleList")
}else{## rtracklayer can not import bigWig files on Windows
load(file.path(path, "cvglist.rds"))
}
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))
Figure 9: Heatmap of aligned features
featureAlignedDistribution(sig, feature.center,
upstream=2000, downstream=2000,
type="l")
Figure 10: Distribution of aligned features
4.8 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"4.9 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, minGOterm=10,
multiAdjMethod="BH",
condense=FALSE)
head(over[["bp"]][, -3])## [1] go.id go.term Ontology
## [4] pvalue count.InDataset count.InGenome
## [7] totaltermInDataset totaltermInGenome BH.adjusted.p.value
## [10] EntrezID
## <0 rows> (or 0-length row.names)head(over[["cc"]][, -3])## [1] go.id go.term Ontology
## [4] pvalue count.InDataset count.InGenome
## [7] totaltermInDataset totaltermInGenome BH.adjusted.p.value
## [10] EntrezID
## <0 rows> (or 0-length row.names)head(over[["mf"]][, -3])## [1] go.id go.term Ontology
## [4] pvalue count.InDataset count.InGenome
## [7] totaltermInDataset totaltermInGenome BH.adjusted.p.value
## [10] EntrezID
## <0 rows> (or 0-length row.names)Please note that the default setting of feature_id_type is “ensembl_gene_id”. If you are using TxDb as annotation data, please set feature id type 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. For example, here is how to obtain the GO gene mapping for mouse and human.
egOrgMap("Mus musculus")## [1] "org.Mm.eg.db"egOrgMap("Homo sapiens")## [1] "org.Hs.eg.db"4.10 Find peaks with bi-directional promoters
Bidirectional promoters are the DNA regions located between the transcription start sites (TSS) of two adjacent genes that are transcribed on the opposite directions and often co-regulated by this shared promoter region9. Here is an example to find peaks near bi-directional promoters and output the percentage of the peaks near bi-directional promoters.
data(myPeakList)
data(TSS.human.NCBI36)
seqlevelsStyle(TSS.human.NCBI36) <- seqlevelsStyle(myPeakList)
annotatedBDP <- peaksNearBDP(myPeakList[1:10,],
AnnotationData=TSS.human.NCBI36,
MaxDistance=5000)
annotatedBDP$peaksWithBDP## GRangesList object of length 2:
## $1
## GRanges object with 2 ranges and 9 metadata columns:
## seqnames ranges strand | bdp_idx peak
## <Rle> <IRanges> <Rle> | <integer> <character>
## X1_93_556427 chr1 556660-556760 * | 1 X1_93_556427
## X1_93_556427 chr1 556660-556760 * | 1 X1_93_556427
## feature feature.ranges feature.strand distance
## <character> <IRanges> <Rle> <integer>
## X1_93_556427 ENSG00000209349 556240-556304 - 355
## X1_93_556427 ENSG00000209351 557933-557999 + 1172
## insideFeature distanceToSite description
## <factor> <integer> <character>
## X1_93_556427 upstream 355
## X1_93_556427 upstream 1172
##
## $8
## GRanges object with 2 ranges and 9 metadata columns:
## seqnames ranges strand | bdp_idx peak
## X1_14_1300250 chr1 1300503-1300603 * | 8 X1_14_1300250
## X1_14_1300250 chr1 1300503-1300603 * | 8 X1_14_1300250
## feature feature.ranges feature.strand distance
## X1_14_1300250 ENSG00000175756 1298974-1300443 - 59
## X1_14_1300250 ENSG00000218550 1303908-1304275 + 3304
## insideFeature distanceToSite
## X1_14_1300250 upstream 59
## X1_14_1300250 upstream 3304
## description
## X1_14_1300250 Aurora kinase A-interacting protein (AURKA-interacting protein) [Source:UniProtKB/Swiss-Prot;Acc:Q9NWT8]
## X1_14_1300250
##
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengthsc(annotatedBDP$percentPeaksWithBDP,
annotatedBDP$n.peaks,
annotatedBDP$n.peaksWithBDP)## [1] 0.2 10.0 2.04.11 Perform permutation test to determine if there is a significant overlap between two sets of peaks
Given two peak lists from two transcript factors (TFs), one common question is whether there is a significant overlap between DNA binding sites of the two TFs, which can be determined using hypergeometric test. As we have discussed in section 4.1, the hypergeometric test requires users to input an estimate of the total potential binding sites for a given TF. To circumvent this requirement, we implemented a permutation test called peakPermTest. Before performing a permutation test, users need to generate a random peak list using the distribution discovered from the input peaks for a given feature type (transcripts or exons), to make sure the binding positions relative to features, such as TSS and geneEnd, and the width of the random peaks follow the distribution of that of the input peaks.
Following are the sample codes to do the peakPermTest:
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, a peak pool representing all potential binding sites can be created with associated binding probabilities using random peak sampling using preparePool. Here is an example to build a peak pool for human genome 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 remove those HOT spots from the peak lists before performing permutation test to avoid the overestimation of the association between two input peak lists. Users can also choose to remove ENCODE blacklist for a given species. The blacklists were constructed by identifying consistently problematic regions over independent cell lines and types of experiments for each species in the ENCODE and modENCODE datasets11. Please note that some of the blacklists may need to be converted to the correct genome assembly using liftover utility. Following are the sample codes to do the permutation test using peakPermTest:
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)
}5 Citing ChIPpeakAnno
Please cite ChIPpeakAnno in your publication as follows:
##
## Please cite the paper below for the ChIPpeakAnno package.
##
## Lihua J Zhu, Claude Gazin, Nathan D Lawson, Herve Pages, Simon M
## Lin, David S Lapointe and Michael R Green, ChIPpeakAnno: a
## Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC
## Bioinformatics. 2010, 11:237
##
## Zhu LJ. Integrative analysis of ChIP-chip and ChIP-seq dataset.
## Methods Mol Biol. 2013;1067:105-24.
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.6 Session Info
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats4 parallel grid stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [2] BSgenome.Ecoli.NCBI.20080805_1.3.1000
## [3] BSgenome_1.52.0
## [4] EnsDb.Hsapiens.v75_2.99.0
## [5] ensembldb_2.8.0
## [6] AnnotationFilter_1.8.0
## [7] GO.db_3.8.2
## [8] rtracklayer_1.44.2
## [9] TxDb.Hsapiens.UCSC.hg38.knownGene_3.4.6
## [10] GenomicFeatures_1.36.4
## [11] org.Hs.eg.db_3.8.2
## [12] AnnotationDbi_1.46.0
## [13] Biobase_2.44.0
## [14] ChIPpeakAnno_3.18.2
## [15] VennDiagram_1.6.20
## [16] futile.logger_1.4.3
## [17] GenomicRanges_1.36.0
## [18] GenomeInfoDb_1.20.0
## [19] Biostrings_2.52.0
## [20] XVector_0.24.0
## [21] IRanges_2.18.1
## [22] S4Vectors_0.22.0
## [23] BiocGenerics_0.30.0
## [24] BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] ProtGenerics_1.16.0 bitops_1.0-6
## [3] matrixStats_0.54.0 mirbase.db_1.2.0
## [5] bit64_0.9-7 progress_1.2.2
## [7] httr_1.4.0 tools_3.6.0
## [9] backports_1.1.4 R6_2.4.0
## [11] DBI_1.0.0 lazyeval_0.2.2
## [13] ade4_1.7-13 prettyunits_1.0.2
## [15] bit_1.1-14 curl_4.0
## [17] compiler_3.6.0 graph_1.62.0
## [19] formatR_1.7 DelayedArray_0.10.0
## [21] bookdown_0.12 RBGL_1.60.0
## [23] stringr_1.4.0 digest_0.6.20
## [25] Rsamtools_2.0.0 rmarkdown_1.14
## [27] pkgconfig_2.0.2 htmltools_0.3.6
## [29] limma_3.40.6 highr_0.8
## [31] regioneR_1.16.2 rlang_0.4.0
## [33] RSQLite_2.1.2 BiocParallel_1.18.0
## [35] RCurl_1.95-4.12 magrittr_1.5
## [37] GenomeInfoDbData_1.2.1 Matrix_1.2-17
## [39] Rcpp_1.0.2 stringi_1.4.3
## [41] yaml_2.2.0 MASS_7.3-51.4
## [43] SummarizedExperiment_1.14.0 zlibbioc_1.30.0
## [45] blob_1.2.0 crayon_1.3.4
## [47] lattice_0.20-38 splines_3.6.0
## [49] multtest_2.40.0 hms_0.5.0
## [51] zeallot_0.1.0 knitr_1.23
## [53] pillar_1.4.2 seqinr_3.4-5
## [55] biomaRt_2.40.3 futile.options_1.0.1
## [57] XML_3.98-1.20 evaluate_0.14
## [59] lambda.r_1.2.3 BiocManager_1.30.4
## [61] idr_1.2 vctrs_0.2.0
## [63] assertthat_0.2.1 xfun_0.8
## [65] FDb.UCSC.tRNAs_1.0.1 survival_2.44-1.1
## [67] tibble_2.1.3 GenomicAlignments_1.20.1
## [69] memoise_1.1.0References
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