Package 'ChIPpeakAnno'

Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments, or any experiments that result in large number of genomic interval data
Description: The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities.
Authors: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Junhui Li, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe, Michael Green
Maintainer: Jianhong Ou <[email protected]>, Lihua Julie Zhu <[email protected]>, Kai Hu <[email protected]>, Junhui Li <[email protected]>
License: GPL (>= 2)
Version: 3.41.0
Built: 2024-11-14 06:06:26 UTC
Source: https://github.com/bioc/ChIPpeakAnno

Help Index

Batch annotation of the peaks identified from either ChIP-seq or ChIP-chip experiments.

Description

The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites leveraging biomaRt, IRanges, Biostrings, BSgenome, GO.db, hypergeometric test phyper and multtest package.

Details

Package: ChIPpeakAnno
Type: Package
Version: 3.0.0
Date: 2014-10-24
License: LGPL
LazyLoad: yes

Author(s)

Lihua Julie Zhu, Jianhong Ou, Hervé Pagès, Claude Gazin, Nathan Lawson, Simon Lin, David Lapointe and Michael Green

Maintainer: Jianhong Ou <[email protected]>, Lihua Julie Zhu <[email protected]>

References

1. Y. Benjamini and Y. Hochberg (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B. Vol. 57: 289-300.
2. Y. Benjamini and D. Yekutieli (2001). The control of the false discovery rate in multiple hypothesis testing under dependency. Annals of Statistics. Accepted.
3. S. Durinck et al. (2005) BioMart and Bioconductor: a powerful link between biological biomarts and microarray data analysis. Bioinformatics, 21, 3439-3440.
4. S. Dudoit, J. P. Shaffer, and J. C. Boldrick (Submitted). Multiple hypothesis testing in microarray experiments.
5. Y. Ge, S. Dudoit, and T. P. Speed. Resampling-based multiple testing for microarray data hypothesis, Technical Report #633 of UCB Stat. http://www.stat.berkeley.edu/~gyc
6. Y. Hochberg (1988). A sharper Bonferroni procedure for multiple tests of significance, Biometrika. Vol. 75: 800-802.
7. S. Holm (1979). A simple sequentially rejective multiple test procedure. Scand. J. Statist.. Vol. 6: 65-70.
8. N. L. Johnson,S. Kotz and A. W. Kemp (1992) Univariate Discrete Distributions, Second Edition. New York: Wiley
9. Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237.

Examples

if(interactive()){
  data(myPeakList)
  library(ensembldb)
  library(EnsDb.Hsapiens.v75)
  anno <- annoGR(EnsDb.Hsapiens.v75)
  annotatedPeak <-
    annotatePeakInBatch(myPeakList[1:6], AnnotationData=anno)
 }

Add GO IDs of the ancestors for a given vector of GO ids

Description

Add GO IDs of the ancestors for a given vector of GO IDs leveraging GO.db

Usage

addAncestors(go.ids, ontology = c("bp", "cc", "mf"))

Arguments

go.ids

A matrix with 4 columns: first column is GO IDs and 4th column is entrez IDs.
ontology

bp for biological process, cc for cellular component and mf for molecular function.

Value

A vector of GO IDs containing the input GO IDs with the GO IDs of their ancestors added.

Author(s)

Lihua Julie Zhu

Examples

go.ids = cbind(c("GO:0008150", "GO:0005576", "GO:0003674"),
               c("ND", "IDA", "ND"), 
               c("BP", "BP", "BP"), 
               c("1", "1", "1"))
library(GO.db)
addAncestors(go.ids, ontology="bp")

Add common IDs to annotated peaks such as gene symbol, entrez ID, ensemble gene id and refseq id.

Description

Add common IDs to annotated peaks such as gene symbol, entrez ID, ensemble gene id and refseq id leveraging organism annotation dataset. For example, org.Hs.eg.db is the dataset from orgs.Hs.eg.db package for human, while org.Mm.eg.db is the dataset from the org.Mm.eg.db package for mouse.

Usage

addGeneIDs(
  annotatedPeak,
  orgAnn,
  IDs2Add = c("symbol"),
  feature_id_type = "ensembl_gene_id",
  silence = TRUE,
  mart
)

Arguments

annotatedPeak

GRanges or a vector of feature IDs.
orgAnn

organism annotation dataset such as org.Hs.eg.db.
IDs2Add

a vector of annotation identifiers to be added
feature_id_type

type of ID to be annotated, default is ensembl_gene_id
silence

TRUE or FALSE. If TRUE, will not show unmapped entrez id for feature ids.
mart

mart object, see useMart of biomaRt package for details

Details

One of orgAnn and mart should be assigned.

  • If orgAnn is given, parameter feature_id_type should be ensemble_gene_id, entrez_id, gene_symbol, gene_alias or refseq_id. And parameter IDs2Add can be set to any combination of identifiers such as "accnum", "ensembl", "ensemblprot", "ensembltrans", "entrez_id", "enzyme", "genename", "pfam", "pmid", "prosite", "refseq", "symbol", "unigene" and "uniprot". Some IDs are unique to an organism, such as "omim" for org.Hs.eg.db and "mgi" for org.Mm.eg.db.

    Here is the definition of different IDs :

    • accnum: GenBank accession numbers

    • ensembl: Ensembl gene accession numbers

    • ensemblprot: Ensembl protein accession numbers

    • ensembltrans: Ensembl transcript accession numbers

    • entrez_id: entrez gene identifiers

    • enzyme: EC numbers

    • genename: gene name

    • pfam: Pfam identifiers

    • pmid: PubMed identifiers

    • prosite: PROSITE identifiers

    • refseq: RefSeq identifiers

    • symbol: gene abbreviations

    • unigene: UniGene cluster identifiers

    • uniprot: Uniprot accession numbers

    • omim: OMIM(Mendelian Inheritance in Man) identifiers

    • mgi: Jackson Laboratory MGI gene accession numbers

  • If mart is used instead of orgAnn, for valid parameter feature_id_type and IDs2Add parameters, please refer to getBM in bioMart package. Parameter feature_id_type should be one valid filter name listed by listFilters(mart) such as ensemble_gene_id. And parameter IDs2Add should be one or more valid attributes name listed by listAttributes(mart) such as external_gene_id, entrezgene, wikigene_name, or mirbase_transcript_name.

Value

GRanges if the input is a GRanges or dataframe if input is a vector.

Author(s)

Jianhong Ou, Lihua Julie Zhu

References

http://www.bioconductor.org/packages/release/data/annotation/

See Also

getBM, AnnotationDb

Examples

data(annotatedPeak)
library(org.Hs.eg.db)
addGeneIDs(annotatedPeak[1:6,],orgAnn="org.Hs.eg.db",
           IDs2Add=c("symbol","omim"))
##addGeneIDs(annotatedPeak$feature[1:6],orgAnn="org.Hs.eg.db",
##           IDs2Add=c("symbol","genename"))
if(interactive()){
  mart <- useMart("ENSEMBL_MART_ENSEMBL",host="www.ensembl.org",
                  dataset="hsapiens_gene_ensembl")
  ##mart <- useMart(biomart="ensembl",dataset="hsapiens_gene_ensembl")
  addGeneIDs(annotatedPeak[1:6,], mart=mart,
             IDs2Add=c("hgnc_symbol","entrezgene"))
}

Add metadata of the GRanges objects used for findOverlapsOfPeaks

Description

Add metadata to to overlapping peaks after calling findOverlapsOfPeaks.

Usage

addMetadata(ol, colNames = NULL, FUN = c, ...)

Arguments

ol

An object of overlappingPeaks, which is output of findOverlapsOfPeaks.
colNames

Names of metadata column to be added. If it is NULL, addMetadata will guess what to add.
FUN

A function to be called
...

Arguments to the function call.

Value

return value is An object of overlappingPeaks.

Author(s)

Jianhong Ou

See Also

See Also as findOverlapsOfPeaks

Examples

peaks1 <- GRanges(seqnames=c(6,6,6,6,5),
                 IRanges(start=c(1543200,1557200,1563000,1569800,167889600),
                         end=c(1555199,1560599,1565199,1573799,167893599),
                         names=c("p1","p2","p3","p4","p5")),
                 strand="+",
                 score=1:5, id=letters[1:5])
peaks2 <- GRanges(seqnames=c(6,6,6,6,5),
                  IRanges(start=c(1549800,1554400,1565000,1569400,167888600),
                          end=c(1550599,1560799,1565399,1571199,167888999),
                          names=c("f1","f2","f3","f4","f5")),
                  strand="+",
                  score=6:10, id=LETTERS[1:5])
ol <- findOverlapsOfPeaks(peaks1, peaks2)
addMetadata(ol)

Class annoGR

Description

An object of class annoGR represents the annotation data could be used by annotationPeakInBatch.

Usage

## S4 method for signature 'annoGR'
info(object)

## S4 method for signature 'GRanges'
annoGR(ranges, feature = "group", date, ...)

## S4 method for signature 'TxDb'
annoGR(
  ranges,
  feature = c("gene", "transcript", "exon", "CDS", "fiveUTR", "threeUTR", "microRNA",
    "tRNAs", "geneModel"),
  date,
  source,
  mdata,
  OrganismDb
)

## S4 method for signature 'EnsDb'
annoGR(
  ranges,
  feature = c("gene", "transcript", "exon", "disjointExons"),
  date,
  source,
  mdata
)

Arguments

object

annoGR object.
ranges

an object of GRanges, TxDb or EnsDb
feature

annotation type
date

a Date object
...

could be following parameters
source

character, where the annotation comes from
mdata

data frame, metadata from annotation
OrganismDb

an object of OrganismDb. It is used for extracting gene symbol for geneModel group for TxDb

Slots

seqnames,ranges,strand,elementMetadata,seqinfo

slots inherit from GRanges. The ranges must have unique names.

source

character, where the annotation comes from

date

a Date object

feature

annotation type, could be "gene", "exon", "transcript", "CDS", "fiveUTR", "threeUTR", "microRNA", "tRNAs", "geneModel" for TxDb object, or "gene", "exon", "transcript" for EnsDb object

mdata

data frame, metadata from annotation

Objects from the Class

Objects can be created by calls of the form new("annoGR", date, elementMetadata, feature, mdata, ranges, seqinfo, seqnames, source, strand)

Author(s)

Jianhong Ou

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
        library(EnsDb.Hsapiens.v79)
        anno <- annoGR(EnsDb.Hsapiens.v79)
    }

Annotate peaks

Description

Annotate peaks by annoGR object in the given range.

Usage

annoPeaks(
  peaks,
  annoData,
  bindingType = c("nearestBiDirectionalPromoters", "startSite", "endSite", "fullRange"),
  bindingRegion = c(-5000, 5000),
  ignore.peak.strand = TRUE,
  select = c("all", "bestOne"),
  ...
)

Arguments

peaks

peak list, GRanges object
annoData

annotation data, GRanges object
bindingType

Specifying the criteria to associate peaks with annotation. Here is how to use it together with the parameter bindingRegion

  • To obtain peaks within 5kb upstream and up to 3kb downstream of TSS within the gene body, set bindingType = "startSite" and bindingRegion = c(-5000, 3000)

  • To obtain peaks up to 5kb upstream within the gene body and 3kb downstream of gene/Exon End, set bindingType = "endSite" and bindingRegion = c(-5000, 3000)

  • To obtain peaks from 5kb upstream to 3kb downstream of genes/Exons , set bindingType = "fullRange" and bindingRegion = c(-5000, 3000)

  • To obtain peaks with nearest bi-directional promoters within 5kb upstream and 3kb downstream of TSS, set bindingType = "nearestBiDirectionalPromoters" and bindingRegion = c(-5000, 3000)

startSite

start position of the feature (strand is considered)

endSite

end position of the feature (strand is considered)

fullRange

whole range of the feature

nearestBiDirectionalPromoters

nearest promoters from both direction of the peaks (strand is considered). It will report bidirectional promoters if there are promoters in both directions in the given region (defined by bindingRegion). Otherwise, it will report the closest promoter in one direction.
bindingRegion

Annotation range used together with bindingType, which is a vector with two integer values, default to c (-5000, 5000). The first one must be no bigger than 0, which means upstream. And the sec ond one must be no less than 1, which means downstream (1 is the site position, 2 is the next base of the site position). For details, see bindingType.
ignore.peak.strand

ignore the peaks strand or not.
select

"all" or "bestOne". Return the annotation containing all or the best one. The "bestOne" is selected by the shortest distance to the sites and then similarity between peak and annotations. Ignored if bindingType is nearestBiDirectionalPromoters.
...

Not used.

Value

Output is a GRanges object of the annotated peaks.

Author(s)

Jianhong Ou

See Also

See Also as annotatePeakInBatch

Examples

library(ensembldb)
    library(EnsDb.Hsapiens.v75)
    data("myPeakList")
    annoGR <- toGRanges(EnsDb.Hsapiens.v75)
    seqlevelsStyle(myPeakList) <- seqlevelsStyle(annoGR)
    annoPeaks(myPeakList, annoGR)

Annotated Peaks

Description

TSS annotated putative STAT1-binding regions that are identified in un-stimulated cells using ChIP-seq technology (Robertson et al., 2007)

Usage

annotatedPeak

Format

GRanges with slot start holding the start position of the peak, slot end holding the end position of the peak, slot names holding the id of the peak, slot strand holding the strands and slot space holding the chromosome location where the peak is located. In addition, the following variables are included.

list("feature")

id of the feature such as ensembl gene ID

list("insideFeature")

upstream: peak resides upstream of the feature; downstream: peak resides downstream of the feature; inside: peak resides inside the feature; overlapStart: peak overlaps with the start of the feature; overlapEnd: peak overlaps with the end of the feature; includeFeature: peak include the feature entirely

list("distancetoFeature")

distance to the nearest feature such as transcription start site

list("start_position")

start position of the feature such as gene

list("end_position")

end position of the feature such as the gene

Details

obtained by data(TSS.human.GRCh37)

data(myPeakList)

annotatePeakInBatch(myPeakList, AnnotationData = TSS.human.GRCh37, output="b", multiple=F)

Examples

data(annotatedPeak)
head(annotatedPeak, 4)  # show first 4 ranges
if (interactive() || Sys.getenv("USER")=="jianhongou") {
y = annotatedPeak$distancetoFeature[!is.na(annotatedPeak$distancetoFeature)]
hist(as.numeric(as.character(y)), 
     xlab="Distance To Nearest TSS", main="", breaks=1000, 
ylim=c(0, 50), xlim=c(min(as.numeric(as.character(y)))-100, 
max(as.numeric(as.character(y)))+100))
}

Obtain the distance to the nearest TSS, miRNA, and/or exon for a list of peaks

Description

Obtain the distance to the nearest TSS, miRNA, exon et al for a list of peak locations leveraging IRanges and biomaRt package

Usage

annotatePeakInBatch(
  myPeakList,
  mart,
  featureType = c("TSS", "miRNA", "Exon"),
  AnnotationData,
  output = c("nearestLocation", "overlapping", "both", "shortestDistance", "inside",
    "upstream&inside", "inside&downstream", "upstream", "downstream",
    "upstreamORdownstream", "nearestBiDirectionalPromoters"),
  multiple = c(TRUE, FALSE),
  maxgap = -1L,
  PeakLocForDistance = c("start", "middle", "end", "endMinusStart"),
  FeatureLocForDistance = c("TSS", "middle", "start", "end", "geneEnd"),
  select = c("all", "first", "last", "arbitrary"),
  ignore.strand = TRUE,
  bindingRegion = NULL,
  ...
)

Arguments

myPeakList

A GRanges object
mart

A mart object, used if AnnotationData is not supplied, see useMart of bioMaRt package for details
featureType

A charcter vector used with mart argument if AnnotationData is not supplied; choose from "TSS", "miRNA" or "Exon"
AnnotationData

A GRanges or annoGR object. It can be obtained from the function getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). Pre-compliled annotations, such as TSS.human.NCBI36, TSS.mouse.NCBIM37, TSS.rat.RGSC3.4 and TSS.zebrafish.Zv8, are provided by this package (attach them with data() function). Another method to provide annotation data is to obtain through biomaRt in real time by using the mart and featureType option
output
nearestLocation (default)

will output the nearest features calculated as PeakLoc - FeatureLocForDistance; when selected, the output can consist of both "strictly nearest features (non-overlapping)" and "overlapping features" as long as they are the nearest

overlapping

will output overlapping features with maximum gap specified as maxgap between peak range and feature range; it is possible for a peak to be annotated with zero ("NA" will be returned) or multiple overlapping features if exist

both

will output all the nearest features as well as any features that overlap with the peak that is not the nearest

shortestDistance

will output the features with the shortest distance; the "shortest distance" is determined from either ends of the feature to either ends of the peak

upstream&inside

will output all upstream and overlapping features with maximum gap

inside&downstream

will output all downstream and overlapping features with maximum gap

upstream

will output all upstream features with maximum gap

downstream

will output all downstream features with maximum gap

upstreamORdownstream

will output all upstream features with maximum gap or downstream with maximum gap

nearestBiDirectionalPromoters

will use annoPeaks to annotate peaks. Nearest promoters from both direction of the peaks (strand is considered). It will report bidirectional promoters if there are promoters in both directions in the given region (defined by bindingRegion). Otherwise, it will report the closest promoter in one direction.
multiple

Not applicable when output is nearest. TRUE: output multiple overlapping features for each peak. FALSE: output at most one overlapping feature for each peak. This parameter is kept for backward compatibility, please use select.
maxgap

The maximum gap that is allowed between 2 ranges for the ranges to be considered as overlapping. The gap between 2 ranges is the number of positions that separate them. The gap between 2 adjacent ranges is 0. By convention when one range has its start or end strictly inside the other (i.e. non-disjoint ranges), the gap is considered to be -1.
PeakLocForDistance

Specify the location of peak for calculating distance,i.e., middle means using middle of the peak to calculate distance to feature, start means using start of the peak to calculate the distance to feature, endMinusStart means using the end of the peak to calculate the distance to features on plus strand and the start of the peak to calculate the distance to features on minus strand. To be compatible with previous version, by default using start
FeatureLocForDistance

Specify the location of feature for calculating distance,i.e., middle means using middle of the feature to calculate distance of peak to feature, start means using start of the feature to calculate the distance to feature, TSS means using start of feature when feature is on plus strand and using end of feature when feature is on minus strand, geneEnd means using end of feature when feature is on plus strand and using start of feature when feature is on minus strand. To be compatible with previous version, by default using TSS
select

"all" may return multiple overlapping peaks, "first" will return the first overlapping peak, "last" will return the last overlapping peak and "arbitrary" will return one of the overlapping peaks.
ignore.strand

When set to TRUE, the strand information is ignored in the annotation. Unless you have stranded peaks and you are interested in annotating peaks to the features in the same strand only, you should just use the default setting ignore.strand = TRUE.
bindingRegion

Annotation range used for annoPeaks, which is a vector with two integer values, default to c (-5000, 5000). The first one must be no bigger than 0. And the sec ond one must be no less than 1. Once bindingRegion is defined, annotation will based on annoPeaks. Here is how to use it together with the parameter output and FeatureLocForDistance.

  • To obtain peaks with nearest bi-directional promoters within 5kb upstream and 3kb downstream of TSS, set output = "nearestBiDirectionalPromoters" and bindingRegion = c(-5000, 3000)

  • To obtain peaks within 5kb upstream and up to 3kb downstream of TSS within the gene body, set output="overlapping", FeatureLocForDistance="TSS" and bindingRegion = c(-5000, 3000)

  • To obtain peaks up to 5kb upstream within the gene body and 3kb downstream of gene/Exon End, set output="overlapping", FeatureLocForDistance="geneEnd" and bindingRegion = c(-5000, 3000)

  • To obtain peaks from 5kb upstream to 3kb downstream of genes/Exons, set output="overlapping", bindingType = "fullRange" and bindingRegion = c(-5000, 3000)

For details, see annoPeaks.
...

Parameters could be passed to annoPeaks

Value

An object of GRanges with slot start holding the start position of the peak, slot end holding the end position of the peak, slot space holding the chromosome location where the peak is located, slot rownames holding the id of the peak. In addition, the following variables are included.

list("feature")

id of the feature such as ensembl gene ID
list("insideFeature")

upstream: peak resides upstream of the feature; downstream: peak resides downstream of the feature; inside: peak resides inside the feature; overlapStart: peak overlaps with the start of the feature; overlapEnd: peak overlaps with the end of the feature; includeFeature: peak include the feature entirely
list("distancetoFeature")

distance to the nearest feature such as transcription start site. By default, the distance is calculated as the distance between the start of the binding site and the TSS that is the gene start for genes located on the forward strand and the gene end for genes located on the reverse strand. The user can specify the location of peak and location of feature for calculating this
list("start_position")

start position of the feature such as gene
list("end_position")

end position of the feature such as the gene
list("strand")

1 or + for positive strand and -1 or - for negative strand where the feature is located
list("shortestDistance")

The shortest distance from either end of peak to either end the feature.

list("fromOverlappingOrNearest")

Relevant only when output is set to "both". If "nearestLocation": indicates this feature's start (feature's end for features from minus strand) is the closest to the peak start ("strictly nearest" or "nearest overlapping"); if "Overlapping": indicates this feature overlaps with this peak although it is not the nearest (non-nearest overlapping)

Author(s)

Lihua Julie Zhu, Jianhong Ou

References

1. Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237

2. Zhu L (2013). "Integrative analysis of ChIP-chip and ChIP-seq dataset." In Lee T and Luk ACS (eds.), Tilling Arrays, volume 1067, chapter 4, pp. -19. Humana Press. http://dx.doi.org/10.1007/978-1-62703-607-8_8

See Also

getAnnotation, findOverlappingPeaks, makeVennDiagram, addGeneIDs, peaksNearBDP, summarizePatternInPeaks, annoGR, annoPeaks

Examples

## example 1: annotate myPeakList by TxDb or EnsDb.
    data(myPeakList)
    library(ensembldb)
    library(EnsDb.Hsapiens.v75)
    annoData <- annoGR(EnsDb.Hsapiens.v75)
    annotatePeak = annotatePeakInBatch(myPeakList[1:6], AnnotationData=annoData)
    annotatePeak
    
    ## example 2: annotate myPeakList (GRanges) 
    ## with TSS.human.NCBI36 (Granges)
    data(TSS.human.NCBI36)
    annotatedPeak = annotatePeakInBatch(myPeakList[1:6], 
                                        AnnotationData=TSS.human.NCBI36)
    annotatedPeak
    
    ## example 3: you have a list of transcription factor biding sites from 
    ## literature and are interested in determining the extent of the overlap 
    ## to the list of peaks from your experiment. Prior calling the function 
    ## annotatePeakInBatch, need to represent both dataset as GRanges 
    ## where start is the start of the binding site, end is the end of the 
    ## binding site, names is the name of the binding site, space and strand 
    ## are the chromosome name and strand where the binding site is located.
    
    myexp <- GRanges(seqnames=c(6,6,6,6,5,4,4), 
                     IRanges(start=c(1543200,1557200,1563000,1569800,
                                     167889600,100,1000),
                             end=c(1555199,1560599,1565199,1573799,
                                   167893599,200,1200),
                             names=c("p1","p2","p3","p4","p5","p6", "p7")), 
                     strand="+")
    literature <- GRanges(seqnames=c(6,6,6,6,5,4,4), 
                          IRanges(start=c(1549800,1554400,1565000,1569400,
                                          167888600,120,800),
                                  end=c(1550599,1560799,1565399,1571199,
                                        167888999,140,1400),
                                  names=c("f1","f2","f3","f4","f5","f6","f7")),
                          strand=rep(c("+", "-"), c(5, 2)))
    annotatedPeak1 <- annotatePeakInBatch(myexp, 
                                          AnnotationData=literature)
    pie(table(annotatedPeak1$insideFeature))
    annotatedPeak1
    ### use toGRanges or rtracklayer::import to convert BED or GFF format
    ###  to GRanges before calling annotatePeakInBatch
    test.bed <- data.frame(space=c("4", "6"), 
                           start=c("100", "1000"),
                           end=c("200", "1100"), 
                           name=c("peak1", "peak2"))
    test.GR = toGRanges(test.bed)
    annotatePeakInBatch(test.GR, AnnotationData = literature)
 
 library(testthat)
  peak <- GRanges(seqnames = "chr1", 
                  IRanges(start = 24736757, end=24737528,
                          names = "testPeak"))
  data(TSS.human.GRCh37)
  TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"]
  # GRanges object with 1 range and 1 metadata column:
  # seqnames            ranges strand |            description
  #<Rle>         <IRanges>  <Rle> |            <character>
  # ENSG00000001461        1 24742285-24799466      + | NIPA-like domain con..
  peak
  #GRanges object with 1 range and 0 metadata columns:
  #   seqnames            ranges strand
  #<Rle>         <IRanges>  <Rle>
  #  testPeak     chr1 24736757-24737528      *
  TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"]
  #GRanges object with 1 range and 1 metadata column:
  #   seqnames            ranges strand |            description
  #<Rle>         <IRanges>  <Rle> |            <character>
  #   ENSG00000001460        1 24683490-24743424      - | UPF0490 protein C1or..
  ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37, 
                            PeakLocForDistance = "start")
  stopifnot(ap$feature=="ENSG00000001461")
  ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
                            PeakLocForDistance = "end")
  stopifnot(ap$feature=="ENSG00000001461")
  ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
                            PeakLocForDistance = "middle")
  stopifnot(ap$feature=="ENSG00000001461")
  ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
                            PeakLocForDistance = "endMinusStart")
  stopifnot(ap$feature=="ENSG00000001461")
  ## Let's calculate the distances between the peak and the TSS of the genes
  ## in the annotation file used for annotating the peaks.
  ## Please note that we need to compute the distance using the annotation
  ## file TSS.human.GRCh37.
  ## If you would like to use  TxDb.Hsapiens.UCSC.hg19.knownGene, 
  ## then you will need to annotate the peaks
  ## using TxDb.Hsapiens.UCSC.hg19.knownGene as well.
  ### using start
  start(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)== 
                                      "ENSG00000001461"]) #picked
  #[1] -5528
  start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                   "ENSG00000001460"])
  #[1] -6667
  #### using middle
  (start(peak) + end(peak))/2 -
      start(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"])
  #[1] -5142.5
  (start(peak) + end(peak))/2 -
      end(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"])
  # [1] 49480566
  end(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                   "ENSG00000001461"]) #picked
  # [1] -4757
  end(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                 "ENSG00000001460"])
  # [1] -5896
  #### using endMinusStart
  end(peak) - start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                    "ENSG00000001461"]) ## picked
  # [1] -4575
  start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                    "ENSG00000001460"])
  #[1] -6667
  ###### using txdb object to annotate the peaks
  library(org.Hs.eg.db)
  select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
         columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
  #  SYMBOL         ENSEMBL ENTREZID
  #  STPG1 ENSG00000001460    90529
  select(org.Hs.eg.db, key= "ENSG00000001461", keytype="ENSEMBL",
         columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
  #ENSEMBL ENTREZID SYMBOL
  # ENSG00000001461    57185 NIPAL3
  require(TxDb.Hsapiens.UCSC.hg19.knownGene)
  txdb.ann <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
  STPG1 <- select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
                  columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
  NIPAL3 <- select(org.Hs.eg.db, key="NIPAL3", keytype="SYMBOL",
                   columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
  ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                            PeakLocForDistance = "start")
  expect_equal(ap$feature, STPG1)
  ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                            PeakLocForDistance = "end")
  expect_equal(ap$feature, STPG1)
  ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                            PeakLocForDistance = "middle")
  expect_equal(ap$feature, STPG1)
  ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                            PeakLocForDistance = "endMinusStart")
  expect_equal(ap$feature, NIPAL3)
  txdb.ann[NIPAL3]
  txdb.ann[txdb.ann$gene_id == NIPAL3]
  #  GRanges object with 1 range and 1 metadata column:
  #    seqnames            ranges strand |     gene_id
  #  <Rle>         <IRanges>  <Rle> | <character>
  #   57185     chr1 24742245-24799473      + |       57185
  #-------
  txdb.ann[txdb.ann$gene_id == STPG1]
  #   GRanges object with 1 range and 1 metadata column:
  #     seqnames            ranges strand |     gene_id
  #  <Rle>         <IRanges>  <Rle> | <character>
  #     90529     chr1 24683489-24741587      - |       90529

Summarize peak distribution over exon, intron, enhancer, proximal promoter, 5 prime UTR and 3 prime UTR

Description

Summarize peak distribution over exon, intron, enhancer, proximal promoter, 5 prime UTR and 3 prime UTR

Usage

assignChromosomeRegion(
  peaks.RD,
  exon,
  TSS,
  utr5,
  utr3,
  proximal.promoter.cutoff = c(upstream = 2000, downstream = 100),
  immediate.downstream.cutoff = c(upstream = 0, downstream = 1000),
  nucleotideLevel = FALSE,
  precedence = NULL,
  TxDb = NULL
)

Arguments

peaks.RD

peaks in GRanges: See example below
exon

exon data obtained from getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). This parameter is for backward compatibility only. TxDb should be used instead.
TSS

TSS data obtained from getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). For example, data(TSS.human.NCBI36),data(TSS.mouse.NCBIM37), data(TSS.rat.RGSC3.4) and data(TSS.zebrafish.Zv8). This parameter is for backward compatibility only. TxDb should be used instead.
utr5

5 prime UTR data obtained from getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). This parameter is for backward compatibility only. TxDb should be used instead.
utr3

3 prime UTR data obtained from getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). This parameter is for backward compatibility only. TxDb should be used instead.
proximal.promoter.cutoff

Specify the cutoff in bases to classify proximal promoter or enhencer. Peaks that reside within proximal.promoter.cutoff upstream from or overlap with transcription start site are classified as proximal promoters. Peaks that reside upstream of the proximal.promoter.cutoff from gene start are classified as enhancers. The default is upstream 2000 bases and downstream 100 bases.
immediate.downstream.cutoff

Specify the cutoff in bases to classify immediate downstream region or enhancer region. Peaks that reside within immediate.downstream.cutoff downstream of gene end but not overlap 3 prime UTR are classified as immediate downstream. Peaks that reside downstream over immediate.downstreatm.cutoff from gene end are classified as enhancers. The default is upstream 0 bases and downstream 1000 bases.
nucleotideLevel

Logical. Choose between peak centric and nucleotide centric view. Default=FALSE
precedence

If no precedence specified, double count will be enabled, which means that if a peak overlap with both promoter and 5'UTR, both promoter and 5'UTR will be incremented. If a precedence order is specified, for example, if promoter is specified before 5'UTR, then only promoter will be incremented for the same example. The values could be any conbinations of "Promoters", "immediateDownstream", "fiveUTRs", "threeUTRs", "Exons" and "Introns", Default=NULL
TxDb

an object of TxDb or similar including EnsDb

Value

A list of two named vectors: percentage and jaccard (Jaccard Index). The information in the vectors:

list("Exons")

Percent or the picard index of the peaks resided in exon regions.
list("Introns")

Percent or the picard index of the peaks resided in intron regions.
list("fiveUTRs")

Percent or the picard index of the peaks resided in 5 prime UTR regions.
list("threeUTRs")

Percent or the picard index of the peaks resided in 3 prime UTR regions.
list("Promoter")

Percent or the picard index of the peaks resided in proximal promoter regions.
list("ImmediateDownstream")

Percent or the picard index of the peaks resided in immediate downstream regions.
list("Intergenic.Region")

Percent or the picard index of the peaks resided in intergenic regions.

The Jaccard index, also known as Intersection over Union. The Jaccard index is between 0 and 1. The higher the index, the more significant the overlap between the peak region and the genomic features in consideration.

Author(s)

Jianhong Ou, Lihua Julie Zhu

References

1. Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237

2. Zhu L.J. (2013) Integrative analysis of ChIP-chip and ChIP-seq dataset. Methods Mol Biol. 2013;1067:105-24. doi: 10.1007/978-1-62703-607-8_8.

See Also

genomicElementDistribution, genomicElementUpSetR, binOverFeature, binOverGene, binOverRegions

Examples

if (interactive() || Sys.getenv("USER")=="jianhongou"){
    ##Display the list of genomes available at UCSC:
    #library(rtracklayer)
    #ucscGenomes()[, "db"]
    ## Display the list of Tracks supported by makeTxDbFromUCSC()
    #supportedUCSCtables()
    ##Retrieving a full transcript dataset for Human from UCSC
    ##TranscriptDb <- 
    ##     makeTxDbFromUCSC(genome="hg19", tablename="ensGene")
    if(require(TxDb.Hsapiens.UCSC.hg19.knownGene)){
      TxDb <- TxDb.Hsapiens.UCSC.hg19.knownGene
      exons <- exons(TxDb, columns=NULL)
      fiveUTRs <- unique(unlist(fiveUTRsByTranscript(TxDb)))
      Feature.distribution <- 
          assignChromosomeRegion(exons, nucleotideLevel=TRUE, TxDb=TxDb)
      barplot(Feature.distribution$percentage)
      assignChromosomeRegion(fiveUTRs, nucleotideLevel=FALSE, TxDb=TxDb)
      data(myPeakList)
      assignChromosomeRegion(myPeakList, nucleotideLevel=TRUE, 
                             precedence=c("Promoters", "immediateDownstream", 
                                          "fiveUTRs", "threeUTRs", 
                                          "Exons", "Introns"), 
                             TxDb=TxDb)
    }
}

obtain the peaks near bi-directional promoters

Description

Obtain the peaks near bi-directional promoters. Also output percent of peaks near bi-directional promoters.

Usage

bdp(peaks, annoData, maxgap = 2000L, ...)

Arguments

peaks

peak list, GRanges object
annoData

annotation data, annoGR object
maxgap

maxgap between peak and TSS
...

Not used.

Value

Output is a list of GRanges object of the peaks near bi-directional promoters.

Author(s)

Jianhong Ou

See Also

See Also as annoPeaks, annoGR

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
    library(ensembldb)
    library(EnsDb.Hsapiens.v75)
    data("myPeakList")
    annoGR <- annoGR(EnsDb.Hsapiens.v75)
    seqlevelsStyle(myPeakList) <- seqlevelsStyle(annoGR)
    ChIPpeakAnno:::bdp(myPeakList, annoGR)
  }

Class "bindist"

Description

An object of class "bindist" represents the relevant fixed-width range of binding site from the feature and number of possible binding site in each range.

Objects from the Class

Objects can be created by calls of the form new("bindist", counts="integer", mids="integer", halfBinSize="integer", bindingType="character", featureType="character").

See Also

preparePool, peakPermTest

Aggregate peaks over bins from the TSS

Description

Aggregate peaks over bins from the feature sites.

Usage

binOverFeature(
  ...,
  annotationData = GRanges(),
  select = c("all", "nearest"),
  radius = 5000L,
  nbins = 50L,
  minGeneLen = 1L,
  aroundGene = FALSE,
  mbins = nbins,
  featureSite = c("FeatureStart", "FeatureEnd", "bothEnd"),
  PeakLocForDistance = c("all", "end", "start", "middle"),
  FUN = sum,
  errFun = sd,
  xlab,
  ylab,
  main
)

Arguments

...

Objects of GRanges to be analyzed
annotationData

An object of GRanges or annoGR for annotation
select

Logical: annotate the peaks to all features or the nearest one
radius

The radius of the longest distance to feature site
nbins

The number of bins
minGeneLen

The minimal gene length
aroundGene

Logical: count peaks around features or a given site of the features. Default = FALSE
mbins

if aroundGene set as TRUE, the number of bins intra-feature. The value will be normalized by value * (radius/genelen) * (mbins/nbins)
featureSite

which site of features should be used for distance calculation
PeakLocForDistance

which site of peaks should be used for distance calculation
FUN

the function to be used for score calculation
errFun

the function to be used for errorbar calculation or values for the errorbar.
xlab

titles for each x axis
ylab

titles for each y axis
main

overall titles for each plot

Value

A data.frame with bin values.

Author(s)

Jianhong Ou

Examples

bed <- system.file("extdata", "MACS_output.bed", package="ChIPpeakAnno")
gr1 <- toGRanges(bed, format="BED", header=FALSE)
data(TSS.human.GRCh37)
binOverFeature(gr1, annotationData=TSS.human.GRCh37,
               radius=5000, nbins=10, FUN=length, errFun=0)

coverage of gene body

Description

calculate the coverage of gene body per gene per bin.

Usage

binOverGene(
  cvglists,
  TxDb,
  upstream.cutoff = 0L,
  downstream.cutoff = upstream.cutoff,
  nbinsGene = 100L,
  nbinsUpstream = 20L,
  nbinsDownstream = nbinsUpstream,
  includeIntron = FALSE,
  minGeneLen = nbinsGene,
  maxGeneLen = Inf
)

Arguments

cvglists

A list of SimpleRleList or RleList. It represents the coverage for samples.
TxDb

An object of TxDb. It is used for extracting the annotations.
upstream.cutoff, downstream.cutoff

cutoff length for upstream or downstream of transcript.
nbinsGene, nbinsUpstream, nbinsDownstream

The number of bins for gene, upstream and downstream.
includeIntron

A logical value which indicates including intron or not.
minGeneLen, maxGeneLen

minimal or maximal length of gene.

Author(s)

Jianhong Ou

See Also

binOverRegions, plotBinOverRegions

Examples

if(Sys.getenv("USER")=="jianhongou"){
path <- system.file("extdata", package="ChIPpeakAnno")
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(rtracklayer)
files <- dir(path, "bigWig")
if(.Platform$OS.type != "windows"){
cvglists <- lapply(file.path(path, files), import,
                   format="BigWig", as="RleList")
names(cvglists) <- sub(".bigWig", "", files)
d <- binOverGene(cvglists, TxDb.Hsapiens.UCSC.hg19.knownGene)
plotBinOverRegions(d)
}
}

coverage of chromosome regions

Description

calculate the coverage of 5'UTR, CDS and 3'UTR per transcript per bin.

Usage

binOverRegions(
  cvglists,
  TxDb,
  upstream.cutoff = 1000L,
  downstream.cutoff = upstream.cutoff,
  nbinsCDS = 100L,
  nbinsUTR = 20L,
  nbinsUpstream = 20L,
  nbinsDownstream = nbinsUpstream,
  includeIntron = FALSE,
  minCDSLen = nbinsCDS,
  minUTRLen = nbinsUTR,
  maxCDSLen = Inf,
  maxUTRLen = Inf
)

Arguments

cvglists

A list of SimpleRleList or RleList. It represents the coverage for samples.
TxDb

An object of TxDb. It is used for extracting the annotations.
upstream.cutoff, downstream.cutoff

cutoff length for upstream or downstream of transcript.
nbinsCDS, nbinsUTR, nbinsUpstream, nbinsDownstream

The number of bins for CDS, UTR, upstream and downstream.
includeIntron

A logical value which indicates including intron or not.
minCDSLen, minUTRLen

minimal length of CDS or UTR of transcript.
maxCDSLen, maxUTRLen

maximal length of CDS or UTR of transctipt.

Author(s)

Jianhong Ou

See Also

binOverGene, plotBinOverRegions

Examples

if(Sys.getenv("USER")=="jianhongou"){
path <- system.file("extdata", package="ChIPpeakAnno")
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(rtracklayer)
files <- dir(path, "bigWig")
if(.Platform$OS.type != "windows"){
cvglists <- lapply(file.path(path, files), import,
                   format="BigWig", as="RleList")
names(cvglists) <- sub(".bigWig", "", files)
d <- binOverRegions(cvglists, TxDb.Hsapiens.UCSC.hg19.knownGene)
plotBinOverRegions(d)
}
}

Deprecated Functions in Package ChIPpeakAnno

Description

These functions are provided for compatibility with older versions of R only, and may be defunct as soon as the next release.

Arguments

Peaks1

GRanges: See example below.
Peaks2

GRanges: See example below.
maxgap, minoverlap

Used in the internal call to findOverlaps() to detect overlaps. See ?findOverlaps in the IRanges package for a description of these arguments.
multiple

TRUE or FALSE: TRUE may return multiple overlapping peaks in Peaks2 for one peak in Peaks1; FALSE will return at most one overlapping peaks in Peaks2 for one peak in Peaks1. This parameter is kept for backward compatibility, please use select.
NameOfPeaks1

Name of the Peaks1, used for generating column name.
NameOfPeaks2

Name of the Peaks2, used for generating column name.
select

all may return multiple overlapping peaks, first will return the first overlapping peak, last will return the last overlapping peak and arbitrary will return one of the overlapping peaks.
annotate

Include overlapFeature and shortestDistance in the OverlappingPeaks or not. 1 means yes and 0 means no. Default to 0.
ignore.strand

When set to TRUE, the strand information is ignored in the overlap calculations.
connectedPeaks

If multiple peaks involved in overlapping in several groups, set it to "merge" will count it as only 1, while set it to "min" will count it as the minimal involved peaks in any concered groups
...

Objects of GRanges: See also findOverlapsOfPeaks.

Details

findOverlappingPeaks is now deprecated wrappers for findOverlapsOfPeaks

See Also

Deprecated, findOverlapsOfPeaks, toGRanges

count overlaps

Description

Count overlaps with max gap.

Usage

cntOverlaps(A, B, maxgap = 0L, ...)

Arguments

A, B

A GRanges object.
maxgap

A single integer >= 0.
...

parameters passed to numOverlaps#'

Condense matrix by colnames

Description

Condense matrix by colnames

Usage

condenseMatrixByColnames(mx, iname, sep = ";", cnt = FALSE)

Arguments

mx

a matrix to be condensed
iname

the name of the column to be condensed
sep

separator for condensed values,default ;
cnt

TRUE/FALSE specifying whether adding count column or not?

Value

dataframe of condensed matrix

Author(s)

Jianhong Ou, Lihua Julie Zhu

Examples

a<-matrix(c(rep(rep(1:5,2),2),rep(1:10,2)),ncol=4)
colnames(a)<-c("con.1","con.2","index.1","index.2")
condenseMatrixByColnames(a,"con.1")
condenseMatrixByColnames(a,2)

Convert other common IDs to entrez gene ID.

Description

Convert other common IDs such as ensemble gene id, gene symbol, refseq id to entrez gene ID leveraging organism annotation dataset. For example, org.Hs.eg.db is the dataset from orgs.Hs.eg.db package for human, while org.Mm.eg.db is the dataset from the org.Mm.eg.db package for mouse.

Usage

convert2EntrezID(IDs, orgAnn, ID_type = "ensembl_gene_id")

Arguments

IDs

a vector of IDs such as ensembl gene ids
orgAnn

organism annotation dataset such as org.Hs.eg.db
ID_type

type of ID: can be ensemble_gene_id, gene_symbol or refseq_id

Value

vector of entrez ids

Author(s)

Lihua Julie Zhu

Examples

ensemblIDs = c("ENSG00000115956", "ENSG00000071082", "ENSG00000071054",
 "ENSG00000115594", "ENSG00000115594", "ENSG00000115598", "ENSG00000170417")
library(org.Hs.eg.db)
entrezIDs = convert2EntrezID(IDs=ensemblIDs, orgAnn="org.Hs.eg.db",
 ID_type="ensembl_gene_id")

Output total number of patterns found in the input sequences

Description

Output total number of patterns found in the input sequences

Usage

countPatternInSeqs(pattern, sequences)

Arguments

pattern

DNAstringSet object
sequences

a vector of sequences

Value

Total number of occurrence of the pattern in the sequences

Author(s)

Lihua Julie Zhu

See Also

summarizePatternInPeaks, translatePattern

Examples

library(Biostrings)
  filepath = 
    system.file("extdata", "examplePattern.fa", package="ChIPpeakAnno")
  dict = readDNAStringSet(filepath = filepath, format="fasta", 
                          use.names=TRUE)
  sequences = c("ACTGGGGGGGGCCTGGGCCCCCAAAT", 
                "AAAAAACCCCTTTTGGCCATCCCGGGACGGGCCCAT", 
                "ATCGAAAATTTCC")
  countPatternInSeqs(pattern=dict[1], sequences=sequences)
  countPatternInSeqs(pattern=dict[2], sequences=sequences)
  pattern = DNAStringSet("ATNGMAA")
  countPatternInSeqs(pattern=pattern, sequences=sequences)

Plot the cumulative percentage tag allocation in sample

Description

Plot the difference between the cumulative percentage tag allocation in paired samples.

Usage

cumulativePercentage(bamfiles, gr, input = 1, binWidth = 1000, ...)

Arguments

bamfiles

Bam file names.
gr

An object of GRanges
input

Which file name is input. default 1.
binWidth

The width of each bin.
...

parameter for summarizeOverlaps.

Value

A list of data.frame with the cumulative percentages.

Author(s)

Jianhong Ou

References

Normalization, bias correction, and peak calling for ChIP-seq Aaron Diaz, Kiyoub Park, Daniel A. Lim, Jun S. Song Stat Appl Genet Mol Biol. Author manuscript; available in PMC 2012 May 3.Published in final edited form as: Stat Appl Genet Mol Biol. 2012 Mar 31; 11(3): 10.1515/1544-6115.1750 /j/sagmb.2012.11.issue-3/1544-6115.1750/1544-6115.1750.xml. Published online 2012 Mar 31. doi: 10.1515/1544-6115.1750 PMCID: PMC3342857

Examples

## Not run: 
path <- system.file("extdata", "reads", package="MMDiffBamSubset")
files <- dir(path, "bam$", full.names = TRUE)
library(BSgenome.Hsapiens.UCSC.hg19)
gr <- as(seqinfo(Hsapiens)["chr1"], "GRanges")
cumulativePercentage(files, gr)

## End(Not run)

Get downstream coordinates

Description

Returns an object of the same type and length as x containing downstream ranges. The output range is defined as

Usage

downstreams(gr, upstream, downstream)

Arguments

gr

A GenomicRanges object
upstream, downstream

non-negative interges.

Details

(end(x) - upstream) to (end(x) + downstream -1)

for ranges on the + and * strand, and as

(start(x) - downstream + 1) to (start(x) + downstream)

for ranges on the - strand.

Note that the returned object might contain out-of-bound ranges.

Value

A GenomicRanges object

Examples

gr <- GRanges("chr1", IRanges(rep(10, 3), width=6), c("+", "-", "*"))
downstreams(gr, 2, 2)

Convert between the name of the organism annotation package ("OrgDb") and the name of the organism.

Description

Give a species name and return the organism annotation package name or give an organism annotation package name then return the species name.

Usage

egOrgMap(name)

Arguments

name

The name of the organism annotation package or the species.

Value

A object of character

Author(s)

Jianhong Ou

Examples

egOrgMap("org.Hs.eg.db")
  egOrgMap("Mus musculus")

Enriched Gene Ontology terms used as example

Description

Enriched Gene Ontology terms used as example

Usage

enrichedGO

Format

A list of 3 dataframes.

list("bp")

dataframe described the enriched biological process with 9 columns

go.id:GO biological process id

go.term:GO biological process term

go.Definition:GO biological process description

Ontology: Ontology branch, i.e. BP for biological process

count.InDataset: count of this GO term in this dataset

count.InGenome: count of this GO term in the genome

pvalue: pvalue from the hypergeometric test

totaltermInDataset: count of all GO terms in this dataset

totaltermInGenome: count of all GO terms in the genome

list("mf")

dataframe described the enriched molecular function with the following 9 columns

go.id:GO molecular function id

go.term:GO molecular function term

go.Definition:GO molecular function description

Ontology: Ontology branch, i.e. MF for molecular function

count.InDataset: count of this GO term in this dataset

count.InGenome: count of this GO term in the genome

pvalue: pvalue from the hypergeometric test

totaltermInDataset: count of all GO terms in this dataset

totaltermInGenome: count of all GO terms in the genome

list("cc")

dataframe described the enriched cellular component the following 9 columns

go.id:GO cellular component id

go.term:GO cellular component term

go.Definition:GO cellular component description

Ontology: Ontology type, i.e. CC for cellular component

count.InDataset: count of this GO term in this dataset

count.InGenome: count of this GO term in the genome

pvalue: pvalue from the hypergeometric test

totaltermInDataset: count of all GO terms in this dataset

totaltermInGenome: count of all GO terms in the genome

Author(s)

Lihua Julie Zhu

Examples

data(enrichedGO)
dim(enrichedGO$mf)
dim(enrichedGO$cc)
dim(enrichedGO$bp)

plot enrichment results

Description

Plot the GO/KEGG/reactome enrichment results

Usage

enrichmentPlot(
  res,
  n = 20,
  strlength = Inf,
  style = c("v", "h"),
  label_wrap = 40,
  label_substring_to_remove = NULL,
  orderBy = c("pvalue", "termId", "none")
)

Arguments

res

output of getEnrichedGO, getEnrichedPATH.
n

number of terms to be plot.
strlength

shorten the description of term by the number of char.
style

plot vertically or horizontally
label_wrap

soft wrap the labels (i.e. descriptions of the GO or PATHWAY terms), default to 40 characters.
label_substring_to_remove

remove common substring from label, default to NULL. Special characters must be escaped. E.g. if you would like to remove "Homo sapiens (human)" from labels, you must use "Homo sapiens \\( human\\)".
orderBy

order the data by pvalue, termId or none.

Value

an object of ggplot

Author(s)

Jianhong Ou, Kai Hu

Examples

data(enrichedGO)
enrichmentPlot(enrichedGO)
if (interactive()||Sys.getenv("USER")=="jianhongou") {
     
     library(org.Hs.eg.db)
     library(GO.db)
     bed <- system.file("extdata", "MACS_output.bed", package="ChIPpeakAnno")
     gr1 <- toGRanges(bed, format="BED", header=FALSE) 
     gff <- system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno")
     gr2 <- toGRanges(gff, format="GFF", header=FALSE, skip=3)
     library(EnsDb.Hsapiens.v75) ##(hg19)
     annoData <- toGRanges(EnsDb.Hsapiens.v75)
     gr1.anno <- annoPeaks(gr1, annoData)
     gr2.anno <- annoPeaks(gr2, annoData)
     over <- lapply(GRangesList(gr1=gr1.anno, gr2=gr2.anno), 
                    getEnrichedGO, orgAnn="org.Hs.eg.db",
                    maxP=.05, minGOterm=10, condense=TRUE)
     enrichmentPlot(over$gr1)
     enrichmentPlot(over$gr2, style = "h")
 }

EnsDb object to GRanges

Description

convert EnsDb object to GRanges

Usage

EnsDb2GR(ranges, feature)

Arguments

ranges

an EnsDb object
feature

feature type, could be disjointExons, gene, exon and transcript

estimate the fragment length

Description

estimate the fragment length for bam files

Usage

estFragmentLength(
  bamfiles,
  index = bamfiles,
  plot = TRUE,
  lag.max = 1000,
  minFragmentSize = 100,
  ...
)

Arguments

bamfiles

The file names of the 'BAM' ('SAM' for asBam) files to be processed.
index

The names of the index file of the 'BAM' file being processed; this is given without the '.bai' extension.
plot

logical. If TRUE (the default) the acf is plotted.
lag.max

maximum lag at which to calculate the acf. See acf
minFragmentSize

minimal fragment size to avoid the phantom peak.
...

Not used.

Value

numberic vector

Author(s)

Jianhong Ou

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
        path <- system.file("extdata", "reads", package="MMDiffBamSubset")
        if(file.exists(path)){
            WT.AB2 <- file.path(path, "WT_2.bam")
            Null.AB2 <- file.path(path, "Null_2.bam")
            Resc.AB2 <- file.path(path, "Resc_2.bam")
            estFragmentLength(c(WT.AB2, Null.AB2, Resc.AB2))
        }
    }

estimate the library size

Description

estimate the library size of bam files

Usage

estLibSize(bamfiles, index = bamfiles, ...)

Arguments

bamfiles

The file names of the 'BAM' ('SAM' for asBam) files to be processed.
index

The names of the index file of the 'BAM' file being processed; this is given without the '.bai' extension.
...

Not used.

Value

numberic vector

Author(s)

Jianhong Ou

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
        path <- system.file("extdata", "reads", package="MMDiffBamSubset")
        if(file.exists(path)){
            WT.AB2 <- file.path(path, "WT_2.bam")
            Null.AB2 <- file.path(path, "Null_2.bam")
            Resc.AB2 <- file.path(path, "Resc_2.bam")
            estLibSize(c(WT.AB2, Null.AB2, Resc.AB2))
        }
    }

Gene model with exon, 5' UTR and 3' UTR information for human sapiens (GRCh37) obtained from biomaRt

Description

Gene model with exon, 5' UTR and 3' UTR information for human sapiens (GRCh37) obtained from biomaRt

Usage

ExonPlusUtr.human.GRCh37

Format

GRanges with slot start holding the start position of the exon, slot end holding the end position of the exon, slot rownames holding ensembl transcript id and slot space holding the chromosome location where the gene is located. In addition, the following variables are included.

list("strand")

1 for positive strand and -1 for negative strand

list("description")

description of the transcript

list("ensembl_gene_id")

gene id

list("utr5start")

5' UTR start

list("utr5end")

5' UTR end

list("utr3start")

3' UTR start

list("utr3end")

3' UTR end

Details

used in the examples Annotation data obtained by: mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl") ExonPlusUtr.human.GRCh37 = getAnnotation(mart=human, featureType="ExonPlusUtr")

Examples

data(ExonPlusUtr.human.GRCh37)
slotNames(ExonPlusUtr.human.GRCh37)

plot distribution in given ranges

Description

plot distribution in the given feature ranges

Usage

featureAlignedDistribution(
  cvglists,
  feature.gr,
  upstream,
  downstream,
  n.tile = 100,
  zeroAt,
  ...
)

Arguments

cvglists

Output of featureAlignedSignal or a list of SimpleRleList or RleList
feature.gr

An object of GRanges with identical width. If the width equal to 1, you can use upstream and downstream to set the range for plot. If the width not equal to 1, you can use zeroAt to set the zero point of the heatmap.
upstream, downstream

upstream or dwonstream from the feature.gr.
n.tile

The number of tiles to generate for each element of feature.gr, default is 100
zeroAt

zero point position of feature.gr
...

any paramters could be used by matplot

Value

invisible matrix of the plot.

Author(s)

Jianhong Ou

See Also

See Also as featureAlignedSignal, featureAlignedHeatmap

Examples

cvglists <- list(A=RleList(chr1=Rle(sample.int(5000, 100), 
                                      sample.int(300, 100))), 
                   B=RleList(chr1=Rle(sample.int(5000, 100), 
                                      sample.int(300, 100))))
  feature.gr <- GRanges("chr1", IRanges(seq(1, 4900, 100), width=100))
  featureAlignedDistribution(cvglists, feature.gr, zeroAt=50, type="l")

extract signals in given ranges from bam files

Description

extract signals in the given feature ranges from bam files (DNAseq only). The reads will be extended to estimated fragement length.

Usage

featureAlignedExtendSignal(
  bamfiles,
  index = bamfiles,
  feature.gr,
  upstream,
  downstream,
  n.tile = 100,
  fragmentLength,
  librarySize,
  pe = c("auto", "PE", "SE"),
  adjustFragmentLength,
  gal,
  ...
)

Arguments

bamfiles

The file names of the 'BAM' ('SAM' for asBam) files to be processed.
index

The names of the index file of the 'BAM' file being processed; this is given without the '.bai' extension.
feature.gr

An object of GRanges with identical width.
upstream, downstream

upstream or dwonstream from the feature.gr.
n.tile

The number of tiles to generate for each element of feature.gr, default is 100
fragmentLength

Estimated fragment length.
librarySize

Estimated library size.
pe

Pair-end or not. Default auto.
adjustFragmentLength

A numberic vector with length 1. Adjust the fragments/reads length to.
gal

A GAlignmentsList object or a list of GAlignmentPairs. If bamfiles is missing, gal is required.
...

Not used.

Value

A list of matrix. In each matrix, each row record the signals for corresponding feature.

Author(s)

Jianhong Ou

See Also

See Also as featureAlignedSignal, estLibSize, estFragmentLength

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
    path <- system.file("extdata", package="MMDiffBamSubset")
    if(file.exists(path)){
        WT.AB2 <- file.path(path, "reads", "WT_2.bam")
        Null.AB2 <- file.path(path, "reads", "Null_2.bam")
        Resc.AB2 <- file.path(path, "reads", "Resc_2.bam")
        peaks <- file.path(path, "peaks", "WT_2_Macs_peaks.xls")
        estLibSize(c(WT.AB2, Null.AB2, Resc.AB2))
        feature.gr <- toGRanges(peaks, format="MACS")
        feature.gr <- feature.gr[seqnames(feature.gr)=="chr1" & 
                             start(feature.gr)>3000000 & 
                             end(feature.gr)<75000000]
        sig <- featureAlignedExtendSignal(c(WT.AB2, Null.AB2, Resc.AB2), 
                               feature.gr=reCenterPeaks(feature.gr, width=1), 
                               upstream = 505,
                               downstream = 505,
                               n.tile=101, 
                               fragmentLength=250,
                               librarySize=1e9)
        featureAlignedHeatmap(sig, reCenterPeaks(feature.gr, width=1010), 
                          zeroAt=.5, n.tile=101)
    }
 }

Heatmap representing signals in given ranges

Description

plot heatmap in the given feature ranges

Usage

featureAlignedHeatmap(
  cvglists,
  feature.gr,
  upstream,
  downstream,
  zeroAt,
  n.tile = 100,
  annoMcols = c(),
  sortBy = names(cvglists)[1],
  color = colorRampPalette(c("yellow", "red"))(50),
  lower.extreme,
  upper.extreme,
  margin = c(0.1, 0.01, 0.15, 0.1),
  gap = 0.01,
  newpage = TRUE,
  gp = gpar(fontsize = 10),
  ...
)

Arguments

cvglists

Output of featureAlignedSignal or a list of SimpleRleList or RleList
feature.gr

An object of GRanges with identical width. If the width equal to 1, you can use upstream and downstream to set the range for plot. If the width not equal to 1, you can use zeroAt to set the zero point of the heatmap.
upstream, downstream

upstream or dwonstream from the feature.gr. It must keep same as featureAlignedSignal. It is used for x-axis label.
zeroAt

zero point position of feature.gr
n.tile

The number of tiles to generate for each element of feature.gr, default is 100
annoMcols

The columns of metadata of feature.gr that specifies the annotations shown of the right side of the heatmap.
sortBy

Sort the feature.gr by columns by annoMcols and then the signals of the given samples. Default is the first sample. Set to NULL to disable sort.
color

vector of colors used in heatmap
lower.extreme, upper.extreme

The lower and upper boundary value of each samples
margin

Margin for of the plot region.
gap

Gap between each heatmap columns.
newpage

Call grid.newpage or not. Default, TRUE
gp

A gpar object can be used for text.
...

Not used.

Value

invisible gList object.

Author(s)

Jianhong Ou

See Also

See Also as featureAlignedSignal, featureAlignedDistribution

Examples

cvglists <- list(A=RleList(chr1=Rle(sample.int(5000, 100), 
                                      sample.int(300, 100))), 
                   B=RleList(chr1=Rle(sample.int(5000, 100), 
                                      sample.int(300, 100))))
  feature.gr <- GRanges("chr1", IRanges(seq(1, 4900, 100), width=100))
  feature.gr$anno <- rep(c("type1", "type2"), c(25, 24))
  featureAlignedHeatmap(cvglists, feature.gr, zeroAt=50, annoMcols="anno")

extract signals in given ranges

Description

extract signals in the given feature ranges

Usage

featureAlignedSignal(
  cvglists,
  feature.gr,
  upstream,
  downstream,
  n.tile = 100,
  ...
)

Arguments

cvglists

List of SimpleRleList or RleList
feature.gr

An object of GRanges with identical width.
upstream, downstream

Set the feature.gr to upstream and dwonstream from the center of the feature.gr if they are set.
n.tile

The number of tiles to generate for each element of feature.gr, default is 100
...

Not used.

Value

A list of matrix. In each matrix, each row record the signals for corresponding feature. rownames of the matrix show the seqnames and coordinates.

Author(s)

Jianhong Ou

See Also

See Also as featureAlignedHeatmap, featureAlignedDistribution

Examples

cvglists <- list(A=RleList(chr1=Rle(sample.int(5000, 100), 
                                      sample.int(300, 100))), 
                   B=RleList(chr1=Rle(sample.int(5000, 100), 
                                      sample.int(300, 100))))
  feature.gr <- GRanges("chr1", IRanges(seq(1, 4900, 100), width=100))
  featureAlignedSignal(cvglists, feature.gr)

Find possible enhancers depend on DNA interaction data

Description

Find possible enhancers by data from chromosome conformation capture techniques such as 3C, 5C or HiC.

Usage

findEnhancers(
  peaks,
  annoData,
  DNAinteractiveData,
  bindingType = c("nearestBiDirectionalPromoters", "startSite", "endSite"),
  bindingRegion = c(-5000, 5000),
  ignore.peak.strand = TRUE,
  ...
)

Arguments

peaks

peak list, GRanges object
annoData

annotation data, GRanges object
DNAinteractiveData

DNA interaction data, GRanges object with interaction blocks informations, GInteractions object, or BEDPE file which could be imported by importGInteractions or BiocIO::import or assembly in following list: hg38, hg19, mm10, danRer10, danRer11.
bindingType

Specifying the criteria to associate peaks with annotation. Here is how to use it together with the parameter bindingRegion. The annotation will be shift to a new position depend on the DNA interaction region.

  • To obtain peaks within 5kb upstream and up to 3kb downstream of shift TSS within the gene body, set bindingType = "startSite" and bindingRegion = c(-5000, 3000)

  • To obtain peaks up to 5kb upstream within the gene body and 3kb downstream of shift gene/Exon End, set bindingType = "endSite" and bindingRegion = c(-5000, 3000)

  • To obtain peaks with nearest bi-directional enhancer regions within 5kb upstream and 3kb downstream of shift TSS, set bindingType = "nearestBiDirectionalPromoters" and bindingRegion = c(-5000, 3000)

startSite

start position of the feature (strand is considered)

endSite

end position of the feature (strand is considered)

nearestBiDirectionalPromoters

nearest enhancer regions from both direction of the peaks (strand is considered). It will report bidirectional enhancer regions if there are enhancer regions in both directions in the given region (defined by bindingRegion). Otherwise, it will report the closest enhancer regions in one direction.
bindingRegion

Annotation range used together with bindingType, which is a vector with two integer values, default to c (-5000, 5000). The first one must be no bigger than 0. And the sec ond one must be no less than 1. For details, see bindingType.
ignore.peak.strand

ignore the peaks strand or not.
...

Not used.

Value

Output is a GRanges object of the annotated peaks.

Author(s)

Jianhong Ou

See Also

See Also as annotatePeakInBatch

Examples

bed <- system.file("extdata", 
                     "wgEncodeUmassDekker5CGm12878PkV2.bed.gz",
                     package="ChIPpeakAnno")
  DNAinteractiveData <- toGRanges(gzfile(bed))
  library(EnsDb.Hsapiens.v75)
  annoData <- toGRanges(EnsDb.Hsapiens.v75, feature="gene")
  data("myPeakList")
  findEnhancers(myPeakList[500:1000], annoData, DNAinteractiveData)

Find occurence of input motifs in the promoter regions of the input gene list

Description

Find occurence of input motifs in the promoter regions of the input gene list

Usage

findMotifsInPromoterSeqs(
  patternFilePath1,
  patternFilePath2,
  findPairedMotif = FALSE,
  BSgenomeName,
  txdb,
  geneIDs,
  upstream = 5000L,
  downstream = 5000L,
  name.motif1 = "motif1",
  name.motif2 = "motif2",
  max.distance = 100L,
  min.distance = 1L,
  motif.orientation = c("both", "motif1UpstreamOfMotif2", "motif2UpstreamOfMoif1"),
  ignore.strand = FALSE,
  format = "fasta",
  skip = 0L,
  motif1LocForDistance = "end",
  motif2LocForDistance = "start",
  outfile,
  append = FALSE
)

Arguments

patternFilePath1

File path containing a list of known motifs. Required
patternFilePath2

File path containing a motif requried to be in the flanking regions of the motif(s) in the first file, i.e, patternFilePath1. Requried if findPairedMotif is set to TRUE
findPairedMotif

Find motifs in paired configuration only or not. Default FALSE
BSgenomeName

A BSgenome object. For a list of existing Bsgenomes, please refer use the function available.genomes in BSgenome package. For example,BSgenome.Hsapiens.UCSC.hg38 is for hg38, BSgenome.Hsapiens.UCSC.hg19 is for hg19, BSgenome.Mmusculus.UCSC.mm10 is for mm10, BSgenome.Celegans.UCSC.ce6 is for ce6 BSgenome.Rnorvegicus.UCSC.rn5 is for rn5, BSgenome.Drerio.UCSC.danRer7 is for Zv9, and BSgenome.Dmelanogaster.UCSC.dm3 is for dm3. Required
txdb

A TxDb object. For creating and using TxDb object, please refer to GenomicFeatures package. For a list of existing TxDb object, please search for annotation package starting with Txdb at http://www.bioconductor.org/packages/release/BiocViews.html#___AnnotationData, such as TxDb.Rnorvegicus.UCSC.rn5.refGene for rat, TxDb.Mmusculus.UCSC.mm10.knownGene for mouse, TxDb.Hsapiens.UCSC.hg19.knownGene and TxDb.Hsapiens.UCSC.hg38.knownGene for human, TxDb.Dmelanogaster.UCSC.dm3.ensGene for Drosophila and TxDb.Celegans.UCSC.ce6.ensGene for C.elegans
geneIDs

One or more gene entrez IDs. For example the entrez ID for EWSIR is 2130 https://www.genecards.org/cgi-bin/carddisp.pl?gene=EWSR1 You can use the addGeneIDs function in ChIPpeakAnno to convert other types of Gene IDs to entrez ID
upstream

Number of bases upstream of the TSS to search for the motifs. Default 5000L
downstream

Number of bases downstream of the TSS to search for the motifs. Default 5000L
name.motif1

Name of the motif in inputfilePath2 for labeling the output file column. Default motif1. used only when searching for motifs in paired configuration
name.motif2

Name of the motif in inputfilePath2 for labeling the output file column. Default motif2 used only when searching for motifs in paired configuration
max.distance

maximum required gap between a paired motifs to be included in the output file. Default 100L
min.distance

Minimum required gap between a paired motifs to be included in the output file. Default 1L
motif.orientation

Required relative oriention between paired motifs: both means any orientation, motif1UpstreamOfMotif2 means motif1 needs to be located on the upstream of motif2, and motif2UpstreamOfMoif1 means motif2 needs to be located on the upstream of motif1. Default both
ignore.strand

Specify whether paired motifs should be located on the same strand. Default FALSE
format

The format of the files specified in inputFilePath1 and inputFilePath2. Default fasta
skip

Specify number of lines to skip at the beginning of the input file. Default 0L
motif1LocForDistance

Specify whether to use the start or end of the motif1 location to calculate distance between paired motifs. Only applicable when findPairedMotif is set to TRUE. Default end
motif2LocForDistance

Specify whether to use the start or end of the motif2 location to calculate distance between paired motifs. Only applicable when findPairedMotif is set to TRUE. Default start
outfile

File path to save the search results
append

Specify whether to append the results to the specified output file, i.e., outfile. Default FALSE

Details

This function outputs the motif occuring locations in the promoter regions of input gene list and input motifs. It also can find paired motifs within specificed gap threshold

Value

A vector of numeric. It is the background corrected log2-transformed ratios, CPMRatios or OddRatios.

An object of GRanges with metadata "tx_start", "tx_end tx_strand", "tx_id", "tx_name", "Gene ID", and motif specific information such as motif name, motif found, motif strand etc.

Author(s)

Lihua Julie Zhu, Kai Hu

Examples

library("BSgenome.Hsapiens.UCSC.hg38")
library("TxDb.Hsapiens.UCSC.hg38.knownGene")

patternFilePath1 =system.file("extdata", "motifIRF4.fa", package="ChIPpeakAnno")
patternFilePath2 =system.file("extdata", "motifAP1.fa", package="ChIPpeakAnno")
pairedMotifs <- findMotifsInPromoterSeqs(patternFilePath1 = patternFilePath1,
   patternFilePath2 = patternFilePath2,
   findPairedMotif = TRUE,
   name.motif1 = "IRF4", name.motif2 = "AP1",
   BSgenomeName = BSgenome.Hsapiens.UCSC.hg38,
   geneIDs = 7486, txdb = TxDb.Hsapiens.UCSC.hg38.knownGene,
   outfile = "testPaired.xls")

unPairedMotifs <- findMotifsInPromoterSeqs(patternFilePath1 = patternFilePath1,
    BSgenomeName = BSgenome.Hsapiens.UCSC.hg38,
   geneIDs = 7486, txdb = TxDb.Hsapiens.UCSC.hg38.knownGene,
   outfile = "testUnPaired.xls")

Find the overlapping peaks for two peak ranges.

Description

Find the overlapping peaks for two input peak ranges.

Usage

findOverlappingPeaks(
  Peaks1,
  Peaks2,
  maxgap = -1L,
  minoverlap = 0L,
  multiple = c(TRUE, FALSE),
  NameOfPeaks1 = "TF1",
  NameOfPeaks2 = "TF2",
  select = c("all", "first", "last", "arbitrary"),
  annotate = 0,
  ignore.strand = TRUE,
  connectedPeaks = c("min", "merge"),
  ...
)

Arguments

Peaks1

GRanges: See example below.
Peaks2

GRanges: See example below.
maxgap, minoverlap

Used in the internal call to findOverlaps() to detect overlaps. See ?findOverlaps in the IRanges package for a description of these arguments.
multiple

TRUE or FALSE: TRUE may return multiple overlapping peaks in Peaks2 for one peak in Peaks1; FALSE will return at most one overlapping peaks in Peaks2 for one peak in Peaks1. This parameter is kept for backward compatibility, please use select.
NameOfPeaks1

Name of the Peaks1, used for generating column name.
NameOfPeaks2

Name of the Peaks2, used for generating column name.
select

all may return multiple overlapping peaks, first will return the first overlapping peak, last will return the last overlapping peak and arbitrary will return one of the overlapping peaks.
annotate

Include overlapFeature and shortestDistance in the OverlappingPeaks or not. 1 means yes and 0 means no. Default to 0.
ignore.strand

When set to TRUE, the strand information is ignored in the overlap calculations.
connectedPeaks

If multiple peaks involved in overlapping in several groups, set it to "merge" will count it as only 1, while set it to "min" will count it as the minimal involved peaks in any concered groups
...

Objects of GRanges: See also findOverlapsOfPeaks.

Details

The new function findOverlapsOfPeaks is recommended.

Efficiently perform overlap queries with an interval tree implemented in IRanges.

Value

OverlappingPeaks

a data frame consists of input peaks information with added information: overlapFeature (upstream: peak1 resides upstream of the peak2; downstream: peak1 resides downstream of the peak2; inside: peak1 resides inside the peak2 entirely; overlapStart: peak1 overlaps with the start of the peak2; overlapEnd: peak1 overlaps with the end of the peak2; includeFeature: peak1 include the peak2 entirely) and shortestDistance (shortest distance between the overlapping peaks)
MergedPeaks

GRanges contains merged overlapping peaks

Author(s)

Lihua Julie Zhu

References

1.Interval tree algorithm from: Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford. Introduction to Algorithms, second edition, MIT Press and McGraw-Hill. ISBN 0-262-53196-8

2.Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237 doi:10.1186/1471-2105-11-237

3. Zhu L (2013). Integrative analysis of ChIP-chip and ChIP-seq dataset. In Lee T and Luk ACS (eds.), Tilling Arrays, volume 1067, chapter 4, pp. -19. Humana Press. http://dx.doi.org/10.1007/978-1-62703-607-8_8

See Also

findOverlapsOfPeaks, annotatePeakInBatch, makeVennDiagram

Examples

if (interactive())
    {    
    peaks1 = 
        GRanges(seqnames=c(6,6,6,6,5), 
                IRanges(start=c(1543200,1557200,1563000,1569800,167889600),
                        end=c(1555199,1560599,1565199,1573799,167893599),
                        names=c("p1","p2","p3","p4","p5")),
                strand=as.integer(1))
    peaks2 = 
        GRanges(seqnames=c(6,6,6,6,5), 
                IRanges(start=c(1549800,1554400,1565000,1569400,167888600),
                        end=c(1550599,1560799,1565399,1571199,167888999),
                        names=c("f1","f2","f3","f4","f5")),
                strand=as.integer(1))
    t1 =findOverlappingPeaks(peaks1, peaks2, maxgap=1000, 
          NameOfPeaks1="TF1", NameOfPeaks2="TF2", select="all", annotate=1) 
    r = t1$OverlappingPeaks
    pie(table(r$overlapFeature))
    as.data.frame(t1$MergedPeaks)
    }

Find the overlapped peaks among two or more set of peaks.

Description

Find the overlapping peaks for two or more (less than five) set of peak ranges.

Usage

findOverlapsOfPeaks(
  ...,
  maxgap = -1L,
  minoverlap = 0L,
  ignore.strand = TRUE,
  connectedPeaks = c("keepAll", "min", "merge")
)

Arguments

...

Objects of GRanges: See example below.
maxgap, minoverlap

Used in the internal call to findOverlaps() to detect overlaps. See ?findOverlaps in the IRanges package for a description of these arguments. If 0 < minoverlap < 1, the function will find overlaps by percentage covered of interval and the filter condition will be set to max covered percentage of overlapping peaks.
ignore.strand

When set to TRUE, the strand information is ignored in the overlap calculations.
connectedPeaks

If multiple peaks are involved in any group of connected/overlapping peaks in any input peak list, set it to "merge" will add 1 to the overlapping counts, while set it to "min" will add the minimal involved peaks in each group of connected/overlapped peaks to the overlapping counts. Set it to "keepAll" will add the number of involved peaks for each peak list to the corresponding overlapping counts. In addition, it will output counts as if connectedPeaks were set to "min". For examples (https://support.bioconductor.org/p/133486/#133603), if 5 peaks in group1 overlap with 2 peaks in group 2, setting connectedPeaks to "merge" will add 1 to the overlapping counts; setting it to "keepAll" will add 5 peaks to count.group1, 2 to count.group2, and 2 to counts; setting it to “min” will add 2 to the overlapping counts.

Details

Efficiently perform overlap queries with an interval tree implemented with GRanges.

Value

return value is An object of overlappingPeaks.

venn_cnt

an object of VennCounts
peaklist

a list consists of all overlapping peaks or unique peaks
uniquePeaks

an object of GRanges consists of all unique peaks
mergedPeaks

an object of GRanges consists of all merged overlapping peaks
peaksInMergedPeaks

an object of GRanges consists of all peaks in each samples involved in the overlapping peaks
overlappingPeaks

a list of data frame consists of the annotation of all the overlapped peaks
all.peaks

a list of GRanges object which contain the input peaks with formated rownames.

Author(s)

Jianhong Ou

References

1.Interval tree algorithm from: Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford. Introduction to Algorithms, second edition, MIT Press and McGraw-Hill. ISBN 0-262-53196-8

2.Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237

3. Zhu L (2013). "Integrative analysis of ChIP-chip and ChIP-seq dataset." In Lee T and Luk ACS (eds.), Tilling Arrays, volume 1067, chapter 4, pp. -19. Humana Press. http://dx.doi.org/10.1007/978-1-62703-607-8_8, http://link.springer.com/protocol/10.1007%2F978-1-62703-607-8_8

See Also

annotatePeakInBatch, makeVennDiagram, getVennCounts, findOverlappingPeaks

Examples

peaks1 <- GRanges(seqnames=c(6,6,6,6,5),
                 IRanges(start=c(1543200,1557200,1563000,1569800,167889600),
                         end=c(1555199,1560599,1565199,1573799,167893599),
                         names=c("p1","p2","p3","p4","p5")),
                 strand="+")
peaks2 <- GRanges(seqnames=c(6,6,6,6,5),
                  IRanges(start=c(1549800,1554400,1565000,1569400,167888600),
                          end=c(1550599,1560799,1565399,1571199,167888999),
                          names=c("f1","f2","f3","f4","f5")),
                  strand="+")
t1 <- findOverlapsOfPeaks(peaks1, peaks2, maxgap=1000)
makeVennDiagram(t1)
t1$venn_cnt
t1$peaklist
t2 <- findOverlapsOfPeaks(peaks1, peaks2, minoverlap = .5)
makeVennDiagram(t2)

t3 <- findOverlapsOfPeaks(peaks1, peaks2, minoverlap = .90)
makeVennDiagram(t3)

Genomic Element distribution

Description

Plot pie chart for genomic element distribution

Usage

genomicElementDistribution(
  peaks,
  TxDb,
  seqlev,
  nucleotideLevel = FALSE,
  ignore.strand = TRUE,
  promoterRegion = c(upstream = 2000, downstream = 100),
  geneDownstream = c(upstream = 0, downstream = 1000),
  labels = list(geneLevel = c(promoter = "Promoter", geneDownstream = "Downstream",
    geneBody = "Gene body", distalIntergenic = "Distal Intergenic"), ExonIntron = c(exon
    = "Exon", intron = "Intron", intergenic = "Intergenic"), Exons = c(utr5 = "5' UTR",
    utr3 = "3' UTR", CDS = "CDS", otherExon = "Other exon"), group = c(geneLevel =
    "Transcript Level", promoterLevel = "Promoter Level", Exons = "Exon level",
    ExonIntron = "Exon/Intron/Intergenic")),
  labelColors = c(promoter = "#E1F114", geneBody = "#9EFF00", geneDownstream = "#57CB1B",
    distalIntergenic = "#066A4B", exon = "#6600FF", intron = "#8F00FF", intergenic =
    "#DA00FF", utr5 = "#00FFDB", utr3 = "#00DFFF", CDS = "#00A0FF", otherExon =
    "#006FFF"),
  plot = TRUE,
  keepExonsInGenesOnly = TRUE,
  promoterLevel
)

Arguments

peaks

peak list, GRanges object or a GRangesList.
TxDb

an object of TxDb
seqlev

sequence level should be involved. Default is all the sequence levels in intersect of peaks and TxDb.
nucleotideLevel

Logical. Choose between peak centric and nucleotide centric view. Default=FALSE
ignore.strand

logical. Whether the strand of the input ranges should be ignored or not. Default=TRUE
promoterRegion

numeric. The upstream and downstream of genes to define promoter region.
geneDownstream

numeric. The upstream and downstream of genes to define gene downstream region.
labels

list. A list for labels for the genomic elements.
labelColors

named character vector. The colors for each labels.
plot

logic. Plot the pie chart for the genomic elements or not.
keepExonsInGenesOnly

logic. Keep the exons within annotated gene only.
promoterLevel

list. The breaks, labels, and colors for divided range of promoters. The breaks must be from 5' -> 3' and the percentage will use the fixed precedence 3' -> 5'

Details

The distribution will be calculated by geneLevel, ExonIntron, and Exons The geneLevel will be categorized as promoter region, gene body, gene downstream and distal intergenic region. The ExonIntron will be categorized as exon, intron and intergenic. The Exons will be categorized as 5' UTR, 3'UTR and CDS. The precedence will follow the order of labels defination. For example, for ExonIntron, if a peak overlap with both exon and intron, and exon is specified before intron, then only exon will be incremented for the same example.

Value

Invisible list of data for plot.

Examples

if (interactive() || Sys.getenv("USER")=="jianhongou"){
  data(myPeakList)
  if(require(TxDb.Hsapiens.UCSC.hg19.knownGene)){
  seqinfo(myPeakList) <- 
  seqinfo(TxDb.Hsapiens.UCSC.hg19.knownGene)[seqlevels(myPeakList)]
  myPeakList <- GenomicRanges::trim(myPeakList)
  myPeakList <- myPeakList[width(myPeakList)>0]
    genomicElementDistribution(myPeakList, 
        TxDb.Hsapiens.UCSC.hg19.knownGene)
    genomicElementDistribution(myPeakList, 
        TxDb.Hsapiens.UCSC.hg19.knownGene,
        nucleotideLevel = TRUE)
    genomicElementDistribution(myPeakList, 
        TxDb.Hsapiens.UCSC.hg19.knownGene,
        promoterLevel=list(
        #from 5' -> 3', fixed precedence 3' -> 5'
        breaks = c(-2000, -1000, -500, 0, 100),
        labels = c("upstream 1-2Kb", "upstream 0.5-1Kb", 
                   "upstream <500b", "TSS - 100b"),
        colors = c("#FFE5CC", "#FFCA99", 
                   "#FFAD65", "#FF8E32")))
  }
}

Genomic Element data for upset plot

Description

Prepare data for upset plot for genomic element distribution

Usage

genomicElementUpSetR(
  peaks,
  TxDb,
  seqlev,
  ignore.strand = TRUE,
  breaks = list(distal_upstream = c(-1e+05, -10000, -1, 1), proximal_upstream = c(-10000,
    -5000, -1, 1), distal_promoter = c(-5000, -2000, -1, 1), proximal_promoter = c(-2000,
    200, -1, 0), `5'UTR` = fiveUTRsByTranscript, `3'UTR` = threeUTRsByTranscript, CDS =
    cds, exon = exons, intron = intronsByTranscript, gene_body = genes,
    immediate_downstream = c(0, 2000, 1, 1), proximal_downstream = c(2000, 5000, 1, 1),
    distal_downstream = c(5000, 1e+05, 1, 1))
)

Arguments

peaks

peak list, GRanges object or a GRangesList.
TxDb

an object of TxDb
seqlev

sequence level should be involved. Default is all the sequence levels in intersect of peaks and TxDb.
ignore.strand

logical. Whether the strand of the input ranges should be ignored or not. Default=TRUE
breaks

list. A list for labels and sets for the genomic elements. The element could be an S4 method for signature 'TxDb' or a numeric vector with length of 4. The three numbers are c(upstream point, downstream point, promoter (-1) or downstream (1), remove gene body or not (1: remove, 0: keep)).

Details

The data will be calculated by for each breaks. No precedence will be considered.

Value

list of data for plot.

Examples

if (interactive() || Sys.getenv("USER")=="jianhongou"){
  data(myPeakList)
  if(require(TxDb.Hsapiens.UCSC.hg19.knownGene)){
  seqinfo(myPeakList) <- 
  seqinfo(TxDb.Hsapiens.UCSC.hg19.knownGene)[seqlevels(myPeakList)]
  myPeakList <- GenomicRanges::trim(myPeakList)
  myPeakList <- myPeakList[width(myPeakList)>0]
  x <- genomicElementUpSetR(myPeakList, 
    TxDb.Hsapiens.UCSC.hg19.knownGene)
  library(UpSetR)
  upset(x$plotData, nsets=13, nintersects=NA)
  }
}

Obtain genomic sequences around the peaks

Description

Obtain genomic sequences around the peaks leveraging the BSgenome and biomaRt package

Usage

getAllPeakSequence(
  myPeakList,
  upstream = 200L,
  downstream = upstream,
  genome,
  AnnotationData
)

Arguments

myPeakList

An object of GRanges: See example below
upstream

upstream offset from the peak start, e.g., 200
downstream

downstream offset from the peak end, e.g., 200
genome

BSgenome object or mart object. Please refer to available.genomes in BSgenome package and useMart in bioMaRt package for details
AnnotationData

GRanges object with annotation information.

Value

GRanges with slot start holding the start position of the peak, slot end holding the end position of the peak, slot rownames holding the id of the peak and slot seqnames holding the chromosome where the peak is located. In addition, the following variables are included:

upstream

upstream offset from the peak start
downstream

downstream offset from the peak end
sequence

the sequence obtained

Author(s)

Lihua Julie Zhu, Jianhong Ou

References

Durinck S. et al. (2005) BioMart and Bioconductor: a powerful link between biological biomarts and microarray data analysis. Bioinformatics, 21, 3439-3440.

Examples

#### use Annotation data from BSgenome
peaks <- GRanges(seqnames=c("NC_008253", "NC_010468"),
                 IRanges(start=c(100, 500), end=c(300, 600), 
                         names=c("peak1", "peak2")))
library(BSgenome.Ecoli.NCBI.20080805)
seq <- getAllPeakSequence(peaks, upstream=20, downstream=20, genome=Ecoli)
write2FASTA(seq, file="test.fa")

Obtain the TSS, exon or miRNA annotation for the specified species

Description

Obtain the TSS, exon or miRNA annotation for the specified species using the biomaRt package

Usage

getAnnotation(
  mart,
  featureType = c("TSS", "miRNA", "Exon", "5utr", "3utr", "ExonPlusUtr", "transcript")
)

Arguments

mart

A mart object, see useMart of biomaRt package for details.
featureType

TSS, miRNA, Exon, 5'UTR, 3'UTR, transcript or Exon plus UTR. The default is TSS.

Value

GRanges with slot start holding the start position of the feature, slot end holding the end position of the feature, slot names holding the id of the feature, slot space holding the chromosome location where the feature is located. In addition, the following variables are included.

list("strand")

1 for positive strand and -1 for negative strand where the feature is located
list("description")

description of the feeature such as gene

Note

For featureType of TSS, start is the transcription start site if strand is 1 (plus strand), otherwise, end is the transcription start site.

Note that the version of the annotation db must match with the genome used for mapping because the coordinates may differ for different genome releases. For example, if you are using Mus_musculus.v103 for mapping, you'd best also use EnsDb.Mmusculus.v103 for annotation. See Examples for more info.

Author(s)

Lihua Julie Zhu, Jianhong Ou, Kai Hu

References

Durinck S. et al. (2005) BioMart and Bioconductor: a powerful link between biological biomarts and microarray data analysis. Bioinformatics, 21, 3439-3440.

Examples

if (interactive() || Sys.getenv("USER")=="jianhongou" )
{
  library(biomaRt)
  mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
  Annotation <- getAnnotation(mart, featureType="TSS")
}

##########################################################
#  Below are 3 options to fetch the annotation file.     #
##########################################################
if (interactive() || Sys.getenv("USER")=="jianhongou" ){
## Option1: with the AnnotationHub package
library(AnnotationHub)
ah <- AnnotationHub()
EnsDb.Mmusculus <- query(ah, pattern = c("Mus musculus", "EnsDb"))
EnsDb.Mmusculus.v101 <- EnsDb.Mmusculus[[length(EnsDb.Mmusculus)]]
class(EnsDb.Mmusculus.v101)

## Option2: with the getAnnotation() function
library(ChIPpeakAnno)
library(biomaRt)
listMarts()
mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL", 
                dataset="mmusculus_gene_ensembl")
Annotation <- getAnnotation(mart)
# Note that getAnnotation() queries biomart, which is always up-to-date.

## Option3: build your own EnsDb package
## This may need extra effort, and the ?makeEnsembldbPackage 
## is a good starting point.
}

Obtain enriched gene ontology (GO) terms that near the peaks

Description

Obtain enriched gene ontology (GO) terms based on the features near the enriched peaks using GO.db package and GO gene mapping package such as org.Hs.db.eg to obtain the GO annotation and using hypergeometric test (phyper) and multtest package for adjusting p-values

Usage

getEnrichedGO(
  annotatedPeak,
  orgAnn,
  feature_id_type = "ensembl_gene_id",
  maxP = 0.01,
  minGOterm = 10,
  multiAdjMethod = NULL,
  condense = FALSE,
  removeAncestorByPval = NULL,
  keepByLevel = NULL,
  subGroupComparison = NULL
)

Arguments

annotatedPeak

A GRanges object or a vector of feature IDs
orgAnn

Organism annotation package such as org.Hs.eg.db for human and org.Mm.eg.db for mouse, org.Dm.eg.db for fly, org.Rn.eg.db for rat, org.Sc.eg.db for yeast and org.Dr.eg.db for zebrafish
feature_id_type

The feature type in annotatedPeak such as ensembl_gene_id, refseq_id, gene_symbol or entrez_id
maxP

The maximum p-value to be considered to be significant
minGOterm

The minimum count in a genome for a GO term to be included
multiAdjMethod

The multiple testing procedures, for details, see mt.rawp2adjp in multtest package
condense

Condense the results or not.
removeAncestorByPval

Remove ancestor by p-value. P-value is calculated by fisher exact test. If gene number in all of the children is significant greater than it in parent term, the parent term will be removed from the list.
keepByLevel

If the shortest path from the go term to 'all' is greater than the given level, the term will be removed.
subGroupComparison

A logical vector to split the peaks into two groups. The enrichment analysis will compare the over-present GO terms in TRUE group and FALSE group separately. The analysis will split into two steps: 1. enrichment analysis for TRUE group by hypergeometric test; 2. enrichment analysis for TRUE over FALSE group by Fisher's Exact test for the enriched GO terms. To keep the output same format, if you want to compare FALSE vs TRUE, please repeat the analysis by inverting the parameter. Default is NULL.

Value

A list with 3 elements

list("bp")

enriched biological process with the following 9 variables

go.id:GO biological process id

go.term:GO biological process term

go.Definition:GO biological process description

Ontology: Ontology branch, i.e. BP for biological process

count.InDataset: count of this GO term in this dataset

count.InGenome: count of this GO term in the genome

pvalue: pvalue from the hypergeometric test

totaltermInDataset: count of all GO terms in this dataset

totaltermInGenome: count of all GO terms in the genome

list("mf")

enriched molecular function with the following 9 variables

go.id:GO molecular function id

go.term:GO molecular function term

go.Definition:GO molecular function description

Ontology: Ontology branch, i.e. MF for molecular function

count.InDataset: count of this GO term in this dataset

count.InGenome: count of this GO term in the genome

pvalue: pvalue from the hypergeometric test

totaltermInDataset: count of all GO terms in this dataset

totaltermInGenome: count of all GO terms in the genome

list("cc")

enriched cellular component the following 9 variables

go.id:GO cellular component id

go.term:GO cellular component term

go.Definition:GO cellular component description

Ontology: Ontology type, i.e. CC for cellular component

count.InDataset: count of this GO term in this dataset

count.InGenome: count of this GO term in the genome

pvalue: pvalue from the hypergeometric test

totaltermInDataset: count of all GO terms in this dataset

totaltermInGenome: count of all GO terms in the genome

Author(s)

Lihua Julie Zhu. Jianhong Ou for subGroupComparison

References

Johnson, N. L., Kotz, S., and Kemp, A. W. (1992) Univariate Discrete Distributions, Second Edition. New York: Wiley

See Also

phyper, hyperGtest

Examples

data(enrichedGO)
  enrichedGO$mf[1:10,]
  enrichedGO$bp[1:10,]
  enrichedGO$cc
  if (interactive()) {
     data(annotatedPeak)
     library(org.Hs.eg.db)
     library(GO.db)
     enriched.GO = getEnrichedGO(annotatedPeak[1:6,], 
                                 orgAnn="org.Hs.eg.db", 
                                 maxP=0.01,
                                 minGOterm=10,
                                 multiAdjMethod= NULL)
     dim(enriched.GO$mf)
     colnames(enriched.GO$mf)
     dim(enriched.GO$bp)
     enriched.GO$cc
}

Obtain enriched PATH that near the peaks

Description

Obtain enriched PATH that are near the peaks using path package such as reactome.db and path mapping package such as org.Hs.db.eg to obtain the path annotation and using hypergeometric test (phyper) and multtest package for adjusting p-values

Usage

getEnrichedPATH(
  annotatedPeak,
  orgAnn,
  pathAnn,
  feature_id_type = "ensembl_gene_id",
  maxP = 0.01,
  minPATHterm = 10,
  multiAdjMethod = NULL,
  subGroupComparison = NULL
)

Arguments

annotatedPeak

GRanges such as data(annotatedPeak) or a vector of feature IDs
orgAnn

organism annotation package such as org.Hs.eg.db for human and org.Mm.eg.db for mouse, org.Dm.eg.db for fly, org.Rn.eg.db for rat, org.Sc.eg.db for yeast and org.Dr.eg.db for zebrafish
pathAnn

pathway annotation package such as KEGG.db (deprecated), reactome.db, KEGGREST
feature_id_type

the feature type in annotatedPeakRanges such as ensembl_gene_id, refseq_id, gene_symbol or entrez_id
maxP

maximum p-value to be considered to be significant
minPATHterm

minimum count in a genome for a path to be included
multiAdjMethod

multiple testing procedures, for details, see mt.rawp2adjp in multtest package
subGroupComparison

A logical vector to split the peaks into two groups. The enrichment analysis will compare the over-present GO terms in TRUE group and FALSE group separately. The analysis will split into two steps: 1. enrichment analysis for TRUE group by hypergeometric test; 2. enrichment analysis for TRUE over FALSE group by Fisher's Exact test for the enriched GO terms. To keep the output same format, if you want to compare FALSE vs TRUE, please repeat the analysis by inverting the parameter. Default is NULL.

Value

A dataframe of enriched path with the following variables.

path.id

KEGG PATH ID
EntrezID

Entrez ID
count.InDataset

count of this PATH in this dataset
count.InGenome

count of this PATH in the genome
pvalue

pvalue from the hypergeometric test
totaltermInDataset

count of all PATH in this dataset
totaltermInGenome

count of all PATH in the genome
PATH

PATH name

Author(s)

Jianhong Ou, Kai Hu

References

Johnson, N. L., Kotz, S., and Kemp, A. W. (1992) Univariate Discrete Distributions, Second Edition. New York: Wiley

See Also

phyper, hyperGtest

Examples

if (interactive()||Sys.getenv("USER")=="jianhongou") {
data(annotatedPeak)
library(org.Hs.eg.db)
library(reactome.db)
enriched.PATH = getEnrichedPATH(annotatedPeak, orgAnn="org.Hs.eg.db",
                 feature_id_type="ensembl_gene_id",
                 pathAnn="reactome.db", maxP=0.01,
                 minPATHterm=10, multiAdjMethod=NULL)
 head(enriched.PATH)
 enrichedKEGG = getEnrichedPATH(annotatedPeak, orgAnn="org.Hs.eg.db",
                 feature_id_type="ensembl_gene_id",
                 pathAnn="KEGGREST", maxP=0.01,
                 minPATHterm=10, multiAdjMethod=NULL)
 enrichmentPlot(enrichedKEGG)
}

Obtain gene ontology (GO) terms for given genes

Description

Obtain gene ontology (GO) terms useing GO gene mapping package such as org.Hs.db.eg to obtain the GO annotation.

Usage

getGO(all.genes, orgAnn = "org.Hs.eg.db", writeTo, ID_type = "gene_symbol")

Arguments

all.genes

A character vector of feature IDs
orgAnn

Organism annotation package such as org.Hs.eg.db for human and org.Mm.eg.db for mouse, org.Dm.eg.db for fly, org.Rn.eg.db for rat, org.Sc.eg.db for yeast and org.Dr.eg.db for zebrafish
writeTo

File path for output table
ID_type

The feature type in annotatedPeak such as ensembl_gene_id, refseq_id, gene_symbol

Value

An invisible table with genes and GO terms.

Author(s)

Lihua Julie Zhu

See Also

getEnrichedGO

Examples

if (interactive()) {
     data(annotatedPeak)
     library(org.Hs.eg.db)
     getGO(annotatedPeak[1:6]$feature, 
          orgAnn="org.Hs.eg.db", 
          ID_type="ensembl_gene_id")
}

Obtain Venn Counts for Venn Diagram, internal function for makeVennDigram

Description

Obtain Venn Counts for peak ranges using chromosome ranges or feature field, internal function for makeVennDigram

Usage

getVennCounts(
  ...,
  maxgap = -1L,
  minoverlap = 0L,
  by = c("region", "feature", "base"),
  ignore.strand = TRUE,
  connectedPeaks = c("min", "merge", "keepAll")
)

Arguments

...

Objects of GRanges. See example below.
maxgap, minoverlap

Used in the internal call to findOverlaps() to detect overlaps. See ?findOverlaps in the IRanges package for a description of these arguments.
by

region, feature or base, default region. feature means using feature field in the GRanges for calculating overlap, region means using chromosome range for calculating overlap, and base means using calculating overlap in nucleotide level.
ignore.strand

When set to TRUE, the strand information is ignored in the overlap calculations.
connectedPeaks

If multiple peaks involved in overlapping in several groups, set it to "merge" will count it as only 1, while set it to "min" will count it as the minimal involved peaks in any concered groups

Value

vennCounts

vennCounts objects containing counts for Venn Diagram generation, see details in limma package vennCounts

Author(s)

Jianhong Ou

See Also

makeVennDiagram, findOverlappingPeaks

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
peaks1 = GRanges(seqnames=c("1", "2", "3"), 
                 IRanges(start = c(967654, 2010897, 2496704), 
                            end = c(967754, 2010997, 2496804), 
                            names = c("Site1", "Site2", "Site3")),  
                   strand=as.integer(1), 
                   feature=c("a","b", "c"))
  peaks2 = 
      GRanges(seqnames= c("1", "2", "3", "1", "2"), 
                    IRanges(start=c(967659, 2010898, 2496700, 3075866, 3123260), 
                         end=c(967869, 2011108, 2496920, 3076166, 3123470), 
                         names = c("t1", "t2", "t3", "t4", "t5")), 
                    strand = c(1L, 1L, -1L,-1L,1L), 
                    feature=c("a","c","d","e", "a"))
    getVennCounts(peaks1,peaks2)
    getVennCounts(peaks1,peaks2, by="feature")
    getVennCounts(peaks1, peaks2, by="base")
}

High Occupancy of Transcription Related Factors regions

Description

High Occupancy of Transcription Related Factors regions of human (hg19)

Usage

HOT.spots

Format

An object of GRangesList

Details

How to generated the data:

temp <- tempfile()

url <- "http://metatracks.encodenets.gersteinlab.org"

download.file(file.path(url, "HOT_All_merged.tar.gz"), temp)

temp2 <- tempfile()

download.file(file.path(url, "HOT_intergenic_All_merged.tar.gz"), temp2)

untar(temp, exdir=dirname(temp))

untar(temp2, exdir=dirname(temp))

f <- dir(dirname(temp), "bed$")

HOT.spots <- sapply(file.path(dirname(temp), f), toGRanges, format="BED")

names(HOT.spots) <- gsub("_merged.bed", "", f)

HOT.spots <- sapply(HOT.spots, unname)

HOT.spots <- GRangesList(HOT.spots)

save(list="HOT.spots",

file="data/HOT.spots.rda",

compress="xz", compression_level=9)

Source

http://metatracks.encodenets.gersteinlab.org/

References

Yip KY, Cheng C, Bhardwaj N, Brown JB, Leng J, Kundaje A, Rozowsky J, Birney E, Bickel P, Snyder M, Gerstein M. Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors. Genome Biol. 2012 Sep 26;13(9):R48. doi: 10.1186/gb-2012-13-9-r48. PubMed PMID: 22950945; PubMed Central PMCID: PMC3491392.

Examples

data(HOT.spots)
 elementNROWS(HOT.spots)

Filter peaks by IDR (irreproducible discovery rate)

Description

Using IDR to assess the consistency of replicate experiments and obtain a high-confidence single set of peaks

Usage

IDRfilter(
  peaksA,
  peaksB,
  bamfileA,
  bamfileB,
  maxgap = -1L,
  minoverlap = 0L,
  singleEnd = TRUE,
  IDRcutoff = 0.01,
  ...
)

Arguments

peaksA, peaksB

peaklist, GRanges object.
bamfileA, bamfileB

file path of bam files.
maxgap, minoverlap

Used in the internal call to findOverlaps() to detect overlaps. See ?findOverlaps in the IRanges package for a description of these arguments.
singleEnd

(Default TRUE) A logical indicating if reads are single or paired-end.
IDRcutoff

If the IDR no less than IDRcutoff, the peak will be removed.
...

Not used.

Value

An object GRanges

Author(s)

Jianhong Ou

References

Li, Qunhua, et al. "Measuring reproducibility of high-throughput experiments." The annals of applied statistics (2011): 1752-1779.

Examples

if(interactive()){
    path <- system.file("extdata", "reads", package="MMDiffBamSubset")
    if(file.exists(path)){
            bamfileA <- file.path(path, "reads", "WT_2.bam")
            bamfileB <- file.path(path, "reads", "Resc_2.bam")
            WT.AB2.Peaks <- file.path(path, "peaks", "WT_2_Macs_peaks.xls")
            Resc.AB2.Peaks <- file.path(path, "peaks",
                                       "Resc_2_Macs_peaks.xls")
            peaksA=toGRanges(WT.AB2.Peaks, format="MACS")
            peaksB=toGRanges(Resc.AB2.Peaks, format="MACS")
            library(idr)
            library(DelayedArray)
            IDRfilter(peaksA, peaksB,
                        bamfileA, bamfileB)
    }
  }

Make Venn Diagram from a list of peaks

Description

Make Venn Diagram from two or more peak ranges, Also calculate p-value to determine whether those peaks overlap significantly.

Usage

makeVennDiagram(
  Peaks,
  NameOfPeaks,
  maxgap = -1L,
  minoverlap = 0L,
  totalTest,
  by = c("region", "feature", "base"),
  ignore.strand = TRUE,
  connectedPeaks = c("min", "merge", "keepAll", "keepFirstListConsistent"),
  method = c("hyperG", "permutation"),
  TxDb,
  plot = TRUE,
  ...
)

Arguments

Peaks

A list of peaks in GRanges format: See example below.
NameOfPeaks

Character vector to specify the name of Peaks, e.g., c("TF1", "TF2"). This will be used as label in the Venn Diagram.
maxgap, minoverlap

Used in the internal call to findOverlaps() to detect overlaps. See ?findOverlaps in the IRanges package for a description of these arguments.
totalTest

Numeric value to specify the total number of tests performed to obtain the list of peaks. It should be much larger than the number of peaks in the largest peak set.
by

"region", "feature" or "base", default = "region". "feature" means using feature field in the GRanges for calculating overlap, "region" means using chromosome range for calculating overlap, and "base" means calculating overlap in nucleotide level.
ignore.strand

Logical: when set to TRUE, the strand information is ignored in the overlap calculations.
connectedPeaks

If multiple peaks involved in overlapping in several groups, set it to "merge" will count it as only 1, while set it to "min" will count it as the minimal involved peaks in any connected peak group. "keepAll" will show all the orginal counts for each list while the final counts will be same as "min". "keepFirstListConsistent" will keep the counts consistent with first list.
method

method to be used for p value calculation. hyperG means hypergeometric test and permutation means peakPermTest.
TxDb

An object of TxDb.
plot

logical. If TRUE (default), a venn diagram is plotted.
...

Additional arguments to be passed to venn.diagram.

Details

For customized graph options, please see venn.diagram in VennDiagram package.

Value

A p.value is calculated by hypergeometric test or permutation test to determine whether the overlaps of peaks or features are significant.

Author(s)

Lihua Julie Zhu, Jianhong Ou

See Also

findOverlapsOfPeaks, venn.diagram, peakPermTest

Examples

if (interactive()){
peaks1 <- GRanges(seqnames=c("1", "2", "3"),
                  IRanges(start=c(967654, 2010897, 2496704),
                          end=c(967754, 2010997, 2496804), 
                          names=c("Site1", "Site2", "Site3")),
                  strand="+",
                  feature=c("a","b","f"))
peaks2 = GRanges(seqnames=c("1", "2", "3", "1", "2"), 
                 IRanges(start = c(967659, 2010898,2496700,
                                   3075866,3123260),
                         end = c(967869, 2011108, 2496920, 
                                 3076166, 3123470),
                         names = c("t1", "t2", "t3", "t4", "t5")), 
                 strand = c("+", "+", "-", "-", "+"), 
                 feature=c("a","b","c","d","a"))
makeVennDiagram(list(peaks1, peaks2), NameOfPeaks=c("TF1", "TF2"),
                totalTest=100,scaled=FALSE, euler.d=FALSE, 
                fill=c("#009E73", "#F0E442"), # circle fill color
                col=c("#D55E00", "#0072B2"), #circle border color
                cat.col=c("#D55E00", "#0072B2"))

makeVennDiagram(list(peaks1, peaks2), NameOfPeaks=c("TF1", "TF2"),
                totalTest=100, 
                fill=c("#009E73", "#F0E442"), # circle fill color
                col=c("#D55E00", "#0072B2"), #circle border color
                cat.col=c("#D55E00", "#0072B2"))

###### 4-way diagram using annotated feature instead of chromosome ranges

makeVennDiagram(list(peaks1, peaks2, peaks1, peaks2), 
                NameOfPeaks=c("TF1", "TF2","TF3", "TF4"), 
                totalTest=100, by="feature",
                main = "Venn Diagram for 4 peak lists",
                fill=c(1,2,3,4))
}

Merge peaks from plus strand and minus strand

Description

Merge peaks from plus strand and minus strand within certain distance apart, and output merged peaks as bed format.

Usage

mergePlusMinusPeaks(
  peaks.file,
  columns = c("name", "chromosome", "start", "end", "strand", "count", "count", "count",
    "count"),
  sep = "\t",
  header = TRUE,
  distance.threshold = 100,
  plus.strand.start.gt.minus.strand.end = TRUE,
  output.bedfile
)

Arguments

peaks.file

Specify the peak file. The peak file should contain peaks from both plus and minus strand
columns

Specify the column names in the peak file
sep

Specify column delimiter, default tab-delimited
header

Specify whether the file has a header row, default TRUE
distance.threshold

Specify the maximum gap allowed between the plus stranded and the nagative stranded peak
plus.strand.start.gt.minus.strand.end

Specify whether plus strand peak start greater than the paired negative strand peak end. Default to TRUE
output.bedfile

Specify the bed output file name

Value

output the merged peaks in bed file and a data frame of the bed format

Author(s)

Lihua Julie Zhu

References

Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237

See Also

annotatePeakInBatch, findOverlappingPeaks, makeVennDiagram

Examples

if (interactive())
{
    data(myPeakList)
    data(TSS.human.NCBI36)
    library(matrixStats)
        peaks <- system.file("extdata", "guide-seq-peaks.txt", 
                              package = "ChIPpeakAnno")
        merged.bed <- mergePlusMinusPeaks(peaks.file = peaks, 
                                          columns=c("name", "chromosome", 
                                                    "start", "end", "strand", 
                                                    "count", "count"), 
                                          sep = "\t", header = TRUE,  
                                          distance.threshold = 100,  
                                plus.strand.start.gt.minus.strand.end = TRUE, 
                                          output.bedfile = "T2test100bp.bed")
}

peak distance to features

Description

Bar plot for distance to features

Usage

metagenePlot(
  peaks,
  AnnotationData,
  PeakLocForDistance = c("middle", "start", "end"),
  FeatureLocForDistance = c("TSS", "middle", "geneEnd"),
  upstream = 1e+05,
  downstream = 1e+05
)

Arguments

peaks

peak list, GRanges object or a GRangesList.
AnnotationData

A GRanges object or a TxDb object.
PeakLocForDistance

Specify the location of peak for calculating distance,i.e., middle means using middle of the peak to calculate distance to feature, start means using start of the peak to calculate the distance to feature. To be compatible with previous version, by default using start
FeatureLocForDistance

Specify the location of feature for calculating distance,i.e., middle means using middle of the feature to calculate distance of peak to feature, TSS means using start of feature when feature is on plus strand and using end of feature when feature is on minus strand, geneEnd means using end of feature when feature is on plus strand and using start of feature when feature is on minus strand.
upstream, downstream

numeric(1). Upstream or downstream region of features to plot.

Details

the bar heatmap is indicates the peaks around features.

Examples

path <- system.file("extdata", package="ChIPpeakAnno")
files <- dir(path, "broadPeak")
peaks <- sapply(file.path(path, files), toGRanges, format="broadPeak")
peaks <- GRangesList(peaks)
names(peaks) <- sub(".broadPeak", "", basename(names(peaks)))
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
metagenePlot(peaks, TxDb.Hsapiens.UCSC.hg19.knownGene)

An example GRanges object representing a ChIP-seq peak dataset

Description

the putative STAT1-binding regions identified in un-stimulated cells using ChIP-seq technology (Robertson et al., 2007)

Usage

myPeakList

Format

GRanges with slot rownames containing the ID of peak as character, slot start containing the start position of the peak, slot end containing the end position of the peak and seqnames containing the chromosome where the peak is located.

Source

Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, et al. (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4:651-7

Examples

data(myPeakList)
slotNames(myPeakList)

get the oligonucleotide frequency

Description

Prepare the oligonucleotide frequency for given Markov order.

Usage

oligoFrequency(sequence, MarkovOrder = 3L)

Arguments

sequence

The sequences packaged in DNAStringSet, DNAString object or output of function getAllPeakSequence.
MarkovOrder

Markov order.

Value

A numeric vector.

Author(s)

Jianhong Ou

See Also

See Also as oligoSummary

Examples

library(seqinr)
    library(Biostrings)
    oligoFrequency(DNAString("AATTCGACGTACAGATGACTAGACT"))

Output a summary of consensus in the peaks

Description

Calculate the z-scores of all combinations of oligonucleotide in a given length by Markove chain.

Usage

oligoSummary(
  sequence,
  oligoLength = 6L,
  freqs = NULL,
  MarkovOrder = 3L,
  quickMotif = FALSE,
  revcomp = FALSE,
  maxsize = 1e+05
)

Arguments

sequence

The sequences packaged in DNAStringSet, DNAString object or output of function getAllPeakSequence.
oligoLength

The length of oligonucleotide.
freqs

Output of function frequency.
MarkovOrder

The order of Markov chain.
quickMotif

Generate the motif by z-score of not.
revcomp

Consider both the given strand and the reverse complement strand when searching for motifs in a complementable alphabet (ie DNA). Default, FALSE.
maxsize

Maximum allowed dataset size (in length of sequences).

Value

A list is returned.

zscore

A numeric vector. The z-scores of each oligonucleotide.
counts

A numeric vector. The counts number of each oligonucleotide.
motifs

a list of motif matrix.

Author(s)

Jianhong Ou

References

van Helden, Jacques, Marcel li del Olmo, and Jose E. Perez-Ortin. "Statistical analysis of yeast genomic downstream sequences reveals putative polyadenylation signals." Nucleic Acids Research 28.4 (2000): 1000-1010.

See Also

See Also as frequency

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
        data(annotatedPeak)
        library(BSgenome.Hsapiens.UCSC.hg19)
        library(seqinr)
        seq <- getAllPeakSequence(annotatedPeak[1:100], 
                     upstream=20, 
                     downstream=20, 
                     genome=Hsapiens)
        oligoSummary(seq)
    }

Permutation Test for two given peak lists

Description

Performs a permutation test to seee if there is an association between two given peak lists.

Usage

peakPermTest(
  peaks1,
  peaks2,
  ntimes = 100,
  seed = as.integer(Sys.time()),
  mc.cores = getOption("mc.cores", 2L),
  maxgap = -1L,
  pool,
  TxDb,
  bindingDistribution,
  bindingType = c("TSS", "geneEnd"),
  featureType = c("transcript", "exon"),
  seqn = NA,
  ...
)

Arguments

peaks1, peaks2

an object of GRanges
ntimes

number of permutations
seed

random seed
mc.cores

The number of cores to use. see mclapply.
maxgap

See findOverlaps in the IRanges package for a description of these arguments.
pool

an object of permPool
TxDb

an object of TxDb
bindingDistribution

an object of bindist
bindingType

where the peaks should bind, TSS or geneEnd
featureType

what annotation type should be used for detecting the binding distribution.
seqn

default is NA, which means not filter the universe pool for sampling. Otherwise the universe pool will be filtered by the seqnames in seqn.
...

further arguments to be passed to numOverlaps.

Value

A list of class permTestResults. See permTest

Author(s)

Jianhong Ou

References

Davison, A. C. and Hinkley, D. V. (1997) Bootstrap methods and their application, Cambridge University Press, United Kingdom, 156-160

See Also

preparePool, bindist

Examples

path <- system.file("extdata", package="ChIPpeakAnno")
    #files <- dir(path, pattern="[12]_WS170.bed", full.names=TRUE)
    #peaks1 <- toGRanges(files[1], skip=5)
    #peaks2 <- toGRanges(files[2], skip=5)
    #peakPermTest(peaks1, peaks2, TxDb=TxDb.Celegans.UCSC.ce6.ensGene)
    if(interactive()){
        peaks1 <- toGRanges(file.path(path, "MACS2_peaks.xls"), 
                            format="MACS2")
        peaks2 <- toGRanges(file.path(path, "peaks.narrowPeak"), 
                            format="narrowPeak")
        library(TxDb.Hsapiens.UCSC.hg19.knownGene)
        peakPermTest(peaks1, peaks2, 
               TxDb=TxDb.Hsapiens.UCSC.hg19.knownGene, min.pctA=10)
    }

Ste12-binding sites from biological replicate 1 in yeast (see reference)

Description

Ste12-binding sites from biological replicate 1 in yeast (see reference)

Usage

Peaks.Ste12.Replicate1

Format

GRanges with slot names containing the ID of peak as character, slot start containing the start position of the peak, slot end containing the end position of the peak and space containing the chromosome where the peak is located.

References

Philippe Lefranois, Ghia M Euskirchen, Raymond K Auerbach, Joel Rozowsky, Theodore Gibson, Christopher M Yellman, Mark Gerstein and Michael Snyder (2009) Efficient yeast ChIP-Seq using multiplex short-read DNA sequencing BMC Genomics 10:37

Examples

data(Peaks.Ste12.Replicate1)
Peaks.Ste12.Replicate1

Ste12-binding sites from biological replicate 2 in yeast (see reference)

Description

Ste12-binding sites from biological replicate 2 in yeast (see reference)

Usage

Peaks.Ste12.Replicate2

Format

GRanges with slot names containing the ID of peak as character, slot start containing the start position of the peak, slot end containing the end position of the peak and space containing the chromosome where the peak is located.

Source

http://www.biomedcentral.com/1471-2164/10/37

References

Philippe Lefranois, Ghia M Euskirchen, Raymond K Auerbach, Joel Rozowsky, Theodore Gibson, Christopher M Yellman, Mark Gerstein and Michael Snyder (2009) Efficient yeast ChIP-Seq using multiplex short-read DNA sequencing BMC Genomics 10:37doi:10.1186/1471-2164-10-37

Examples

data(Peaks.Ste12.Replicate2)
Peaks.Ste12.Replicate2

Ste12-binding sites from biological replicate 3 in yeast (see reference)

Description

Ste12-binding sites from biological replicate 3 in yeast (see reference)

Usage

Peaks.Ste12.Replicate3

Format

GRanges with slot names containing the ID of peak as character, slot start containing the start position of the peak, slot end containing the end position of the peak and space containing the chromosome where the peak is located.

Source

http://www.biomedcentral.com/1471-2164/10/37

References

Philippe Lefranois, Ghia M Euskirchen, Raymond K Auerbach, Joel Rozowsky, Theodore Gibson, Christopher M Yellman, Mark Gerstein and Michael Snyder (2009) Efficient yeast ChIP-Seq using multiplex short-read DNA sequencing BMC Genomics 10:37doi:10.1186/1471-2164-10-37

Examples

data(Peaks.Ste12.Replicate3)
Peaks.Ste12.Replicate3

An example GRanges object representing a ChIP-seq peak dataset

Description

An example GRanges object representing a ChIP-seq peak dataset

Usage

peaks1

Format

GRanges

Examples

data(peaks1)
head(peaks1, n = 2)

An example GRanges object representing a ChIP-seq peak dataset

Description

An example GRanges object representing a ChIP-seq peak dataset

Usage

peaks2

Format

GRanges

Examples

data(peaks2)
head(peaks2, n = 2)

An example GRanges object representing a ChIP-seq peak dataset

Description

An example GRanges object representing a ChIP-seq peak dataset

Usage

peaks3

Format

GRanges

Examples

data(peaks3)
head(peaks3, n = 2)

obtain the peaks near bi-directional promoters

Description

Obtain the peaks near bi-directional promoters. Also output percent of peaks near bi-directional promoters.

Usage

peaksNearBDP(myPeakList, AnnotationData, MaxDistance = 5000L, ...)

Arguments

myPeakList

GRanges: See example below
AnnotationData

annotation data obtained from getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). For example, data(TSS.human.NCBI36), data(TSS.mouse.NCBIM37), data(TSS.rat.RGSC3.4) and data(TSS.zebrafish.Zv8).
MaxDistance

Specify the maximum gap allowed between the peak and nearest gene
...

Not used

Value

A list of 4

list("peaksWithBDP")

annotated Peaks containing bi-directional promoters.

GRangesList with slot start holding the start position of the peak, slot end holding the end position of the peak, slot space holding the chromosome location where the peak is located, slot rownames holding the id of the peak. In addition, the following variables are included.

feature: id of the feature such as ensembl gene ID

insideFeature: upstream: peak resides upstream of the feature; downstream: peak resides downstream of the feature; inside: peak resides inside the feature; overlapStart: peak overlaps with the start of the feature; overlapEnd: peak overlaps with the end of the feature; includeFeature: peak include the feature entirely.

distancetoFeature: distance to the nearest feature such as transcription start site. By default, the distance is calculated as the distance between the start of the binding site and the TSS that is the gene start for genes located on the forward strand and the gene end for genes located on the reverse strand. The user can specify the location of peak and location of feature for calculating this

feature_range: start and end position of the feature such as gene

feature_strand: 1 or + for positive strand and -1 or - for negative strand where the feature is located

list("percentPeaksWithBDP")

The percent of input peaks containing bi-directional promoters
list("n.peaks")

The total number of input peaks
list("n.peaksWithBDP")

The # of input peaks containing bi-directional promoters

Author(s)

Lihua Julie Zhu, Jianhong Ou

References

Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237

See Also

annotatePeakInBatch, findOverlappingPeaks, makeVennDiagram

Examples

if (interactive() || Sys.getenv("USER")=="jianhongou")
{
    data(myPeakList)
    data(TSS.human.NCBI36)
    seqlevelsStyle(TSS.human.NCBI36) <- seqlevelsStyle(myPeakList)
    annotatedBDP = peaksNearBDP(myPeakList[1:6,], 
                                AnnotationData=TSS.human.NCBI36,
                                MaxDistance=5000,
                                PeakLocForDistance =  "middle", 
                                FeatureLocForDistance = "TSS")
    c(annotatedBDP$percentPeaksWithBDP, annotatedBDP$n.peaks, 
      annotatedBDP$n.peaksWithBDP)
}

Class "permPool"

Description

An object of class "permPool" represents the possible locations to do permutation test.

Slots

grs

object of "GRangesList" The list of binding ranges

N

vector of "integer", permutation number for each ranges

Objects from the Class

Objects can be created by calls of the form new("permPool", grs="GRangesList", N="integer").

See Also

preparePool, peakPermTest

Pie Charts

Description

Draw a pie chart with percentage

Usage

pie1(
  x,
  labels = names(x),
  edges = 200,
  radius = 0.8,
  clockwise = FALSE,
  init.angle = if (clockwise) 90 else 0,
  density = NULL,
  angle = 45,
  col = NULL,
  border = NULL,
  lty = NULL,
  main = NULL,
  percentage = TRUE,
  rawNumber = FALSE,
  digits = 3,
  cutoff = 0.01,
  legend = FALSE,
  legendpos = "topright",
  legendcol = 2,
  radius.innerlabel = radius,
  ...
)

Arguments

x

a vector of non-negative numerical quantities. The values in x are displayed as the areas of pie slices.
labels

one or more expressions or character strings giving names for the slices. Other objects are coerced by as.graphicsAnnot. For empty or NA (after coercion to character) labels, no label nor pointing line is drawn.
edges

the circular outline of the pie is approximated by a polygon with this many edges.
radius

the pie is drawn centered in a square box whose sides range from -1 to 1. If the character strings labeling the slices are long it may be necessary to use a smaller radius.
clockwise

logical indicating if slices are drawn clockwise or counter clockwise (i.e., mathematically positive direction), the latter is default.
init.angle

number specifying the starting angle (in degrees) for the slices. Defaults to 0 (i.e., "3 o'clock") unless clockwise is true where init.angle defaults to 90 (degrees), (i.e., "12 o'clock").
density

the density of shading lines, in lines per inch. The default value of NULL means that no shading lines are drawn. Non-positive values of density also inhibit the drawing of shading lines.
angle

the slope of shading lines, given as an angle in degrees (counter-clockwise).
col

a vector of colors to be used in filling or shading the slices. If missing a set of 6 pastel colours is used, unless density is specified when par("fg") is used.
border, lty

(possibly vectors) arguments passed to polygon which draws each slice.
main

an overall title for the plot.
percentage

logical. Add percentage in the figure or not. default TRUE.
rawNumber

logical. Instead percentage, add raw number in the figure or not. default FALSE.
digits

When set percentage as TRUE, how many significant digits are to be used for percentage. see format. default 3.
cutoff

When percentage is TRUE, if the percentage is lower than cutoff, it will NOT be shown. default 0.01.
legend

logical. Instead of lable, draw legend for the pie. default, FALSE.
legendpos, legendcol

legend position and legend columns. see legend
radius.innerlabel

position of percentage or raw number label relative to the circle.
...

graphical parameters can be given as arguments to pie. They will affect the main title and labels only.

Author(s)

Jianhong Ou

See Also

pie

Examples

pie1(1:5)

plot the coverage of regions

Description

plot the output of binOverRegions or binOverGene

Usage

plotBinOverRegions(dat, ...)

Arguments

dat

A list of matrix which indicate the coverage of regions per bin
...

Parameters could be used by matplot

Author(s)

Jianhong Ou

See Also

binOverRegions, binOverGene

Examples

if(interactive()){
path <- system.file("extdata", package="ChIPpeakAnno")
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(rtracklayer)
files <- dir(path, "bigWig")
if(.Platform$OS.type != "windows"){
cvglists <- lapply(file.path(path, files), import,
                   format="BigWig", as="RleList")
names(cvglists) <- sub(".bigWig", "", files)
d <- binOverGene(cvglists, TxDb.Hsapiens.UCSC.hg19.knownGene)
plotBinOverRegions(d)
}
}

prepare data for permutation test

Description

prepare data for permutation test peakPermTest

Usage

preparePool(
  TxDb,
  template,
  bindingDistribution,
  bindingType = c("TSS", "geneEnd"),
  featureType = c("transcript", "exon"),
  seqn = NA
)

Arguments

TxDb

an object of TxDb
template

an object of GRanges
bindingDistribution

an object of bindist
bindingType

the relevant position to features
featureType

feature type, transcript or exon.
seqn

seqnames. If given, the pool for permutation will be restrict in the given chromosomes.

Value

a list with two elements, grs, a list of GRanges. N, the numbers of elements should be drawn from in each GRanges.

Author(s)

Jianhong Ou

See Also

peakPermTest, bindist

Examples

if(interactive() || Sys.getenv("USER")=="jianhongou"){
        path <- system.file("extdata", package="ChIPpeakAnno")
        peaksA <- toGRanges(file.path(path, "peaks.narrowPeak"), 
                            format="narrowPeak")
        peaksB <- toGRanges(file.path(path, "MACS2_peaks.xls"), format="MACS2")
        library(TxDb.Hsapiens.UCSC.hg19.knownGene)
        ppp <- preparePool(TxDb.Hsapiens.UCSC.hg19.knownGene, 
                           peaksA, bindingType="TSS",
                           featureType="transcript")
    }

re-center the peaks

Description

Create a new list of peaks based on the peak centers of given list.

Usage

reCenterPeaks(peaks, width = 2000L, ...)

Arguments

peaks

An object of GRanges or annoGR.
width

The width of new peaks
...

Not used.

Value

An object of GRanges.

Author(s)

Jianhong Ou

Examples

reCenterPeaks(GRanges("chr1", IRanges(1, 10)), width=2)

Perform overlap queries between reads and genomic features by bins

Description

summarizeOverlapsByBins extends summarizeOverlaps by providing fixed window size and step to split each feature into bins and then do queries. It will return counts by signalSummaryFUN, which applied to bins in one feature, for each feature.

Usage

summarizeOverlapsByBins(
  targetRegions,
  reads,
  windowSize = 50,
  step = 10,
  signalSummaryFUN = max,
  mode = countByOverlaps,
  ...
)

Arguments

targetRegions

A GRanges object of genomic regions of interest.
reads

A GRanges, GRangesList GAlignments, GAlignmentsList, GAlignmentPairs or BamFileList object that represents the data to be counted by summarizeOverlaps.
windowSize

Size of windows
step

Step of windows
signalSummaryFUN

function, which will be applied to the bins in each feature.
mode

mode can be one of the pre-defined count methods. see summarizeOverlaps. default is countByOverlaps, alia of countOverlaps(features, reads, ignore.strand=ignore.strand)
...

Additional arguments passed to summarizeOverlaps.

Value

A RangedSummarizedExperiment object. The assays slot holds the counts, rowRanges holds the annotation from features.

Author(s)

Jianhong Ou

Examples

fls <- list.files(system.file("extdata", package="GenomicAlignments"),
                  recursive=TRUE, pattern="*bam$", full=TRUE)
    names(fls) <- basename(fls)
    genes <- GRanges(
        seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)),
        ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600, 
                           4000, 7500, 5000, 5400), 
                         width=c(rep(500, 3), 600, 900, 500, 300, 900, 
                                 300, 500, 500),
                         names=letters[1:11])) 
    se <- summarizeOverlapsByBins(genes, fls, windowSize=50, step=10)

Output a summary of the occurrence and enrichment of each pattern in the sequences.

Description

Output a summary of the occurrence and enrichment of each pattern in the sequences.

Usage

summarizePatternInPeaks(
  patternFilePath,
  format = "fasta",
  BSgenomeName,
  peaks,
  revcomp = TRUE,
  method = c("binom.test", "permutation.test"),
  expectFrequencyMethod = c("Markov", "Naive"),
  MarkovOrder = 3L,
  bgdForPerm = c("shuffle", "chromosome"),
  chromosome = c("asPeak", "random"),
  nperm = 1000,
  alpha = 0.05,
  ...
)

Arguments

patternFilePath

Character value. The path to the file that contains the pattern.
format

Character value. The format of file containing the oligonucleotide pattern, either "fasta" (default) or "fastq".
BSgenomeName

Character value. BSgenome object. Please refer to available.genomes in BSgenome package for details.
peaks

Character value. GRanges containing the peaks.
revcomp

Boolean value, if TURE, also search the reverse compliment of pattern. Default is TRUE.
method

Character value. Method for pattern enrichment test, 'binom.test' (default) or 'permutation.test'.
expectFrequencyMethod

Character value. Method for calculating the expected probability of pattern occurrence, 'Markov' (default) or 'Naive'.
MarkovOrder

Integer value. The order of Markov chain. Default is 3.
bgdForPerm

Character value. The method for obtaining the background sequence. 'chromosome' (default) selects background chromosome from chromosomes, refer to 'chromosome' parameter; 'shuffle' will obtain the backgroud sequence by shufflubg any k-mers in peak sequences, refer to '...'.
chromosome

Character value. Relevant if "bgdForPerm='chromosome'". 'asPeak' means to use the same chromosomes in peaks; 'random' means to use all chromosomes randomly. Default is 'asPeak'.
nperm

Integer value. The number of permutation test, default is 1000.
alpha

Numeric value. The significant level for permutation test, default is 0.05.
...

Aditional parameter passed to function shuffle_sequences

Details

Please see shuffle_sequences for the more information bout 'shuffle' method.

Value

A list including two data frames named 'motif_enrichment' and 'motif_occurrence'. The 'motif_enrichment' has four columns:

  • "patternNum": number of matched pattern

  • "totalNumPatternWithSameLen": total number of pattern with the same length

  • "expectedRate": expected rate of pattern for 'binom.test' method

  • "patternRate": real rate of pattern for 'permutation.test' method

  • "pValueBinomTest": p value of bimom test for 'binom.test' method

  • "cutOffPermutationTest": cut off of permutation test for 'permutation.test' method

The 'motif_occurrence' has 14 columns:

  • "motifChr": Chromosome of motif

  • "motifStartInChr": motif start position in chromosome

  • "motifEndInChr": motif end position in chromosome

  • "motifName": motif name

  • "motifPattern": motif pattern

  • "motifStartInPeak": motif start position in peak

  • "motifEndInPeak": motif end position in peak

  • "motifFound": specific motif Found in peak

  • "motifFoundStrand": strand of specific motif Found in peak, "-" means reverse complement of motif found in peaks

  • "peakChr": Chromosome of peak

  • "peakStart": peak start position

  • "peakEnd": peak end position

  • "peakWidth": peak width

  • "peakStrand": peak strand

Author(s)

Lihua Julie Zhu, Junhui Li, Kai Hu

Examples

library(BSgenome.Hsapiens.UCSC.hg19)
filepath <- system.file("extdata", "examplePattern.fa", 
                        package = "ChIPpeakAnno")
peaks <- GRanges(seqnames = c("chr17", "chr3", "chr12", "chr8"),
                 IRanges(start = c(41275784, 10076141, 4654135, 31024288),
                         end = c(41276382, 10076732, 4654728, 31024996),
                         names = paste0("peak", 1:4)))
result <- summarizePatternInPeaks(patternFilePath = filepath, peaks = peaks,
                                  BSgenomeName = Hsapiens)

Perform overlap queries between reads and genome by windows

Description

tileCount extends summarizeOverlaps by providing fixed window size and step to split whole genome into windows and then do queries. It will return counts in each windows.

Usage

tileCount(
  reads,
  genome,
  windowSize = 1e+06,
  step = 1e+06,
  keepPartialWindow = FALSE,
  mode = countByOverlaps,
  ...
)

Arguments

reads

A GRanges, GRangesList GAlignments, GAlignmentsList, GAlignmentPairs or BamFileList object that represents the data to be counted by summarizeOverlaps.
genome

The object from/on which to get/set the sequence information.
windowSize

Size of windows
step

Step of windows
keepPartialWindow

Keep last partial window or not.
mode

mode can be one of the pre-defined count methods. see summarizeOverlaps. default is countByOverlaps, alia of countOverlaps(features, reads, ignore.strand=ignore.strand)
...

Additional arguments passed to summarizeOverlaps.

Value

A RangedSummarizedExperiment object. The assays slot holds the counts, rowRanges holds the annotation from genome.

Author(s)

Jianhong Ou

Examples

fls <- list.files(system.file("extdata", package="GenomicAlignments"),
                  recursive=TRUE, pattern="*bam$", full=TRUE)
    names(fls) <- basename(fls)
    genes <- GRanges(seqlengths = c(chr2L=7000, chr2R=10000))
    se <- tileCount(fls, genes, windowSize=1000, step=500)

Slide windows on a given GRanges object

Description

tileGRanges returns a set of genomic regions by sliding the windows in a given step. Each window is called a "tile".

Usage

tileGRanges(targetRegions, windowSize, step, keepPartialWindow = FALSE, ...)

Arguments

targetRegions

A GRanges object of genomic regions of interest.
windowSize

Size of windows
step

Step of windows
keepPartialWindow

Keep last partial window or not.
...

Not used.

Value

A GRanges object.

Author(s)

Jianhong Ou

Examples

genes <- GRanges(
        seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)),
        ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600, 
                           4000, 7500, 5000, 5400), 
                         width=c(rep(500, 3), 600, 900, 500, 300, 900, 
                                 300, 500, 500),
                         names=letters[1:11])) 
    se <- tileGRanges(genes, windowSize=50, step=10)

Convert dataset to GRanges

Description

Convert UCSC BED format and its variants, such as GFF, or any user defined dataset such as MACS output file to GRanges

Usage

toGRanges(data, ...)

## S4 method for signature 'connection'
toGRanges(
  data,
  format = c("BED", "GFF", "GTF", "MACS", "MACS2", "MACS2.broad", "narrowPeak",
    "broadPeak", "CSV", "others"),
  header = FALSE,
  comment.char = "#",
  colNames = NULL,
  ...
)

## S4 method for signature 'TxDb'
toGRanges(
  data,
  feature = c("gene", "transcript", "exon", "CDS", "fiveUTR", "threeUTR", "microRNA",
    "tRNAs", "geneModel"),
  OrganismDb,
  ...
)

## S4 method for signature 'EnsDb'
toGRanges(
  data,
  feature = c("gene", "transcript", "exon", "disjointExons"),
  ...
)

## S4 method for signature 'character'
toGRanges(
  data,
  format = c("BED", "GFF", "GTF", "MACS", "MACS2", "MACS2.broad", "narrowPeak",
    "broadPeak", "CSV", "others"),
  header = FALSE,
  comment.char = "#",
  colNames = NULL,
  ...
)

Arguments

data

an object of data.frame, TxDb or EnsDb, or the file name of data to be imported. Alternatively, data can be a readable txt-mode connection (See ?read.table).
...

parameters passed to read.table
format

data format. If the data format is set to BED, GFF, narrowPeak or broadPeak, please refer to http://genome.ucsc.edu/FAQ/FAQformat#format1 for column order. "MACS" is for converting the excel output file from MACS1. "MACS2" is for converting the output file from MACS2. If set to CSV, must have columns: seqnames, start, end, strand.
header

A logical value indicating whether the file contains the names of the variables as its first line. If missing, the value is determined from the file format: header is set to TRUE if the first row contains one fewer field than the number of columns or the format is set to 'CSV'.
comment.char

character: a character vector of length one containing a single character or an empty string. Use "" to turn off the interpretation of comments altogether.
colNames

If the data format is set to "others", colname must be defined. And the colname must contain space, start and end. The column name for the chromosome # should be named as space.
feature

annotation type
OrganismDb

an object of OrganismDb. It is used for extracting gene symbol for geneModel group for TxDb

Value

An object of GRanges

Author(s)

Jianhong Ou

Examples

macs <- system.file("extdata", "MACS_peaks.xls", package="ChIPpeakAnno")
  macsOutput <- toGRanges(macs, format="MACS")
  if(interactive() || Sys.getenv("USER")=="jianhongou"){
    ## MACS connection
    macs <- readLines(macs)
    macs <- textConnection(macs)
    macsOutput <- toGRanges(macs, format="MACS")
    close(macs)
    ## bed
    toGRanges(system.file("extdata", "MACS_output.bed", package="ChIPpeakAnno"),
                format="BED")
    ## narrowPeak
    toGRanges(system.file("extdata", "peaks.narrowPeak", package="ChIPpeakAnno"),
                format="narrowPeak")
    ## broadPeak
    toGRanges(system.file("extdata", "TAF.broadPeak", package="ChIPpeakAnno"),
                format="broadPeak")
    ## CSV
    toGRanges(system.file("extdata", "peaks.csv", package="ChIPpeakAnno"),
                format="CSV")
    ## MACS2
    toGRanges(system.file("extdata", "MACS2_peaks.xls", package="ChIPpeakAnno"),
                format="MACS2")
    ## GFF
    toGRanges(system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno"),
                format="GFF")
    ## EnsDb
    library(EnsDb.Hsapiens.v75)
    toGRanges(EnsDb.Hsapiens.v75, feature="gene")
    ## TxDb
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    toGRanges(TxDb.Hsapiens.UCSC.hg19.knownGene, feature="gene")
    ## data.frame
    macs <- system.file("extdata", "MACS_peaks.xls", package="ChIPpeakAnno")
    macs <- read.delim(macs, comment.char="#")
    toGRanges(macs)
  }

translate pattern from IUPAC Extended Genetic Alphabet to regular expression

Description

translate pattern containing the IUPAC nucleotide ambiguity codes to regular expression. For example,Y->[C|T], R-> [A|G], S-> [G|C], W-> [A|T], K-> [T|U|G], M-> [A|C], B-> [C|G|T], D-> [A|G|T], H-> [A|C|T], V-> [A|C|G] and N-> [A|C|T|G].

Usage

translatePattern(pattern)

Arguments

pattern

a character vector with the IUPAC nucleotide ambiguity codes

Value

a character vector with the pattern represented as regular expression

Author(s)

Lihua Julie Zhu

See Also

countPatternInSeqs, summarizePatternInPeaks

Examples

pattern1 = "AACCNWMK"
    translatePattern(pattern1)

TSS annotation for human sapiens (GRCh37) obtained from biomaRt

Description

TSS annotation for human sapiens (GRCh37) obtained from biomaRt

Usage

TSS.human.GRCh37

Format

A GRanges object with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

The dataset TSS.human.GRCh37 was obtained by:

mart = useMart(biomart = "ENSEMBL_MART_ENSEMBL", host="grch37.ensembl.org", path="/biomart/martservice", dataset = "hsapiens_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.human.GRCh37)
slotNames(TSS.human.GRCh37)

TSS annotation for human sapiens (GRCh38) obtained from biomaRt

Description

TSS annotation for human sapiens (GRCh38) obtained from biomaRt

Usage

TSS.human.GRCh38

Format

A 'GRanges' [package "GenomicRanges"] object with ensembl id as names.

Details

used in the examples Annotation data obtained by:

mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.human.GRCh38)
slotNames(TSS.human.GRCh38)

TSS annotation for human sapiens (NCBI36) obtained from biomaRt

Description

TSS annotation for human sapiens (NCBI36) obtained from biomaRt

Usage

TSS.human.NCBI36

Format

GRanges with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

used in the examples Annotation data obtained by:

mart = useMart(biomart = "ensembl_mart_47", dataset = "hsapiens_gene_ensembl", archive=TRUE)

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.human.NCBI36)
slotNames(TSS.human.NCBI36)

TSS annotation data for Mus musculus (GRCm38.p1) obtained from biomaRt

Description

TSS annotation data for Mus musculus (GRCm38.p1) obtained from biomaRt

Usage

TSS.mouse.GRCm38

Format

GRanges with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

Annotation data obtained by:

mart = useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.mouse.GRCm38)
slotNames(TSS.mouse.GRCm38)

TSS annotation data for mouse (NCBIM37) obtained from biomaRt

Description

TSS annotation data for mouse (NCBIM37) obtained from biomaRt

Usage

TSS.mouse.NCBIM37

Format

GRanges with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

Annotation data obtained by:

mart = useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.mouse.NCBIM37)
slotNames(TSS.mouse.NCBIM37)

TSS annotation data for rat (RGSC3.4) obtained from biomaRt

Description

TSS annotation data for rat (RGSC3.4) obtained from biomaRt

Usage

TSS.rat.RGSC3.4

Format

GRanges with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

Annotation data obtained by:

mart = useMart(biomart = "ensembl", dataset = "rnorvegicus_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.rat.RGSC3.4)
slotNames(TSS.rat.RGSC3.4)

TSS annotation data for Rattus norvegicus (Rnor_5.0) obtained from biomaRt

Description

TSS annotation data for Rattus norvegicus (Rnor_5.0) obtained from biomaRt

Usage

TSS.rat.Rnor_5.0

Format

GRanges with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

Annotation data obtained by:

mart = useMart(biomart = "ensembl", dataset = "rnorvegicus_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.rat.Rnor_5.0)
slotNames(TSS.rat.Rnor_5.0)

TSS annotation data for zebrafish (Zv8) obtained from biomaRt

Description

A GRanges object to annotate TSS for zebrafish (Zv8) obtained from biomaRt

Usage

TSS.zebrafish.Zv8

Format

GRanges with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

Annotation data obtained by: mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="may2009.archive.ensembl.org", path="/biomart/martservice", dataset="drerio_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.zebrafish.Zv8)
slotNames(TSS.zebrafish.Zv8)

TSS annotation for Danio rerio (Zv9) obtained from biomaRt

Description

TSS annotation for Danio rerio (Zv9) obtained from biomaRt

Usage

TSS.zebrafish.Zv9

Format

GRanges with slot start holding the start position of the gene, slot end holding the end position of the gene, slot names holding ensembl gene id, slot seqnames holding the chromosome location where the gene is located and slot strand holding the strinad information. In addition, the following variables are included.

list("description")

description of the gene

Details

Annotation data obtained by:

mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="mar2015.archive.ensembl.org", path="/biomart/martservice", dataset="drerio_gene_ensembl")

getAnnotation(mart, featureType = "TSS")

Examples

data(TSS.zebrafish.Zv9)
slotNames(TSS.zebrafish.Zv9)

TxDb object to GRanges

Description

convert TxDb object to GRanges

Usage

TxDb2GR(ranges, feature, OrganismDb)

Arguments

ranges

an Txdb object
feature

feature type, could be geneModel, gene, exon, transcript, CDS, fiveUTR, threeUTR, microRNA, and tRNA
OrganismDb

org db object

transcription factor binding site clusters (V3) from ENCODE

Description

possible binding pool for human (hg19) from transcription factor binding site clusters (V3) from ENCODE data and removed the HOT spots

Usage

wgEncodeTfbsV3

Format

An object of GRanges.

Details

How to generate the data:

temp <- tempfile()

download.file(file.path("http://hgdownload.cse.ucsc.edu", "goldenPath",

"hg19", "encodeDCC",

"wgEncodeRegTfbsClustered",

"wgEncodeRegTfbsClusteredV3.bed.gz"), temp)

data <- read.delim(gzfile(temp, "r"), header=FALSE)

unlink(temp)

colnames(data)[1:4] <- c("seqnames", "start", "end", "TF")

wgEncodeRegTfbsClusteredV3 <- GRanges(as.character(data$seqnames),

IRanges(data$start, data$end),

TF=data$TF)

data(HOT.spots)

hot <- reduce(unlist(HOT.spots))

ol <- findOverlaps(wgEncodeRegTfbsClusteredV3, hot)

wgEncodeTfbsV3 <- wgEncodeRegTfbsClusteredV3[-unique(queryHits(ol))]

wgEncodeTfbsV3 <- reduce(wgEncodeTfbsV3)

save(list="wgEncodeTfbsV3",

file="data/wgEncodeTfbsV3.rda",

compress="xz", compression_level=9)

Source

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/ wgEncodeRegTfbsClustered/wgEncodeRegTfbsClusteredV3.bed.gz

Examples

data(wgEncodeTfbsV3)
head(wgEncodeTfbsV3)

Write sequences to a file in fasta format

Description

Write the sequences obtained from getAllPeakSequence to a file in fasta format leveraging writeFASTA in Biostrings package. FASTA is a simple file format for biological sequence data. A FASTA format file contains one or more sequences and there is a header line which begins with a > proceeding each sequence.

Usage

write2FASTA(mySeq, file = "", width = 80)

Arguments

mySeq

GRanges with varibles name and sequence ,e.g., results obtained from getAllPeakSequence
file

Either a character string naming a file or a connection open for reading or writing. If "" (the default for write2FASTA), then the function writes to the standard output connection (the console) unless redirected by sink
width

The maximum number of letters per line of sequence

Value

Output as FASTA file format to the naming file or the console.

Author(s)

Lihua Julie Zhu

Examples

peaksWithSequences = GRanges(seqnames=c("1", "2"),
IRanges(start=c(1000, 2000), 
end=c(1010, 2010), 
names=c("id1", "id2")), 
sequence= c("CCCCCCCCGGGGG", "TTTTTTTAAAAAA"))

write2FASTA(peaksWithSequences, file="testseq.fasta", width=50)

Return the value from a Bimap objects

Description

Search by name for an Bimap object.

Usage

xget(
  x,
  envir,
  mode,
  ifnotfound = NA,
  inherits,
  output = c("all", "first", "last")
)

Arguments

x, envir, mode, ifnotfound, inherits

see mget
output

return the all or first item for each query

Value

a character vector

Author(s)

Jianhong Ou

See Also

See Also as mget, mget

Examples

library(org.Hs.eg.db)
    xget(as.character(1:10), org.Hs.egSYMBOL)