Contents
Abstract
ChIPseeker is an R package for annotating ChIP-seq data analysis. It supports annotating ChIP peaks and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions. Comparison of ChIP peak profiles and annotation are also supported. Moreover, it supports evaluating significant overlap among ChIP-seq datasets. Currently, ChIPseeker contains 17,000 bed file information from GEO database. These datasets can be downloaded and compare with user’s own data to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation.
Introduction
Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) has become standard technologies for genome wide identification of DNA-binding protein target sites. After read mappings and peak callings, the peak should be annotated to answer the biological questions. Annotation also create the possibility of integrate expression profile data to predict gene expression regulation. ChIPseeker1 was developed for annotating nearest genes and genomic features to peaks.
ChIP peak data set comparison is also very important. We can use it as an index to estimate how well biological replications are. Even more important is applying to infer cooperative regulation. If two ChIP seq data, obtained by two different binding proteins, overlap significantly, these two proteins may form a complex or have interaction in regulation chromosome remodelling or gene expression. ChIPseeker1 support statistical testing of significant overlap among ChIP seq data sets, and incorporate open access database GEO for users to compare their own dataset to those deposited in database. Protein interaction hypothesis can be generated by mining data deposited in database. Converting genome coordinations from one genome version to another is also supported, making this comparison available for different genome version and different species.
Several visualization functions are implemented to visualize the coverage of the ChIP seq data, peak annotation, average profile and heatmap of peaks binding to TSS region.
Functional enrichment analysis of the peaks can be performed by my Bioconductor packages DOSE2, ReactomePA3, clusterProfiler4.
## loading packages
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
library(clusterProfiler)
ChIP profiling
The datasets CBX6 and CBX7 in this vignettes were downloaded from GEO (GSE40740)5 while ARmo_0M, ARmo_1nM and ARmo_100nM were downloaded from GEO (GSE48308)6 . ChIPseeker provides readPeakFile to load the peak and store in GRanges object.
files <- getSampleFiles()
print(files)
## $ARmo_0M
## [1] "/tmp/RtmpUWv1jJ/Rinst7c751b35c329/ChIPseeker/extdata/GEO_sample_data/GSM1174480_ARmo_0M_peaks.bed.gz"
##
## $ARmo_1nM
## [1] "/tmp/RtmpUWv1jJ/Rinst7c751b35c329/ChIPseeker/extdata/GEO_sample_data/GSM1174481_ARmo_1nM_peaks.bed.gz"
##
## $ARmo_100nM
## [1] "/tmp/RtmpUWv1jJ/Rinst7c751b35c329/ChIPseeker/extdata/GEO_sample_data/GSM1174482_ARmo_100nM_peaks.bed.gz"
##
## $CBX6_BF
## [1] "/tmp/RtmpUWv1jJ/Rinst7c751b35c329/ChIPseeker/extdata/GEO_sample_data/GSM1295076_CBX6_BF_ChipSeq_mergedReps_peaks.bed.gz"
##
## $CBX7_BF
## [1] "/tmp/RtmpUWv1jJ/Rinst7c751b35c329/ChIPseeker/extdata/GEO_sample_data/GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz"
peak <- readPeakFile(files[[4]])
peak
## GRanges object with 1331 ranges and 2 metadata columns:
## seqnames ranges strand | V4 V5
## <Rle> <IRanges> <Rle> | <factor> <numeric>
## [1] chr1 [ 815093, 817883] * | MACS_peak_1 295.76
## [2] chr1 [1243288, 1244338] * | MACS_peak_2 63.19
## [3] chr1 [2979977, 2981228] * | MACS_peak_3 100.16
## [4] chr1 [3566182, 3567876] * | MACS_peak_4 558.89
## [5] chr1 [3816546, 3818111] * | MACS_peak_5 57.57
## ... ... ... ... . ... ...
## [1327] chrX [135244783, 135245821] * | MACS_peak_1327 55.54
## [1328] chrX [139171964, 139173506] * | MACS_peak_1328 270.19
## [1329] chrX [139583954, 139586126] * | MACS_peak_1329 918.73
## [1330] chrX [139592002, 139593238] * | MACS_peak_1330 210.88
## [1331] chrY [ 13845134, 13845777] * | MACS_peak_1331 58.39
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths
ChIP peaks coverage plot
After peak calling, we would like to know the peak locations over the whole genome, covplot function calculates the coverage of peak regions over chromosomes and generate a figure to visualize. GRangesList is also supported and can be used to compare coverage of multiple bed files.
covplot(peak, weightCol="V5")
covplot(peak, weightCol="V5", chrs=c("chr17", "chr18"), xlim=c(4.5e7, 5e7))
Profile of ChIP peaks binding to TSS regions
First of all, for calculate the profile of ChIP peaks binding to TSS regions, we should prepare the TSS regions, which are defined as the flanking sequence of the TSS sites. Then align the peaks that are mapping to these regions, and generate the tagMatrix.
## promoter <- getPromoters(TxDb=txdb, upstream=3000, downstream=3000)
## tagMatrix <- getTagMatrix(peak, windows=promoter)
##
## to speed up the compilation of this vignettes, we use a precalculated tagMatrix
data("tagMatrixList")
tagMatrix <- tagMatrixList[[4]]
In the above code, you should notice that tagMatrix is not restricted to TSS regions. The regions can be other types that defined by the user.
Heatmap of ChIP binding to TSS regions
tagHeatmap(tagMatrix, xlim=c(-3000, 3000), color="red")
ChIPseeker provide a one step function to generate this figure from bed file. The following function will generate the same figure as above.
peakHeatmap(files[[4]], TxDb=txdb, upstream=3000, downstream=3000, color="red")
Average Profile of ChIP peaks binding to TSS region
plotAvgProf(tagMatrix, xlim=c(-3000, 3000),
xlab="Genomic Region (5'->3')", ylab = "Read Count Frequency")
The function plotAvgProf2 provide a one step from bed file to average profile plot. The following command will generate the same figure as shown above.
plotAvgProf2(files[[4]], TxDb=txdb, upstream=3000, downstream=3000,
xlab="Genomic Region (5'->3')", ylab = "Read Count Frequency")
Confidence interval estimated by bootstrap method is also supported for characterizing ChIP binding profiles.
plotAvgProf(tagMatrix, xlim=c(-3000, 3000), conf = 0.95, resample = 1000)
Profile of ChIP peaks binding to start site of Exon/Intron
Referring to the issue #16, we developed getBioRegion function to support centering all peaks to the start region of Exon/Intron. Users can also create heatmap or average profile of ChIP peaks binding to these regions.
Peak Annotation
peakAnno <- annotatePeak(files[[4]], tssRegion=c(-3000, 3000),
TxDb=txdb, annoDb="org.Hs.eg.db")
## >> loading peak file... 2016-12-16 07:32:40 PM
## >> preparing features information... 2016-12-16 07:32:40 PM
## >> identifying nearest features... 2016-12-16 07:32:41 PM
## >> calculating distance from peak to TSS... 2016-12-16 07:32:41 PM
## >> assigning genomic annotation... 2016-12-16 07:32:41 PM
## >> adding gene annotation... 2016-12-16 07:32:58 PM
## >> assigning chromosome lengths 2016-12-16 07:32:59 PM
## >> done... 2016-12-16 07:32:59 PM
Peak Annotation is performed by annotatePeak. User can define TSS (transcription start site) region, by default TSS is defined from -3kb to +3kb. The output of annotatePeak is csAnno instance. ChIPseeker provides as.GRanges to convert csAnno to GRanges instance, and as.data.frame to convert csAnno to data.frame which can be exported to file by write.table.
TxDb object contained transcript-related features of a particular genome. Bioconductor provides several package that containing TxDb object of model organisms with multiple commonly used genome version, for instance TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene for human genome hg38 and hg19, TxDb.Mmusculus.UCSC.mm10.knownGene and TxDb.Mmusculus.UCSC.mm9.knownGene for mouse genome mm10 and mm9, etc. User can also prepare their own TxDb object by retrieving information from UCSC Genome Bioinformatics and BioMart data resources by R function makeTxDbFromBiomart and makeTxDbFromUCSC. TxDb object should be passed for peak annotation.
All the peak information contained in peakfile will be retained in the output of annotatePeak. The position and strand information of nearest genes are reported. The distance from peak to the TSS of its nearest gene is also reported. The genomic region of the peak is reported in annotation column. Since some annotation may overlap, ChIPseeker adopted the following priority in genomic annotation.
- Promoter
- 5’ UTR
- 3’ UTR
- Exon
- Intron
- Downstream
- Intergenic
Downstream is defined as the downstream of gene end.
ChIPseeker also provides parameter genomicAnnotationPriority for user to prioritize this hierachy.
annotatePeak report detail information when the annotation is Exon or Intron, for instance “Exon (uc002sbe.3/9736, exon 69 of 80)”, means that the peak is overlap with an Exon of transcript uc002sbe.3, and the corresponding Entrez gene ID is 9736 (Transcripts that belong to the same gene ID may differ in splice events), and this overlaped exon is the 69th exon of the 80 exons that this transcript uc002sbe.3 prossess.
Parameter annoDb is optional, if provided, extra columns including SYMBOL, GENENAME, ENSEMBL/ENTREZID will be added. The geneId column in annotation output will be consistent with the geneID in TxDb. If it is ENTREZID, ENSEMBL will be added if annoDb is provided, while if it is ENSEMBL ID, ENTREZID will be added.
Visualize Genomic Annotation
To annotate the location of a given peak in terms of genomic features, annotatePeak assigns peaks to genomic annotation in “annotation” column of the output, which includes whether a peak is in the TSS, Exon, 5’ UTR, 3’ UTR, Intronic or Intergenic. Many researchers are very interesting in these annotations. TSS region can be defined by user and annotatePeak output in details of which exon/intron of which genes as illustrated in previous section.
Pie and Bar plot are supported to visualize the genomic annotation.
plotAnnoPie(peakAnno)
plotAnnoBar(peakAnno)
Since some annotation overlap, user may interested to view the full annotation with their overlap, which can be partially resolved by vennpie function.
vennpie(peakAnno)
We extend UpSetR to view full annotation overlap. User can user upsetplot function.
upsetplot(peakAnno)
We can combine vennpie with upsetplot by setting vennpie = TRUE.
upsetplot(peakAnno, vennpie=TRUE)
Visualize distribution of TF-binding loci relative to TSS
The distance from the peak (binding site) to the TSS of the nearest gene is calculated by annotatePeak and reported in the output. We provide plotDistToTSS to calculate the percentage of binding sites upstream and downstream from the TSS of the nearest genes, and visualize the distribution.
plotDistToTSS(peakAnno,
title="Distribution of transcription factor-binding loci\nrelative to TSS")
Functional enrichment analysis
Once we have obtained the annotated nearest genes, we can perform functional enrichment analysis to identify predominant biological themes among these genes by incorporating biological knowledge provided by biological ontologies. For instance, Gene Ontology (GO)7 annotates genes to biological processes, molecular functions, and cellular components in a directed acyclic graph structure, Kyoto Encyclopedia of Genes and Genomes (KEGG)8 annotates genes to pathways, Disease Ontology (DO)9 annotates genes with human disease association, and Reactome10 annotates gene to pathways and reactions.
ChIPseeker also provides a function, seq2gene, for linking genomc regions to genes in a many-to-many mapping. It consider host gene (exon/intron), promoter region and flanking gene from intergenic region that may under control via cis-regulation. This function is designed to link both coding and non-coding genomic regions to coding genes and facilitate functional analysis.
Enrichment analysis is a widely used approach to identify biological themes. I have developed several Bioconductor packages for investigating whether the number of selected genes associated with a particular biological term is larger than expected, including DOSE2 for Disease Ontology, ReactomePA for reactome pathway, clusterProfiler4 for Gene Ontology and KEGG enrichment analysis.
library(ReactomePA)
pathway1 <- enrichPathway(as.data.frame(peakAnno)$geneId)
head(pathway1, 2)
## ID Description GeneRatio BgRatio pvalue
## 1266738 1266738 Developmental Biology 44/291 438/6748 7.850446e-08
## 195721 195721 Signaling by Wnt 28/291 245/6748 1.840754e-06
## p.adjust qvalue
## 1266738 4.985033e-05 4.985033e-05
## 195721 5.844395e-04 5.844395e-04
## geneID
## 1266738 27022/1944/1942/2494/55740/4838/219699/3983/1793/6712/7225/64221/1280/3651/1282/8874/3175/4756/56963/9423/9611/6928/9969/6929/390874/2034/785/1290/3642/4821/57167/1291/6092/2042/5361/6657/2049/2044/4825/57556/1879/1808/63978/9282
## 195721 79971/607/5692/23401/55553/144165/11197/5717/6662/57690/5714/9736/7474/6657/51176/6423/5145/6885/23072/51384/83595/7976/64321/340419/23513/7088/4920/6658
## Count
## 1266738 44
## 195721 28
gene <- seq2gene(peak, tssRegion = c(-1000, 1000), flankDistance = 3000, TxDb=txdb)
pathway2 <- enrichPathway(gene)
head(pathway2, 2)
## ID Description GeneRatio BgRatio pvalue
## 1266738 1266738 Developmental Biology 33/235 438/6748 1.792286e-05
## 195721 195721 Signaling by Wnt 22/235 245/6748 3.826936e-05
## p.adjust qvalue
## 1266738 0.01073579 0.01071598
## 195721 0.01093603 0.01091585
## geneID
## 1266738 27022/2494/55740/4838/219699/3983/7225/1280/3651/56963/9423/9611/2034/785/4821/1291/6092/2042/4825/57556/63978/1944/1282/8874/6928/57167/2044/1808/1942/4756/9969/9282/3642
## 195721 5692/23401/55553/144165/11197/5717/6662/5714/9736/6423/23072/51384/340419/4920/6658/607/5145/7976/51176/64321/23513/7088
## Count
## 1266738 33
## 195721 22
dotplot(pathway2)
More information can be found in the vignettes of Bioconductor packages DOSE2, ReactomePA, clusterProfiler4, which also provide several methods to visualize enrichment results. The clusterProfiler4 is designed for comparing and visualizing functional profiles among gene clusters, and can directly applied to compare biological themes at GO, DO, KEGG, Reactome perspective.
ChIP peak data set comparison
Profile of several ChIP peak data binding to TSS region
Function plotAvgProf and tagHeatmap can accept a list of tagMatrix and visualize profile or heatmap among several ChIP experiments, while plotAvgProf2 and peakHeatmap can accept a list of bed files and perform the same task in one step.
Average profiles
## promoter <- getPromoters(TxDb=txdb, upstream=3000, downstream=3000)
## tagMatrixList <- lapply(files, getTagMatrix, windows=promoter)
##
## to speed up the compilation of this vigenette, we load a precaculated tagMatrixList
data("tagMatrixList")
plotAvgProf(tagMatrixList, xlim=c(-3000, 3000))
plotAvgProf(tagMatrixList, xlim=c(-3000, 3000), conf=0.95,resample=500, facet="row")
Peak heatmaps
tagHeatmap(tagMatrixList, xlim=c(-3000, 3000), color=NULL)
ChIP peak annotation comparision
The plotAnnoBar and plotDistToTSS can also accept input of a named list of annotated peaks (output of annotatePeak).
peakAnnoList <- lapply(files, annotatePeak, TxDb=txdb,
tssRegion=c(-3000, 3000), verbose=FALSE)
We can use plotAnnoBar to comparing their genomic annotation.
plotAnnoBar(peakAnnoList)
R function plotDistToTSS can use to comparing distance to TSS profiles among ChIPseq data.
plotDistToTSS(peakAnnoList)
Functional profiles comparison
As shown in section 4, the annotated genes can analyzed by clusterProfiler4, DOSE2 and ReactomePA for Gene Ontology, KEGG, Disease Ontology and Reactome Pathway enrichment analysis.
The clusterProfiler4 package provides compareCluster function for comparing biological themes among gene clusters, and can be easily adopted to compare different ChIP peak experiments.
genes = lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
names(genes) = sub("_", "\n", names(genes))
compKEGG <- compareCluster(geneCluster = genes,
fun = "enrichKEGG",
pvalueCutoff = 0.05,
pAdjustMethod = "BH")
plot(compKEGG, showCategory = 15, title = "KEGG Pathway Enrichment Analysis")
Overlap of peaks and annotated genes
User may want to compare the overlap peaks of replicate experiments or from different experiments. ChIPseeker provides peak2GRanges that can read peak file and stored in GRanges object. Several files can be read simultaneously using lapply, and then passed to vennplot to calculate their overlap and draw venn plot.
vennplot accept a list of object, can be a list of GRanges or a list of vector. Here, I will demonstrate using vennplot to visualize the overlap of the nearest genes stored in peakAnnoList.
genes= lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
vennplot(genes)
Statistical testing of ChIP seq overlap
Overlap is very important, if two ChIP experiment by two different proteins overlap in a large fraction of their peaks, they may cooperative in regulation. Calculating the overlap is only touch the surface. ChIPseeker implemented statistical methods to measure the significance of the overlap.
Shuffle genome coordination
p <- GRanges(seqnames=c("chr1", "chr3"),
ranges=IRanges(start=c(1, 100), end=c(50, 130)))
shuffle(p, TxDb=txdb)
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 [248586646, 248586696] *
## [2] chr3 [ 74096300, 74096331] *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
We implement the shuffle function to randomly permute the genomic locations of ChIP peaks defined in a genome which stored in TxDb object.
Peak overlap enrichment analysis
With the ease of this shuffle method, we can generate thousands of random ChIP data and calculate the background null distribution of the overlap among ChIP data sets.
enrichPeakOverlap(queryPeak = files[[5]],
targetPeak = unlist(files[1:4]),
TxDb = txdb,
pAdjustMethod = "BH",
nShuffle = 50,
chainFile = NULL,
verbose = FALSE)
## qSample
## ARmo_0M GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
## ARmo_1nM GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
## ARmo_100nM GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
## CBX6_BF GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
## tSample qLen tLen
## ARmo_0M GSM1174480_ARmo_0M_peaks.bed.gz 1663 812
## ARmo_1nM GSM1174481_ARmo_1nM_peaks.bed.gz 1663 2296
## ARmo_100nM GSM1174482_ARmo_100nM_peaks.bed.gz 1663 1359
## CBX6_BF GSM1295076_CBX6_BF_ChipSeq_mergedReps_peaks.bed.gz 1663 1331
## N_OL pvalue p.adjust
## ARmo_0M 0 0.86274510 0.86274510
## ARmo_1nM 8 0.11764706 0.23529412
## ARmo_100nM 3 0.29411765 0.39215686
## CBX6_BF 968 0.01960784 0.07843137
Parameter queryPeak is the query ChIP data, while targetPeak is bed file name or a vector of bed file names from comparison; nShuffle is the number to shuffle the peaks in targetPeak. To speed up the compilation of this vignettes, we only set nShuffle to 50 as an example for only demonstration. User should set the number to 1000 or above for more robust result. Parameter chainFile are chain file name for mapping the targetPeak to the genome version consistent with queryPeak when their genome version are different. This creat the possibility of comparison among different genome version and cross species.
In the output, qSample is the name of queryPeak and qLen is the the number of peaks in queryPeak. N_OL is the number of overlap between queryPeak and targetPeak.
Data Mining with ChIP seq data deposited in GEO
There are many ChIP seq data sets that have been published and deposited in GEO database. We can compare our own dataset to those deposited in GEO to search for significant overlap data. Significant overlap of ChIP seq data by different binding proteins may be used to infer cooperative regulation and thus can be used to generate hypotheses.
We collect about 17,000 bed files deposited in GEO, user can use getGEOspecies to get a summary based on speices.
GEO data collection
getGEOspecies()
## species Freq
## 1 Aedes aegypti 11
## 2 Anabaena 6
## 3 Anolis carolinensis 5
## 4 Anopheles gambiae 2
## 5 Apis mellifera 5
## 6 Apis mellifera scutellata 1
## 7 Arabidopsis lyrata 4
## 8 Arabidopsis thaliana 209
## 9 Atelerix albiventris 2
## 10 Bos taurus 28
## 11 Branchiostoma lanceolatum 62
## 12 Brassica rapa 8
## 13 Caenorhabditis elegans 164
## 14 Candida albicans 25
## 15 Candida dubliniensis 20
## 16 Canis lupus familiaris 7
## 17 Chlamydomonas reinhardtii 51
## 18 Chlorocebus aethiops 2
## 19 Cleome hassleriana 1
## 20 Columba livia 6
## 21 Crassostrea gigas 1
## 22 Cryptococcus neoformans 51
## 23 Cyprinus carpio 40
## 24 Danio rerio 164
## 25 Drosophila busckii 1
## 26 Drosophila melanogaster 734
## 27 Drosophila melanogaster;\tSindbis virus 3
## 28 Drosophila miranda 2
## 29 Drosophila pseudoobscura 7
## 30 Drosophila simulans 12
## 31 Drosophila virilis 26
## 32 Drosophila willistoni 1
## 33 Drosophila yakuba 8
## 34 Equus caballus 1
## 35 Escherichia coli 13
## 36 Escherichia coli BW25113 4
## 37 Escherichia coli K-12 2
## 38 Escherichia coli str. K-12 substr. MG1655 8
## 39 Gallus gallus 55
## 40 Geobacter sulfurreducens PCA 3
## 41 Gorilla gorilla 2
## 42 Histophilus somni 1
## 43 Homo sapiens 10083
## 44 Homo sapiens;\tHuman herpesvirus 8 6
## 45 Human herpesvirus 6B 2
## 46 Human herpesvirus 8 6
## 47 Larimichthys crocea 7
## 48 Legionella pneumophila 5
## 49 Leishmania amazonensis 4
## 50 Leishmania major 2
## 51 Leishmania major strain Friedlin 4
## 52 Leishmania tarentolae 15
## 53 Macaca mulatta 116
## 54 Monodelphis domestica 8
## 55 Moraxella catarrhalis O35E 6
## 56 Mus musculus 7596
## 57 Mus musculus x Mus spretus 1
## 58 Mycobacterium tuberculosis 2
## 59 Myotis brandtii 15
## 60 Naumovozyma castellii 1
## 61 Nematostella vectensis 23
## 62 Oreochromis niloticus 1
## 63 Ornithorhynchus anatinus 5
## 64 Oryza sativa 23
## 65 Oryzias latipes 2
## 66 Pan troglodytes 93
## 67 Papio anubis 1
## 68 Plasmodium falciparum 105
## 69 Plasmodium falciparum 3D7 29
## 70 Pseudomonas putida KT2440 2
## 71 Pseudozyma aphidis 11
## 72 Pyrococcus furiosus 4
## 73 Rattus norvegicus 57
## 74 Rhodopseudomonas palustris 6
## 75 Rhodopseudomonas palustris CGA009 3
## 76 Saccharomyces cerevisiae 586
## 77 Saccharomyces cerevisiae x Saccharomyces paradoxus 16
## 78 Saccharomyces cerevisiae;\tMus musculus 12
## 79 Saccharomyces kudriavzevii 1
## 80 Saccharomyces paradoxus 8
## 81 Saccharomyces uvarum 1
## 82 Schizosaccharomyces japonicus 2
## 83 Schizosaccharomyces pombe 130
## 84 Schmidtea mediterranea 7
## 85 Sorghum bicolor 2
## 86 Streptomyces coelicolor A3(2) 6
## 87 Sus scrofa 23
## 88 Taeniopygia guttata 1
## 89 Tupaia chinensis 7
## 90 Vibrio cholerae 8
## 91 Xenopus (Silurana) tropicalis 62
## 92 Xenopus laevis 10
## 93 Zea mays 63
The summary can also based on genome version as illustrated below:
getGEOgenomeVersion()
## organism genomeVersion Freq
## 1 Anolis carolinensis anoCar2 5
## 2 Bos taurus bosTau4 2
## 3 Bos taurus bosTau6 24
## 4 Bos taurus bosTau7 2
## 5 Caenorhabditis elegans ce10 4
## 6 Caenorhabditis elegans ce6 64
## 7 Danio rerio danRer6 6
## 8 Danio rerio danRer7 61
## 9 Drosophila melanogaster dm3 502
## 10 Gallus gallus galGal3 32
## 11 Gallus gallus galGal4 15
## 12 Homo sapiens hg18 2512
## 13 Homo sapiens hg19 6876
## 14 Homo sapiens hg38 43
## 15 Mus musculus mm10 214
## 16 Mus musculus mm8 507
## 17 Mus musculus mm9 6289
## 18 Monodelphis domestica monDom5 8
## 19 Pan troglodytes panTro3 48
## 20 Pan troglodytes panTro4 42
## 21 Macaca mulatta rheMac2 81
## 22 Macaca mulatta rheMac3 31
## 23 Rattus norvegicus rn5 3
## 24 Saccharomyces cerevisiae sacCer2 141
## 25 Saccharomyces cerevisiae sacCer3 227
## 26 Sus scrofa susScr2 17
## 27 Xenopus (Silurana) tropicalis xenTro3 3
User can access the detail information by getGEOInfo, for each genome version.
hg19 <- getGEOInfo(genome="hg19", simplify=TRUE)
head(hg19)
## series_id gsm organism
## 111 GSE16256 GSM521889 Homo sapiens
## 112 GSE16256 GSM521887 Homo sapiens
## 113 GSE16256 GSM521883 Homo sapiens
## 114 GSE16256 GSM1010966 Homo sapiens
## 115 GSE16256 GSM896166 Homo sapiens
## 116 GSE16256 GSM910577 Homo sapiens
## title
## 111 Reference Epigenome: ChIP-Seq Analysis of H3K27me3 in IMR90 Cells; renlab.H3K27me3.IMR90-02.01
## 112 Reference Epigenome: ChIP-Seq Analysis of H3K27ac in IMR90 Cells; renlab.H3K27ac.IMR90-03.01
## 113 Reference Epigenome: ChIP-Seq Analysis of H3K14ac in IMR90 Cells; renlab.H3K14ac.IMR90-02.01
## 114 polyA RNA sequencing of STL003 Pancreas Cultured Cells; polyA-RNA-seq_STL003PA_r1a
## 115 Reference Epigenome: ChIP-Seq Analysis of H4K8ac in hESC H1 Cells; renlab.H4K8ac.hESC.H1.01.01
## 116 Reference Epigenome: ChIP-Seq Analysis of H3K4me1 in Human Spleen Tissue; renlab.H3K4me1.STL003SX.01.01
## supplementary_file
## 111 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM521nnn/GSM521889/suppl/GSM521889_UCSD.IMR90.H3K27me3.SK05.bed.gz
## 112 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM521nnn/GSM521887/suppl/GSM521887_UCSD.IMR90.H3K27ac.YL58.bed.gz
## 113 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM521nnn/GSM521883/suppl/GSM521883_UCSD.IMR90.H3K14ac.SK17.bed.gz
## 114 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1010nnn/GSM1010966/suppl/GSM1010966_UCSD.Pancreas.mRNA-Seq.STL003.bed.gz
## 115 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM896nnn/GSM896166/suppl/GSM896166_UCSD.H1.H4K8ac.AY17.bed.gz
## 116 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM910nnn/GSM910577/suppl/GSM910577_UCSD.Spleen.H3K4me1.STL003.bed.gz
## genomeVersion pubmed_id
## 111 hg19 19829295
## 112 hg19 19829295
## 113 hg19 19829295
## 114 hg19 19829295
## 115 hg19 19829295
## 116 hg19 19829295
If simplify is set to FALSE, extra information including source_name, extract_protocol, description, data_processing and submission_date will be incorporated.
Download GEO ChIP data sets
ChIPseeker provide function downloadGEObedFiles to download all the bed files of a particular genome.
downloadGEObedFiles(genome="hg19", destDir="hg19")
Or a vector of GSM accession number by downloadGSMbedFiles.
gsm <- hg19$gsm[sample(nrow(hg19), 10)]
downloadGSMbedFiles(gsm, destDir="hg19")
Overlap significant testing
After download the bed files from GEO, we can pass them to enrichPeakOverlap for testing the significant of overlap. Parameter targetPeak can be the folder, e.g. hg19, that containing bed files. enrichPeakOverlap will parse the folder and compare all the bed files. It is possible to test the overlap with bed files that are mapping to different genome or different genome versions, enrichPeakOverlap provide a parameter chainFile that can pass a chain file and liftOver the targetPeak to the genome version consistent with queryPeak. Signifcant overlap can be use to generate hypothesis of cooperative regulation.By mining the data deposited in GEO, we can identify some putative complex or interacted regulators in gene expression regulation or chromsome remodelling for further validation.
Homepage
Please visit ChIPseeker homepage for more information.
References
1. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: An r/bioconductor package for chip peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
2. Yu, G., Wang, L.-G., Yan, G.-R. & He, Q.-Y. DOSE: An r/bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608–609 (2015).
3. Yu, G. & He, Q.-Y. ReactomePA: An r/bioconductor package for reactome pathway analysis and visualization. Mol. BioSyst. 12, 477–479 (2016).
4. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an r package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 16, 284–287 (2012).
5. Pemberton, H. et al. Genome-wide co-localization of polycomb orthologs and their effects on gene expression in human fibroblasts. Genome Biol. 15, R23 (2014).
6. Urbanucci, A. et al. Overexpression of androgen receptor enhances the binding of the receptor to the chromatin in prostate cancer. Oncogene 31, 2153–2163 (2012).
7. Ashburner, M. et al. Gene ontology: Tool for the unification of biology. Nat Genet 25, 25–29 (2000).
8. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucl. Acids Res. 32, D277–D280 (2004).
9. Schriml, L. M. et al. Disease ontology: A backbone for disease semantic integration. Nucleic Acids Research 40, D940–D946 (2011).
10. Croft, D. et al. The reactome pathway knowledgebase. Nucleic Acids Research 42, D472–D477 (2013).
