---
title: "The ChIPpeakAnno user's guide"
author: "Jianhong Ou, Jun Yu, Lihua Julie Zhu"
output:
  html_document:
    theme: simplex
    toc: true
    toc_float: true
    toc_depth: 4
    fig_caption: true
date: "`r BiocStyle::doc_date()`"
package: "`r BiocStyle::pkg_ver('ChIPpeakAnno')`"
bibliography: bibliography.bib
csl: nature.csl
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.
vignette: >
  %\VignetteIndexEntry{ChIPpeakAnno Vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r, echo=FALSE, results="hide", warning=FALSE}
suppressPackageStartupMessages({
    library(ChIPpeakAnno)
    library(org.Hs.eg.db)
    library(GenomicFeatures)
    library(TxDb.Hsapiens.UCSC.hg38.knownGene)
    library(rtracklayer)
    library(GO.db)
    library(EnsDb.Hsapiens.v75)
    library(BSgenome.Ecoli.NCBI.20080805)
    library(reactome.db)
})
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```

# 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 Bioconductor[@Gentleman2004] 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 discovery[@Durinck2005]. 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** package[@Benjamini1995; @benjamini2001; @johnson2005; @Holm1979; @Hochberg1988; @dudoit2003].
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.  


# Quick start
```{r quickStart}
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")
}
```



# 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.

```{r workflow19, fig.cap="venn diagram of overlaps for duplicated experiments"}
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))
gr1[1:2]
gr1.import[1:2] #note the name slot is different from gr1
gff <- system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno")
gr2 <- toGRanges(gff, format="GFF", header=FALSE, skip=3)
ol <- findOverlapsOfPeaks(gr1, gr2)
makeVennDiagram(ol,
                fill=c("#009E73", "#F0E442"), # circle fill color
                col=c("#D55E00", "#0072B2"), #circle border color
                cat.col=c("#D55E00", "#0072B2"))
```

A pie chart is used to demonstrate the overlap features of the common peaks.

```{r workflow20,fig.cap="Pie chart of common peaks among features"}
pie1(table(ol$overlappingPeaks[["gr1///gr2"]]$overlapFeature))
```

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.


```{r workflow21}
overlaps <- ol$peaklist[["gr1///gr2"]]
## ============== old style ===========
## data(TSS.human.GRCh37)
## overlaps.anno <- annotatePeakInBatch(overlaps, AnnotationData=annoData,
##                                      output="overlapping", maxgap=5000L)
## overlaps.anno <- addGeneIDs(overlaps.anno, "org.Hs.eg.db", "symbol")
## ============== new style ===========
library(EnsDb.Hsapiens.v75) ##(hg19)
## create annotation file from EnsDb or TxDb
annoData <- toGRanges(EnsDb.Hsapiens.v75, feature="gene")
annoData[1:2]
overlaps.anno <- annotatePeakInBatch(overlaps, AnnotationData=annoData,
                                    output="overlapping", maxgap=5000L)
overlaps.anno$gene_name <-
    annoData$gene_name[match(overlaps.anno$feature,
                             names(annoData))]
head(overlaps.anno)
```

Once the peaks are annotated, the distribution of the distance to the nearest
feature such as the transcription start sites (TSS) can be plotted.
The sample code here plots the distribution
of the aggregated peak scores and the number of peaks around the TSS.

```{r workflow22,fig.cap="Distribution of aggregated peak scores or peak numbers around transcript start sites.",fig.width=8,fig.height=6}
gr1.copy <- gr1
gr1.copy$score <- 1
binOverFeature(gr1, gr1.copy, annotationData=annoData,
               radius=5000, nbins=10, FUN=c(sum, length),
               ylab=c("score", "count"),
               main=c("Distribution of aggregated peak scores around TSS",
                      "Distribution of aggregated peak numbers around TSS"))
```

The distribution of the peaks over exon, intron, enhancer, proximal promoter,
5' UTR and 3' UTR can be summarized in peak centric or nucleotide centric view using
the function `assignChromosomeRegion`.
Please note that setting nucleotideLevel = TRUE will give a nucleotide level distribution over
different features.

```{r workflow23,fig.cap="Peak distribution over different genomic features.",fig.width=10,fig.height=4}
if(require(TxDb.Hsapiens.UCSC.hg19.knownGene)){
    aCR<-assignChromosomeRegion(gr1, nucleotideLevel=FALSE,
                           precedence=c("Promoters", "immediateDownstream",
                                         "fiveUTRs", "threeUTRs",
                                         "Exons", "Introns"),
                           TxDb=TxDb.Hsapiens.UCSC.hg19.knownGene)
    barplot(aCR$percentage)
}
```

# Detailed Use Cases and Scenarios

Here we describe some details in using different functions in `ChIPpeakAnno`
for different tasks. As shown in the last section, the common workflow includes:
loading called peaks from BED, GFF, or other formats; evaluating and visualizing
the concordance among the biological replicates; combining peaks from
replicates; preparing genomic annotation(s) as GRanges; associating/annotating
peaks with the annotation(s); summarizing peak
distributions over exon, intron, enhancer, proximal promoter, 5'UTR and 3'UTR
regions; retrieving the sequences around the peaks; and enrichment analysis of GO and
biological pathway. We also 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.

## 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.

```{r findOverlapsOfPeaks3}
peaks1 <- GRanges(seqnames=c("1", "2", "3", "4", "5", "6",
                              "2", "6", "6", "6", "6", "5"),
                   ranges=IRanges(start=c(967654, 2010897, 2496704, 3075869,
                                          3123260, 3857501, 201089, 1543200,
                                          1557200, 1563000, 1569800, 167889600),
                                  end= c(967754, 2010997, 2496804, 3075969,
                                         3123360, 3857601, 201089, 1555199,
                                         1560599, 1565199, 1573799, 167893599),
                                  names=paste("Site", 1:12, sep="")),
                  strand="+")

peaks2 <- GRanges(seqnames=c("1", "2", "3", "4", "5", "6", "1", "2", "3",
                                     "4", "5", "6", "6", "6", "6", "6", "5"),
                          ranges=IRanges(start=c(967659, 2010898, 2496700,
                                                 3075866, 3123260, 3857500,
                                                 96765, 201089, 249670, 307586,
                                                 312326, 385750, 1549800,
                                                 1554400, 1565000, 1569400,
                                                 167888600),
                                         end=c(967869, 2011108, 2496920,
                                               3076166,3123470, 3857780,
                                               96985, 201299, 249890, 307796,
                                               312586, 385960, 1550599, 1560799,
                                               1565399, 1571199, 167888999),
                                         names=paste("t", 1:17, sep="")),
                          strand=c("+", "+", "+", "+", "+", "+", "-", "-", "-",
                                   "-", "-", "-", "+", "+", "+", "+", "+"))

ol <- findOverlapsOfPeaks(peaks1, peaks2, maxgap=1000)
peaklist <- ol$peaklist
```

The function `findOverlapsOfPeaks` returns an object of **overlappingPeaks**,
which contains there elements: venn_cnt, peaklist (a list 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.

```{r overlappingPeaks4,fig.cap="Pie chart of common peaks among features."}
overlappingPeaks <- ol$overlappingPeaks
names(overlappingPeaks)
dim(overlappingPeaks[["peaks1///peaks2"]])
overlappingPeaks[["peaks1///peaks2"]][1:2, ]
pie1(table(overlappingPeaks[["peaks1///peaks2"]]$overlapFeature))
```

The following code returns the merged overlapping peaks from the peaklist object.


```{r overlappingPeaks5}
peaklist[["peaks1///peaks2"]]
```


The peaks in peaks1 but not overlap with the peaks in peaks2 can be obtained with:


```{r 6}
peaklist[["peaks1"]]
```


The peaks in peaks2 but not overlap with the peaks in peaks1 can be obtained with:


```{r 7}
peaklist[["peaks2"]]
```

Venn diagram can be generated by the function `makeVennDiagram` using the output
of `findOverlapsOfPeaks` as an input.

The `makeVennDiagram` also outputs p-values indicating whether the overlapping is significant.

```{r findOverlapsOfPeaks8,fig.cap="venn diagram of overlaps",fig.width=6,fig.height=6}
makeVennDiagram(ol, totalTest=1e+2,
                fill=c("#009E73", "#F0E442"), # circle fill color
                col=c("#D55E00", "#0072B2"), #circle border color
                cat.col=c("#D55E00", "#0072B2"))
```

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`.


```{r VennerableFigure}
#     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.

```{r findOverlapsOfPeaks9,fig.cap="venn diagram of overlaps for three input peak lists",fig.width=6,fig.height=6}
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,
                fill=c("#CC79A7", "#56B4E9", "#F0E442"), # circle fill color
                col=c("#D55E00", "#0072B2", "#E69F00"), #circle border color
                cat.col=c("#D55E00", "#0072B2", "#E69F00"))
```

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](https://stat.ethz.ch/pipermail/bioconductor/2010-November/036540.html).
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**](#section410).


## 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 documentation[@Durinck2005].
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:

```{r annoGRgene}
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
annoData <- toGRanges(TxDb.Hsapiens.UCSC.hg19.knownGene, feature="gene")
annoData
```

## 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:

```{r annotatePeakInBatchByannoGR}
data(myPeakList)
annotatedPeak <- annotatePeakInBatch(myPeakList[1:6],
                                     AnnotationData = annoData)
annotatedPeak[1:3]
```

As discussed in the previous
section, all the genomic locations of the human genes have been precompiled, such as
TSS.human.NCBI36 dataset, using function `getAnnotation`. You can pass it
to the argument _annotaionData_ of the `annotatePeakInBatch` function.


```{r annotatePeakInBatch1}
data(TSS.human.NCBI36)
annotatedPeak <- annotatePeakInBatch(myPeakList[1:6],
                 AnnotationData=TSS.human.NCBI36)
annotatedPeak[1:3]
```

You can also pass the user defined features as annotationData.
A pie chart can be plotted to show the peak distribution among the
features after annotation.


```{r annotatePeakInBatch2}
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]
```
```{r pie1,fig.cap="Pie chart of peak distribution among features",fig.width=5,fig.height=5}
pie1(table(as.data.frame(annotatedPeak2)$insideFeature))
```

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.

```{r annotatedPromoter}
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]
```


In the function `annotatyePeakInBatch`, various parameters can be adjusted to
specify the way to calculate the distance and how the features are selected. For
example, _PeakLocForDistance_ is to specify the location of the peak for distance
calculation: "middle" (recommended) means using the middle of the peak, and
"start" (default, for backward compatibility) means using the start of the peak
to calculate the distance to the features. Similarly, _FeatureLocForDistance_ is to
specify the location of the feature for distance calculation: "middle" means using
the middle of the feature, "start" means using the start of the feature to calculate
the distance from the peak to the feature;  "TSS" (default) means using the
start of the feature when the feature is on plus strand and using the end of
feature when the feature is on minus strand; "geneEnd" means using end of the
feature when feature is on plus strand and using start of feature when feature
is on minus strand.

The argument "output" specifies the characteristics of the output of the
annotated features. The default is "nearestLocation", which means to output the
nearest features calculated as PeakLocForDistance-FeatureLocForDistance;
"overlapping" will output the overlapping features within the maximum gap specified as
maxgap between the peak range and feature range; "shortestDistance" will output the
nearest features; "both" will output all the nearest features, in addition, will
output any features that overlap the peak that are not the nearest features.
other options see ?annotatePeakInBatch.

## Find the overlapping and flanking features

In addition to 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".
```{r}
annotatedPeak <- annotatePeakInBatch(myPeakList[1:6],
                                     AnnotationData = annoData,
                                     output="both", maxgap=5000)
head(annotatedPeak)
```


## 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.


```{r addGeneIDs18}
data(annotatedPeak)
library(org.Hs.eg.db)
addGeneIDs(annotatedPeak[1:6], orgAnn="org.Hs.eg.db", IDs2Add=c("symbol"))
addGeneIDs(annotatedPeak$feature[1:6], orgAnn="org.Hs.eg.db",
           IDs2Add=c("symbol"))
```



## 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.


```{r getAllPeakSequence12}
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`.


```{r write2FASTA13}
write2FASTA(peaksWithSequences,"test.fa")
```


## 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`.


```{r heatmap,fig.cap="Heatmap of aligned features",fig.width=4,fig.height=6}
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))
```


```{r distribution,fig.cap="Distribution of aligned features",fig.width=6,fig.height=6}
featureAlignedDistribution(sig, feature.center,
                           upstream=2000, downstream=2000,
                           type="l")
```


## Output a summary of motif occurrences in the peaks.

Here is an example to search the motifs in the binding peaks. The motif patterns to be searched are saved in the file examplepattern.fa.


```{r summarizePatternInPeaks17}
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)
library(BSgenome.Ecoli.NCBI.20080805)
summarizePatternInPeaks(patternFilePath=filepath, format="fasta", skip=0L,
                        BSgenomeName=Ecoli, peaks=peaks)
```



## 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.
```{r getEnriched14}
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])
head(over[["cc"]][, -3])
head(over[["mf"]][, -3])
```
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](http://www.bioconductor.org/packages/release/data/annotation/),
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.

```{r egOrgMap15}
egOrgMap("Mus musculus")
egOrgMap("Homo sapiens")
```

To obtain enriched the pathways, use the following sample code.

```{r getEnrichedPath}
library(reactome.db)
enriched.PATH <- 
  getEnrichedPATH(annotatedPeak[1:500],
                  orgAnn="org.Hs.eg.db",
                  feature_id_type="ensembl_gene_id",
                  pathAnn="reactome.db",
                  maxP=0.01,
                  minPATHterm=10,
                  multiAdjMethod=NULL)#Try to change the method to BH
```

To add gene symbols to the enriched pathways. Use below.

```r {getEnrichedPath2}
ann <- addGeneIDs(enriched.PATH[,2], feature_id_type = "entrez_id", orgAnn = org.Hs.eg.db, IDs2Add = "symbol")
# enriched.PATH <- merge(ann, enriched.PATH, by.x = "entrez_id", by.y = "EntrezID")
# before v3.31.1

enriched.PATH <- merge(ann, enriched.PATH, by.x = "entrez_id", by.y = "entrez_id")
# after v3.31.1

head(enriched.PATH)
```

## 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 region[@robertson2007].
Here is an example to find peaks near bi-directional promoters and
output the percentage of the peaks near bi-directional promoters.

```{r peaksNearBDP16}
data(myPeakList)
data(TSS.human.NCBI36)
seqlevelsStyle(TSS.human.NCBI36) <- seqlevelsStyle(myPeakList)
annotatedBDP <- peaksNearBDP(myPeakList[1:10,],
                             AnnotationData=TSS.human.NCBI36,
                             MaxDistance=5000)
annotatedBDP$peaksWithBDP
c(annotatedBDP$percentPeaksWithBDP,
  annotatedBDP$n.peaks,
  annotatedBDP$n.peaksWithBDP)
```


## Perform permutation test to determine if there is a significant overlap between two sets of peaks
<a name="section410"></a>

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`:


```{r 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](http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeRegTfbsClustered/wgEncodeRegTfbsClusteredV3.bed.gz) 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 experiments[@yip2012]. 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](https://sites.google.com/site/anshulkundaje/projects/blacklists) 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 datasets[@encode2012integrated]. 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`:

```{r peakPermTest1}
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)
}
```

# Citing ChIPpeakAnno
Please cite `ChIPpeakAnno` in your publication as follows:
```{r citation, echo=FALSE}
citation(package="ChIPpeakAnno")
```


# Session Info
```{r sessionInfo}
sessionInfo()
```
# References
