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.
Citation
If you use ChIPseeker(Yu, Wang, and He 2015) in published research, please cite:
- Q Wang#, M Li#, T Wu, L Zhan, L Li, M Chen, W Xie, Z Xie, E Hu, S Xu, G Yu*. Exploring epigenomic datasets by ChIPseeker. Current Protocols, 2022, 2(10): e585.
- G Yu*, LG Wang, QY He*. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparision and visualization. Bioinformatics. 2015, 31(14):2382-2383.
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 integrating expression profile data to predict gene expression regulation. ChIPseeker(Yu, Wang, and He 2015) 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. ChIPseeker(Yu, Wang, and He 2015) 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 DOSE(Yu et al. 2015), ReactomePA(Yu and He 2016), clusterProfiler(Yu et al. 2012).
ChIP profiling
The datasets CBX6 and CBX7 in this vignettes were downloaded from GEO (GSE40740)(Pemberton et al. 2014) while ARmo_0M, ARmo_1nM and ARmo_100nM were downloaded from GEO (GSE48308)(Urbanucci et al. 2012) . ChIPseeker provides readPeakFile to load the peak and store in GRanges object.
## $ARmo_0M
## [1] "/tmp/RtmpbwGRjA/Rinst29a3831e130dc6/ChIPseeker/extdata/GEO_sample_data/GSM1174480_ARmo_0M_peaks.bed.gz"
##
## $ARmo_1nM
## [1] "/tmp/RtmpbwGRjA/Rinst29a3831e130dc6/ChIPseeker/extdata/GEO_sample_data/GSM1174481_ARmo_1nM_peaks.bed.gz"
##
## $ARmo_100nM
## [1] "/tmp/RtmpbwGRjA/Rinst29a3831e130dc6/ChIPseeker/extdata/GEO_sample_data/GSM1174482_ARmo_100nM_peaks.bed.gz"
##
## $CBX6_BF
## [1] "/tmp/RtmpbwGRjA/Rinst29a3831e130dc6/ChIPseeker/extdata/GEO_sample_data/GSM1295076_CBX6_BF_ChipSeq_mergedReps_peaks.bed.gz"
##
## $CBX7_BF
## [1] "/tmp/RtmpbwGRjA/Rinst29a3831e130dc6/ChIPseeker/extdata/GEO_sample_data/GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz"## GRanges object with 1331 ranges and 2 metadata columns:
## seqnames ranges strand | V4 V5
## <Rle> <IRanges> <Rle> | <character> <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 seqlengthsChIP 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.
Profile of ChIP peaks binding to TSS regions
First of all, for calculating 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. ChIPseeker expanded the scope of region. Users can input the type and by parameters to get the regions they want.
Heatmap of ChIP binding to TSS regions
Heatmap of ChIP peaks binding to TSS regions
ChIPseeker provide a one step function to generate this figure from bed file. The following function will generate the same figure as above.
Users can use nbin parameter to speed up.
Users can also use ggplot method to change the details of the figures.
peakHeatmap(files[[4]],TxDb = txdb,nbin = 800,upstream=3000, downstream=3000) +
scale_fill_distiller(palette = "RdYlGn")Users can also profile genebody regions with peakHeatmap().
peakHeatmap(peak = files[[4]],
TxDb = txdb,
upstream = rel(0.2),
downstream = rel(0.2),
by = "gene",
type = "body",
nbin = 800)
Heatmap of genebody regions
Sometimes there will be a need to explore the comparison of the peak heatmap over two regions, for example, the following picture is the peak over two gene sets. One possible scenery of using this method is to compare the peak heatmap over up-regulating genes and down-regulating genes. Here txdb1 and txdb2 is the simulated gene sets obtain from TxDb.Hsapiens.UCSC.hg19.knownGene. Using peakHeatmap_multiple_Sets(), accepting list object containing different regions information. The length of each part is correlated to the amount of regions.
txdb1 <- transcripts(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb2 <- unlist(fiveUTRsByTranscript(TxDb.Hsapiens.UCSC.hg19.knownGene))[1:10000,]
region_list <- list(geneX = txdb1, geneY = txdb2)
peakHeatmap_multiple_Sets(peak = files[[4]],
upstream = 1000,downstream = 1000,
by = c("geneX","geneY"),
type = "start_site",
TxDb = region_list,nbin = 800)
Heatmap of over two regions
We also meet the need of ploting heatmap and peak profiling together.
peak_Profile_Heatmap(peak = files[[4]],
upstream = 1000,
downstream = 1000,
by = "gene",
type = "start_site",
TxDb = txdb,
nbin = 800)
Combination of heatmap and peak profiling
Exploring several regions with heatmap and peak profiling is also supported.
txdb1 <- transcripts(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb2 <- unlist(fiveUTRsByTranscript(TxDb.Hsapiens.UCSC.hg19.knownGene))[1:10000,]
region_list <- list(geneX = txdb1, geneY = txdb2)
peak_Profile_Heatmap(peak = files[[4]],
upstream = 1000,
downstream = 1000,
by = c("geneX","geneY"),
type = "start_site",
TxDb = region_list,nbin = 800)
Combination of heatmap and peak profiling over several regions
Average Profile of ChIP peaks binding to TSS region
plotAvgProf(tagMatrix, xlim=c(-3000, 3000),
xlab="Genomic Region (5'->3')", ylab = "Read Count Frequency")## >> plotting figure... 2024-04-30 11:05:50 PM
Average Profile of ChIP peaks binding to TSS region
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.
Profile of ChIP peaks binding to different regions
Referring to the issue #16 , we developed and improved several functions support start site region, end site region and body region of Gene/Transcript/Exon/Intron/3UTR/5UTR. getBioRegion can prepare the different regions for ChIP peaks to bind. getTagMatrix can accept type, by, upstream and downstream parameters to get tagmatrix according to different needs. plotPeakProf and plotPeakProf2 supports the plotting of profiles of peaks binding to different regions.Users can also create heatmap or average profile of ChIP peaks binding to these regions.
In order to plot body regions, a new methond binning,was introduced to getTagMatrix. The idea of binning was derived from deeptools(Ramı́rez et al. 2016). binning scaled the regions having different lengths to the equal length by deviding the regions into the same amounts of boxes. Because the amount of boxes is equal, the regions can be thought of scaling to equal length.binning method can speed up the getTagMatrix by changing the precision from bp to box(several bps).
There are three ways to plot these regions. First, users can use getBioRegion to prepare the regions. Then align the peaks that are mapping to these regions, and generate the tagMatrix by getTagMatrix. At Last, plot the figures by plotPeakProf. Second, users can input type and by parameters to getTagMatrix to get the tagMatrix and plot the figures. Third, users can use plotPeakProf2 to do everything in one step.
Binning method for profile of ChIP peaks binding to TSS regions
Here uses the method of inputting type and by parameters. type = "start_site" means the start site region. by = "gene" means that it is the start site region of gene(TSS regions). If users want to use binning method, the nbin method must be set.
Profile of ChIP peaks binding to body regions
We improved and developed several functions to plot body region of Gene/Transcript/Exon/Intron/3UTR/5UTR. If users want to get more information from the body region, we added upstream and downstream parameters to functions in order to get flank extension of body regions. upstream and downstream can be NULL(default), rel object and actual numbers. NULL(default) reflects body regions with no flank extension. Rel object reflects the percentage of total length of body regions. Actual numbers reflects the actual length of flank extension.
## Here uses `plotPeakProf2` to do all things in one step.
## Gene body regions having lengths smaller than nbin will be filtered
## A message will be given to warning users about that.
## >> 9 peaks(0.872093%), having lengths smaller than 800bp, are filtered...
## the ignore_strand is FALSE in default. We put here to emphasize that.
## We will not show it again in the below example
plotPeakProf2(peak = peak, upstream = rel(0.2), downstream = rel(0.2),
conf = 0.95, by = "gene", type = "body", nbin = 800,
TxDb = txdb, weightCol = "V5",ignore_strand = F)
Users can also get the profile ChIP peaks binding to gene body regions with no flank extension or flank extension decided by actual length.
## The first method using getBioRegion(), getTagMatrix() and plotPeakProf() to plot in three steps.
genebody <- getBioRegion(TxDb = txdb,
by = "gene",
type = "body")
matrix_no_flankextension <- getTagMatrix(peak,windows = genebody, nbin = 800)
plotPeakProf(matrix_no_flankextension,conf = 0.95)
## The second method of using getTagMatrix() and plotPeakProf() to plot in two steps
matrix_actual_extension <- getTagMatrix(peak,windows = genebody, nbin = 800,
upstream = 1000,downstream = 1000)
plotPeakProf(matrix_actual_extension,conf = 0.95)Users can also get the body region of 5UTR/3UTR.
Peak Annotation
## >> loading peak file... 2024-04-30 11:05:51 PM
## >> preparing features information... 2024-04-30 11:05:51 PM
## >> identifying nearest features... 2024-04-30 11:05:51 PM
## >> calculating distance from peak to TSS... 2024-04-30 11:05:52 PM
## >> assigning genomic annotation... 2024-04-30 11:05:52 PM
## >> adding gene annotation... 2024-04-30 11:06:05 PM
## >> assigning chromosome lengths 2024-04-30 11:06:06 PM
## >> done... 2024-04-30 11:06:06 PMNote that it would also be possible to use Ensembl-based EnsDb annotation databases created by the ensembldb package for the peak annotations by providing it with the TxDb parameter. Since UCSC-style chromosome names are used we have to change the style of the chromosome names from Ensembl to UCSC in the example below.
library(EnsDb.Hsapiens.v75)
edb <- EnsDb.Hsapiens.v75
seqlevelsStyle(edb) <- "UCSC"
peakAnno.edb <- annotatePeak(files[[4]], tssRegion=c(-3000, 3000),
TxDb=edb, annoDb="org.Hs.eg.db")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.
Genomic Annotation by pieplot
Genomic Annotation by barplot
Since some annotation overlap, user may interested to view the full annotation with their overlap, which can be partially resolved by vennpie function.
Genomic Annotation by vennpie
We extend UpSetR to view full annotation overlap. User can user upsetplot function.
We can combine vennpie with upsetplot by setting 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.
Distribution of Binding Sites
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)(Ashburner et al. 2000) annotates genes to biological processes, molecular functions, and cellular components in a directed acyclic graph structure, Kyoto Encyclopedia of Genes and Genomes (KEGG)(Kanehisa et al. 2004) annotates genes to pathways, Disease Ontology (DO)(Schriml et al. 2011) annotates genes with human disease association, and Reactome(Croft et al. 2013) 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 DOSE(Yu et al. 2015) for Disease Ontology, ReactomePA for reactome pathway, clusterProfiler(Yu et al. 2012) for Gene Ontology and KEGG enrichment analysis.
## ID Description GeneRatio BgRatio pvalue
## R-HSA-9830369 R-HSA-9830369 Kidney development 19/498 46/11091 2.372217e-14
## R-HSA-9758941 R-HSA-9758941 Gastrulation 27/498 125/11091 6.167816e-12
## p.adjust qvalue
## R-HSA-9830369 2.476595e-11 2.467106e-11
## R-HSA-9758941 3.219600e-09 3.207264e-09
## geneID
## R-HSA-9830369 2625/5076/7490/652/6495/2303/3975/6928/10736/5455/7849/3237/6092/2122/255743/2296/3400/28514/2138
## R-HSA-9758941 5453/5692/5076/5080/7546/3169/652/5015/2303/5717/3975/2627/5714/344022/7849/5077/2637/7022/8320/7545/6657/4487/51176/2296/28514/2626/64321
## Count
## R-HSA-9830369 19
## R-HSA-9758941 27gene <- seq2gene(peak, tssRegion = c(-1000, 1000), flankDistance = 3000, TxDb=txdb)
pathway2 <- enrichPathway(gene)
head(pathway2, 2)## ID Description GeneRatio BgRatio pvalue
## R-HSA-9830369 R-HSA-9830369 Kidney development 17/407 46/11091 1.878830e-13
## R-HSA-9758941 R-HSA-9758941 Gastrulation 24/407 125/11091 1.928201e-11
## p.adjust qvalue
## R-HSA-9830369 1.828102e-10 1.772034e-10
## R-HSA-9758941 9.380696e-09 9.092988e-09
## geneID
## R-HSA-9830369 2625/5076/3227/652/6495/2303/3975/3237/6092/2122/255743/7490/6928/7849/5455/2296/28514
## R-HSA-9758941 5453/5692/5076/7546/3169/652/2303/5717/3975/2627/5714/344022/2637/8320/7545/7020/2626/5080/5015/7849/51176/2296/64321/28514
## Count
## R-HSA-9830369 17
## R-HSA-9758941 24
More information can be found in the vignettes of Bioconductor packages DOSE(Yu et al. 2015), ReactomePA, clusterProfiler(Yu et al. 2012), which also provide several methods to visualize enrichment results. The clusterProfiler(Yu et al. 2012) 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, tagHeatmap and plotPeakProf can accept a list of tagMatrix and visualize profile or heatmap among several ChIP experiments, while plotAvgProf2 , peakHeatmap and plotPeakProf2 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))## >> plotting figure... 2024-04-30 11:07:02 PM
Average Profiles of ChIP peaks among different experiments
## normal method
plotPeakProf2(files, upstream = 3000, downstream = 3000, conf = 0.95,
by = "gene", type = "start_site", TxDb = txdb,
facet = "row")
## binning method
plotPeakProf2(files, upstream = 3000, downstream = 3000, conf = 0.95,
by = "gene", type = "start_site", TxDb = txdb,
facet = "row", nbin = 800)
Profile of several ChIP peak data binding to body region
Functions plotPeakProf and plotPeakProf2 also support to plot profile of several ChIP peak data binding to body region.
plotPeakProf2(files, upstream = rel(0.2), downstream = rel(0.2),
conf = 0.95, by = "gene", type = "body",
TxDb = txdb, facet = "row", nbin = 800)
ChIP peak annotation comparision
The plotAnnoBar and plotDistToTSS can also accept input of a named list of annotated peaks (output of annotatePeak).
We can use plotAnnoBar to comparing their genomic annotation.
Genomic Annotation among different ChIPseq data
R function plotDistToTSS can use to comparing distance to TSS profiles among ChIPseq data.
Distribution of Binding Sites among different ChIPseq data
Functional profiles comparison
As shown in section 4, the annotated genes can analyzed by r Biocpkg("clusterProfiler")(Yu et al. 2012), r Biocpkg("DOSE")(Yu et al. 2015), meshes and r Biocpkg("ReactomePA") for Gene Ontology, KEGG, Disease Ontology, MeSH and Reactome Pathway enrichment analysis.
The clusterProfiler(Yu et al. 2012) 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")
dotplot(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.
Overlap of annotated 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 177147963-177148012 *
## [2] chr3 197129825-197129855 *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsWe 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 N_OL
## ARmo_0M GSM1174480_ARmo_0M_peaks.bed.gz 1663 812 0
## ARmo_1nM GSM1174481_ARmo_1nM_peaks.bed.gz 1663 2296 8
## ARmo_100nM GSM1174482_ARmo_100nM_peaks.bed.gz 1663 1359 3
## CBX6_BF GSM1295076_CBX6_BF_ChipSeq_mergedReps_peaks.bed.gz 1663 1331 968
## pvalue p.adjust
## ARmo_0M 0.82352941 0.82352941
## ARmo_1nM 0.05882353 0.11764706
## ARmo_100nM 0.35294118 0.47058824
## CBX6_BF 0.01960784 0.07843137Parameter 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
## species Freq
## 1 Actinobacillus pleuropneumoniae 1
## 2 Actinobacillus pleuropneumoniae serovar 3 str. JL03 1
## 3 Aedes aegypti 11
## 4 Anabaena 6
## 5 Anolis carolinensis 7
## 6 Anopheles gambiae 2
## 7 Apis mellifera 17
## 8 Apis mellifera scutellata 1
## 9 Arabidopsis 17
## 10 Arabidopsis lyrata 4
## 11 Arabidopsis thaliana 2473
## 12 Atelerix albiventris 2
## 13 Bombus terrestris 8
## 14 Bos taurus 257
## 15 Brachypodium distachyon 10
## 16 Branchiostoma lanceolatum 93
## 17 Brassica rapa 12
## 18 Caenorhabditis elegans 532
## 19 Callithrix jacchus 48
## 20 Candida albicans 34
## 21 Candida dubliniensis 20
## 22 Canis lupus 1
## 23 Canis lupus familiaris 35
## 24 Capsaspora owczarzaki 21
## 25 Carica papaya 1
## 26 Caulobacter crescentus NA1000 8
## 27 Caulobacter vibrioides 2
## 28 Chlamydomonas reinhardtii 53
## 29 Chlorocebus aethiops 11
## 30 Chlorocebus sabaeus 6
## 31 Ciona savignyi 4
## 32 Citrullus lanatus 3
## 33 Cleome hassleriana 1
## 34 Columba livia 6
## 35 Corynebacterium glutamicum 9
## 36 Crassostrea gigas 4
## 37 Cryptococcus neoformans 67
## 38 Cucumis melo 9
## 39 Cucumis sativus 3
## 40 Cyprinus carpio 40
## 41 Danio rerio 532
## 42 Daphnia magna 36
## 43 Daphnia pulex 14
## 44 Dictyostelium discoideum 77
## 45 Dimocarpus longan 9
## 46 Dromaius novaehollandiae 27
## 47 Drosophila busckii 2
## 48 Drosophila melanogaster 1483
## 49 Drosophila melanogaster;\tSindbis virus 3
## 50 Drosophila miranda 2
## 51 Drosophila pseudoobscura 7
## 52 Drosophila simulans 12
## 53 Drosophila virilis 28
## 54 Drosophila willistoni 1
## 55 Drosophila yakuba 8
## 56 Equus caballus 1
## 57 Escherichia coli 126
## 58 Escherichia coli BW25113 4
## 59 Escherichia coli K-12 2
## 60 Escherichia coli str. K-12 substr. MG1655 16
## 61 Eutrema salsugineum 12
## 62 Fragaria vesca 3
## 63 Fusarium oxysporum 4
## 64 Gallus gallus 292
## 65 Gasterosteus aculeatus 1
## 66 Geobacter sulfurreducens PCA 3
## 67 Glycine max 31
## 68 Gorilla gorilla 8
## 69 Haloferax volcanii 3
## 70 Helianthus annuus 2
## 71 Heterocephalus glaber 4
## 72 Histophilus somni 1
## 73 Homo sapiens 51828
## 74 Homo sapiens;\tHuman betaherpesvirus 6 2
## 75 Homo sapiens;\tHuman betaherpesvirus 6A 38
## 76 Homo sapiens;\tHuman gammaherpesvirus 8 12
## 77 Homo sapiens;\tHuman herpesvirus 8 6
## 78 Homo sapiens;\tHuman poliovirus 1 strain Sabin 5
## 79 Homo sapiens;\tInfluenza A virus (A/hvPR8/34(H1N1)) 12
## 80 Homo sapiens;\tMus musculus 13
## 81 Homo sapiens;\tSindbis virus 8
## 82 Human gammaherpesvirus 4 3
## 83 Human herpesvirus 6B 2
## 84 Human herpesvirus 8 6
## 85 Human immunodeficiency virus 11
## 86 Hydra vulgaris 6
## 87 Komagataella phaffii 12
## 88 Larimichthys crocea 7
## 89 Leersia perrieri 1
## 90 Legionella pneumophila 5
## 91 Leishmania amazonensis 4
## 92 Leishmania major 14
## 93 Leishmania major strain Friedlin 4
## 94 Leishmania tarentolae 15
## 95 Macaca mulatta 136
## 96 Macaca mulatta polyomavirus 1 7
## 97 Macaca mulatta;\tMacacine gammaherpesvirus 4 3
## 98 Malus domestica 21
## 99 Marchantia polymorpha 1
## 100 Medicago truncatula 8
## 101 Mesoplasma florum 1
## 102 Microcebus murinus 6
## 103 Moloney murine leukemia virus 9
## 104 Monodelphis domestica 31
## 105 Moraxella catarrhalis O35E 6
## 106 Mus 2
## 107 Mus musculus 38038
## 108 Mus musculus musculus x M. m. castaneus 1
## 109 Mus musculus x Mus spretus 11
## 110 Mus musculus;\tHuman herpesvirus 1 strain KOS 2
## 111 Mus musculus;\tMurid herpesvirus 68 2
## 112 Musa acuminata AAA Group 3
## 113 Mycobacterium abscessus ATCC 19977 4
## 114 Mycobacterium marinum 1
## 115 Mycobacterium tuberculosis 2
## 116 Mycobacterium tuberculosis H37Rv 5
## 117 Mycolicibacterium smegmatis 2
## 118 Mycolicibacterium smegmatis MC2 155 2
## 119 Myotis brandtii 15
## 120 Naumovozyma castellii 1
## 121 Nematostella vectensis 31
## 122 Neurospora crassa 3
## 123 Nicotiana tabacum 6
## 124 Nicrophorus vespilloides 4
## 125 Nippostrongylus brasiliensis 9
## 126 Nomascus leucogenys 1
## 127 Oreochromis niloticus 1
## 128 Ornithorhynchus anatinus 5
## 129 Oryza brachyantha 11
## 130 Oryza glaberrima 1
## 131 Oryza punctata 1
## 132 Oryza sativa 110
## 133 Oryza sativa Japonica Group 17
## 134 Oryzias latipes 26
## 135 Ovis aries 2
## 136 Pan troglodytes 106
## 137 Pantherophis guttatus 7
## 138 Papio anubis 3
## 139 Patiria miniata 1
## 140 Penicillium chrysogenum 1
## 141 Petunia x hybrida 10
## 142 Phascolarctos cinereus 2
## 143 Phaseolus coccineus 2
## 144 Plasmodium falciparum 220
## 145 Plasmodium falciparum 3D7 43
## 146 Plectus sambesii 1
## 147 Pongo pygmaeus 6
## 148 Procambarus fallax 6
## 149 Procambarus virginalis 16
## 150 Pseudomonas aeruginosa PAO1 7
## 151 Pseudomonas putida KT2440 2
## 152 Pseudomonas savastanoi pv. phaseolicola 1448A 3
## 153 Pseudozyma aphidis 11
## 154 Pyricularia oryzae 18
## 155 Pyrococcus furiosus 4
## 156 Pyrus x bretschneideri 4
## 157 Rattus norvegicus 237
## 158 Rhodobacter sphaeroides 12
## 159 Rhodopseudomonas palustris 6
## 160 Rhodopseudomonas palustris CGA009 3
## 161 Romanomermis culicivorax 1
## 162 Saccharomyces cerevisiae 3740
## 163 Saccharomyces cerevisiae BY4741 8
## 164 Saccharomyces cerevisiae S288C 18
## 165 Saccharomyces cerevisiae x Saccharomyces paradoxus 16
## 166 Saccharomyces cerevisiae;\tHomo sapiens 2
## 167 Saccharomyces cerevisiae;\tMus musculus 12
## 168 Saccharomyces kudriavzevii 1
## 169 Saccharomyces paradoxus 8
## 170 Saccharomyces uvarum 1
## 171 Salmo salar 32
## 172 Salmonella enterica subsp. enterica serovar Typhimurium str. SL1344 4
## 173 Sarcophilus harrisii 3
## 174 Schizosaccharomyces japonicus 2
## 175 Schizosaccharomyces pombe 460
## 176 Schmidtea mediterranea 7
## 177 Solanum lycopersicum 76
## 178 Solanum pennellii 18
## 179 Sorghum bicolor 3
## 180 Spodoptera frugiperda 16
## 181 Streptomyces coelicolor A3(2) 6
## 182 Strongylocentrotus purpuratus 36
## 183 Sus scrofa 334
## 184 synthetic construct 42
## 185 Taeniopygia guttata 21
## 186 Tetrahymena thermophila 19
## 187 Tetrahymena thermophila CU428 6
## 188 Thermus thermophilus HB27 4
## 189 Torulaspora microellipsoides 6
## 190 Toxoplasma gondii 21
## 191 Toxoplasma gondii RH 12
## 192 Tribolium castaneum 14
## 193 Trichinella spiralis 1
## 194 Trichuris muris 4
## 195 Triticum aestivum 127
## 196 Trypanosoma brucei 12
## 197 Tupaia chinensis 7
## 198 Vibrio cholerae 8
## 199 Vitis vinifera 2
## 200 Xenopus (Silurana) tropicalis 62
## 201 Xenopus laevis 29
## 202 Xenopus tropicalis 133
## 203 Xenopus tropicalis x Xenopus laevis 4
## 204 Zea mays 173The summary can also based on genome version as illustrated below:
## organism genomeVersion Freq
## 1 Anolis carolinensis anoCar2 7
## 2 Bos taurus bosTau4 2
## 3 Bos taurus bosTau6 33
## 4 Bos taurus bosTau7 2
## 5 Canis lupus familiaris canFam3 10
## 6 Caenorhabditis elegans ce10 328
## 7 Caenorhabditis elegans ce6 64
## 8 Danio rerio danRer6 89
## 9 Danio rerio danRer7 89
## 10 Drosophila melanogaster dm3 767
## 11 Gallus gallus galGal3 33
## 12 Gallus gallus galGal4 73
## 13 Gasterosteus aculeatus gasAcu1 1
## 14 Heterocephalus glaber hetGla2 4
## 15 Homo sapiens hg18 3368
## 16 Homo sapiens hg19 30326
## 17 Homo sapiens hg38 4091
## 18 Mus musculus mm10 19089
## 19 Mus musculus mm8 556
## 20 Mus musculus mm9 17247
## 21 Monodelphis domestica monDom5 31
## 22 Ovis aries oviAri3 2
## 23 Pan troglodytes panTro3 48
## 24 Pan troglodytes panTro4 48
## 25 Macaca mulatta rheMac2 84
## 26 Macaca mulatta rheMac3 40
## 27 Rattus norvegicus rn5 66
## 28 Saccharomyces cerevisiae sacCer2 145
## 29 Saccharomyces cerevisiae sacCer3 2646
## 30 Sus scrofa susScr2 17
## 31 Sus scrofa susScr3 18
## 32 Taeniopygia guttata taeGut2 20
## 33 Xenopus (Silurana) tropicalis xenTro3 3User can access the detail information by getGEOInfo, for each genome version.
## 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 19829295If 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.
Or a vector of GSM accession number by downloadGSMbedFiles.
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.
Need helps?
If you have questions/issues, please visit ChIPseeker homepage first. Your problems are mostly documented. If you think you found a bug, please follow the guide and provide a reproducible example to be posted on github issue tracker. For questions, please post to Bioconductor support site and tag your post with ChIPseeker.
For Chinese user, you can follow me on WeChat (微信).
Session Information
Here is the output of sessionInfo() on the system on which this document was compiled:
## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ChIPseeker_1.40.0
## [2] ReactomePA_1.48.0
## [3] clusterProfiler_4.12.0
## [4] ggplot2_3.5.1
## [5] org.Hs.eg.db_3.19.1
## [6] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [7] GenomicFeatures_1.56.0
## [8] AnnotationDbi_1.66.0
## [9] Biobase_2.64.0
## [10] GenomicRanges_1.56.0
## [11] GenomeInfoDb_1.40.0
## [12] IRanges_2.38.0
## [13] S4Vectors_0.42.0
## [14] BiocGenerics_0.50.0
## [15] yulab.utils_0.1.4
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.8
## [3] magrittr_2.0.3 farver_2.1.1
## [5] rmarkdown_2.26 fs_1.6.4
## [7] BiocIO_1.14.0 zlibbioc_1.50.0
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.20.0 RCurl_1.98-1.14
## [13] prettydoc_0.4.1 ggtree_3.12.0
## [15] htmltools_0.5.8.1 S4Arrays_1.4.0
## [17] plotrix_3.8-4 curl_5.2.1
## [19] SparseArray_1.4.0 gridGraphics_0.5-1
## [21] sass_0.4.9 KernSmooth_2.23-22
## [23] bslib_0.7.0 plyr_1.8.9
## [25] cachem_1.0.8 GenomicAlignments_1.40.0
## [27] igraph_2.0.3 lifecycle_1.0.4
## [29] pkgconfig_2.0.3 gson_0.1.0
## [31] Matrix_1.7-0 R6_2.5.1
## [33] fastmap_1.1.1 GenomeInfoDbData_1.2.12
## [35] MatrixGenerics_1.16.0 digest_0.6.35
## [37] aplot_0.2.2 enrichplot_1.24.0
## [39] colorspace_2.1-0 patchwork_1.2.0
## [41] RSQLite_2.3.6 labeling_0.4.3
## [43] fansi_1.0.6 httr_1.4.7
## [45] polyclip_1.10-6 abind_1.4-5
## [47] compiler_4.4.0 bit64_4.0.5
## [49] withr_3.0.0 graphite_1.50.0
## [51] BiocParallel_1.38.0 viridis_0.6.5
## [53] DBI_1.2.2 highr_0.10
## [55] gplots_3.1.3.1 ggforce_0.4.2
## [57] MASS_7.3-60.2 rappdirs_0.3.3
## [59] DelayedArray_0.30.0 rjson_0.2.21
## [61] HDO.db_0.99.1 caTools_1.18.2
## [63] gtools_3.9.5 tools_4.4.0
## [65] scatterpie_0.2.2 ape_5.8
## [67] glue_1.7.0 restfulr_0.0.15
## [69] nlme_3.1-164 GOSemSim_2.30.0
## [71] shadowtext_0.1.3 grid_4.4.0
## [73] reshape2_1.4.4 fgsea_1.30.0
## [75] generics_0.1.3 gtable_0.3.5
## [77] tidyr_1.3.1 data.table_1.15.4
## [79] tidygraph_1.3.1 utf8_1.2.4
## [81] XVector_0.44.0 ggrepel_0.9.5
## [83] pillar_1.9.0 stringr_1.5.1
## [85] splines_4.4.0 dplyr_1.1.4
## [87] tweenr_2.0.3 treeio_1.28.0
## [89] lattice_0.22-6 rtracklayer_1.64.0
## [91] bit_4.0.5 tidyselect_1.2.1
## [93] GO.db_3.19.1 Biostrings_2.72.0
## [95] reactome.db_1.88.0 knitr_1.46
## [97] gridExtra_2.3 SummarizedExperiment_1.34.0
## [99] xfun_0.43 graphlayouts_1.1.1
## [101] matrixStats_1.3.0 stringi_1.8.3
## [103] UCSC.utils_1.0.0 boot_1.3-30
## [105] lazyeval_0.2.2 ggfun_0.1.4
## [107] yaml_2.3.8 evaluate_0.23
## [109] codetools_0.2-20 ggraph_2.2.1
## [111] tibble_3.2.1 qvalue_2.36.0
## [113] graph_1.82.0 ggplotify_0.1.2
## [115] cli_3.6.2 munsell_0.5.1
## [117] jquerylib_0.1.4 Rcpp_1.0.12
## [119] png_0.1-8 XML_3.99-0.16.1
## [121] parallel_4.4.0 blob_1.2.4
## [123] DOSE_3.30.0 bitops_1.0-7
## [125] viridisLite_0.4.2 tidytree_0.4.6
## [127] scales_1.3.0 purrr_1.0.2
## [129] crayon_1.5.2 rlang_1.1.3
## [131] cowplot_1.1.3 fastmatch_1.1-4
## [133] KEGGREST_1.44.0References
Ashburner, Michael, Catherine A. Ball, Judith A. Blake, David Botstein, Heather Butler, J. Michael Cherry, Allan P. Davis, et al. 2000. “Gene Ontology: Tool for the Unification of Biology.” Nat Genet 25 (1): 25–29. https://doi.org/10.1038/75556.
Croft, D., A. F. Mundo, R. Haw, M. Milacic, J. Weiser, G. Wu, M. Caudy, et al. 2013. “The Reactome Pathway Knowledgebase.” Nucleic Acids Research 42 (D1): D472–D477. https://doi.org/10.1093/nar/gkt1102.
Kanehisa, Minoru, Susumu Goto, Shuichi Kawashima, Yasushi Okuno, and Masahiro Hattori. 2004. “The KEGG Resource for Deciphering the Genome.” Nucleic Acids Research 32 (suppl 1): D277–D280. https://doi.org/10.1093/nar/gkh063.
Pemberton, Helen, Emma Anderton, Harshil Patel, Sharon Brookes, Hollie Chandler, Richard Palermo, Julie Stock, et al. 2014. “Genome-Wide Co-Localization of Polycomb Orthologs and Their Effects on Gene Expression in Human Fibroblasts.” Genome Biology 15 (2): R23. https://doi.org/10.1186/gb-2014-15-2-r23.
Ramı́rez, Fidel, Devon P Ryan, Björn Grüning, Vivek Bhardwaj, Fabian Kilpert, Andreas S Richter, Steffen Heyne, Friederike Dündar, and Thomas Manke. 2016. “DeepTools2: A Next Generation Web Server for Deep-Sequencing Data Analysis.” Nucleic Acids Research 44 (W1): W160–W165.
Schriml, L. M., C. Arze, S. Nadendla, Y.-W. W. Chang, M. Mazaitis, V. Felix, G. Feng, and W. A. Kibbe. 2011. “Disease Ontology: A Backbone for Disease Semantic Integration.” Nucleic Acids Research 40 (D1): D940–D946. https://doi.org/10.1093/nar/gkr972.
Urbanucci, A, B Sahu, J Seppälä, A Larjo, L M Latonen, K K Waltering, T L J Tammela, et al. 2012. “Overexpression of Androgen Receptor Enhances the Binding of the Receptor to the Chromatin in Prostate Cancer.” Oncogene 31 (17): 2153–63. https://doi.org/10.1038/onc.2011.401.
Yu, Guangchuang, and Qing-Yu He. 2016. “ReactomePA: An R/Bioconductor Package for Reactome Pathway Analysis and Visualization.” Molecular BioSystems 12 (2): 477–79. https://doi.org/10.1039/C5MB00663E.
Yu, Guangchuang, Li-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters.” OMICS: A Journal of Integrative Biology 16 (5): 284–87. https://doi.org/10.1089/omi.2011.0118.
Yu, Guangchuang, Li-Gen Wang, and Qing-Yu He. 2015. “ChIPseeker: An R/Bioconductor Package for Chip Peak Annotation, Comparison and Visualization.” Bioinformatics 31 (14): 2382–3. https://doi.org/10.1093/bioinformatics/btv145.
Yu, Guangchuang, Li-Gen Wang, Guang-Rong Yan, and Qing-Yu He. 2015. “DOSE: An R/Bioconductor Package for Disease Ontology Semantic and Enrichment Analysis.” Bioinformatics 31 (4): 608–9. https://doi.org/10.1093/bioinformatics/btu684.