Author: Malcolm Perry
Date: 2026-04-28

# Package Vignette for Genomic Interactions: ChIA-PET data

#### Malcolm Perry

#### 2026-04-28

## ChIA-PET

Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) is a recent method to study protein-mediated interactions at a genome-wide scale. Like most techniques for studying chromatin interaction it is based on chromosome conformation capture technology. Unlike 3C, 4C and 5C, however, it can detect interactions genome-wide, and includes a ChIP step to purify interactions involving a protein of interest.

The raw data from ChIA-PET is in the form of paired-end reads attached to one of two linker sequences. Reads with chimeric linkers are removed, and the data is aligned to the reference genome. The ChIA-PET tool can then be used to find pairs of regions (“anchors”) which have a significant number of reads mapping between them and therefore represent biologically meaningful chromatin interactions in the sample.

## Imports

First we need to load the GenomicInteractions package, and the mm9 reference genome:

    library(GenomicInteractions)

    ## Loading required package: InteractionSet

    ## Loading required package: GenomicRanges

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    library(InteractionSet)
    library(GenomicRanges)

## Data

We can then read in our data directly from the output of the ChIA-PET tool. At this stage we can also provide information about the cell type and a description tag for the experiment. The data is taken from Li et al., 2012, published in Cell. They have used antibodies against the initiation form of Pol II, which you would expect to find at active promoters, and we are looking at data from the K562 myelogenous leukemia cell line. The data should therefore give us an insight into the processes which regulate genes that are being actively transcribed.

    chiapet.data = system.file("extdata/k562.rep1.cluster.pet3+.txt",
                               package="GenomicInteractions")

    k562.rep1 = makeGenomicInteractionsFromFile(chiapet.data,
                                    type="chiapet.tool",
                                    experiment_name="k562",
                                    description="k562 pol2 8wg16")

This loads the data into a `GenomicInteractions` object, which consists of two linked `GenomicRanges` objects containing the anchors in each interaction, as well as the p-value, FDR and the number of reads supporting each interaction.

## GenomicInteractions Objects

The metadata we have added can easily be accesed, and edited:

    name(k562.rep1)

    ## [1] "k562"

    description(k562.rep1) = "PolII-8wg16 Chia-PET for K562"

As can the data from the ChIA-PET experiment:

    head(interactionCounts(k562.rep1))

    ## [1]   3 562   3   3   3   3

    head((k562.rep1)$fdr)

    ## [1] 1.25703e-10 0.00000e+00 1.17148e-06 4.86859e-08 2.76777e-08 3.97019e-08

    hist(-log10(k562.rep1$p.value))

The two linked `GRanges` objects can be returned, but not altered in-place:

    anchorOne(k562.rep1)

    ## GRanges object with 64565 ranges and 0 metadata columns:
    ##           seqnames              ranges strand
    ##              <Rle>           <IRanges>  <Rle>
    ##       [1]     chr1       569922-571422      *
    ##       [2]     chr1       832761-905482      *
    ##       [3]     chr1       839092-842325      *
    ##       [4]     chr1       839393-841792      *
    ##       [5]     chr1       852731-855234      *
    ##       ...      ...                 ...    ...
    ##   [64561]     chrX 154432946-154435728      *
    ##   [64562]     chrX 154436728-154439876      *
    ##   [64563]     chrX 154439789-154442306      *
    ##   [64564]     chrX 154459648-154462031      *
    ##   [64565]     chrX 154839050-154843949      *
    ##   -------
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

    anchorTwo(k562.rep1)

    ## GRanges object with 64565 ranges and 0 metadata columns:
    ##           seqnames              ranges strand
    ##              <Rle>           <IRanges>  <Rle>
    ##       [1]     chrM          8342-10675      *
    ##       [2]     chr1       838470-920603      *
    ##       [3]     chr1       935528-939051      *
    ##       [4]     chr1       955081-956755      *
    ##       [5]     chr1       933685-937006      *
    ##       ...      ...                 ...    ...
    ##   [64561]     chrX 154442294-154446983      *
    ##   [64562]     chrX 154442540-154445105      *
    ##   [64563]     chrX 154448371-154451728      *
    ##   [64564]     chrX 154469339-154471852      *
    ##   [64565]     chrX 154843728-154848393      *
    ##   -------
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

`GenomicInteractions` objects can easily handle interactions detected between chromosomes, known as trans-chromosomal interactions, since the anchors can be at any point along the genome. `is.trans` returns a logical vector; likewise `is.cis` is the opposite of this function.

    sprintf("Percentage of trans-chromosomal interactions %.2f",
            100*sum(is.trans(k562.rep1))/length(k562.rep1))

    ## [1] "Percentage of trans-chromosomal interactions 1.00"

The length of each interaction is not stored as metadata, but we can calculate the distance of each interaction using either the inner edge, outer edge or midpoints of the anchors. This is undefined for inter-chromosomal interactions, so NA is returned, so it is important to exclude these interactions from some analyses.

    head(calculateDistances(k562.rep1, method="midpoint"))

    ## [1]     NA  10415  96581 115325  81363  79098

`GenomicRanges` objects can be subsetted by either integer or logical vectors like most R objects, and also BioConductor `Rle` objects.

    k562.rep1[1:10] # first interactions in the dataset

    ## GenomicInteractions object with 10 interactions and 3 metadata columns:
    ##        seqnames1       ranges1     seqnames2       ranges2 |    counts
    ##            <Rle>     <IRanges>         <Rle>     <IRanges> | <integer>
    ##    [1]      chr1 569922-571422 ---      chrM    8342-10675 |         3
    ##    [2]      chr1 832761-905482 ---      chr1 838470-920603 |       562
    ##    [3]      chr1 839092-842325 ---      chr1 935528-939051 |         3
    ##    [4]      chr1 839393-841792 ---      chr1 955081-956755 |         3
    ##    [5]      chr1 852731-855234 ---      chr1 933685-937006 |         3
    ##    [6]      chr1 855856-858861 ---      chr1 935669-937245 |         3
    ##    [7]      chr1 874165-879175 ---      chr1 933340-938306 |        10
    ##    [8]      chr1 874190-877867 ---      chr1 955674-959630 |         5
    ##    [9]      chr1 889676-896594 ---      chr1 933897-938982 |        13
    ##   [10]      chr1 898753-907581 ---      chr1 931133-939571 |        19
    ##            p.value         fdr
    ##          <numeric>   <numeric>
    ##    [1] 1.62140e-12 1.25703e-10
    ##    [2] 0.00000e+00 0.00000e+00
    ##    [3] 4.21364e-08 1.17148e-06
    ##    [4] 1.45938e-09 4.86859e-08
    ##    [5] 7.85539e-10 2.76777e-08
    ##    [6] 1.16802e-09 3.97019e-08
    ##    [7] 1.23139e-25 3.58932e-23
    ##    [8] 6.63691e-15 6.98795e-13
    ##    [9] 4.91311e-36 2.33753e-33
    ##   [10] 0.00000e+00 0.00000e+00
    ##   -------
    ##   regions: 129090 ranges and 0 metadata columns
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

    k562.rep1[sample(length(k562.rep1), 100)] # 100 interactions subsample

    ## GenomicInteractions object with 100 interactions and 3 metadata columns:
    ##         seqnames1             ranges1     seqnames2             ranges2 |
    ##             <Rle>           <IRanges>         <Rle>           <IRanges> |
    ##     [1]     chr10   49584975-49587441 ---     chr10   49590002-49593282 |
    ##     [2]     chr16   56482876-56486689 ---     chr16   56552316-56555456 |
    ##     [3]     chr10 112326575-112331257 ---     chr10 112329166-112338154 |
    ##     [4]      chr7 157451714-157454521 ---      chr7 157456768-157459804 |
    ##     [5]      chr1   36813659-36816300 ---      chr1   36839045-36842903 |
    ##     ...       ...                 ... ...       ...                 ... .
    ##    [96]     chr11   61675096-61677190 ---     chr11   61681005-61683350 |
    ##    [97]     chr12 120907343-120910344 ---     chr12 120965426-120968868 |
    ##    [98]     chr19   12898952-12904878 ---     chr19   13261459-13267783 |
    ##    [99]     chr10 127571672-127574503 ---     chr10 127578738-127581888 |
    ##   [100]      chr1   27881178-27884186 ---      chr1   27922193-27924677 |
    ##            counts     p.value         fdr
    ##         <integer>   <numeric>   <numeric>
    ##     [1]         3 8.70722e-15 8.99407e-13
    ##     [2]         3 6.60533e-12 4.51206e-10
    ##     [3]        11 7.56701e-43 4.50239e-40
    ##     [4]         3 1.44031e-15 1.69441e-13
    ##     [5]         3 2.20946e-11 1.28285e-09
    ##     ...       ...         ...         ...
    ##    [96]         3 8.20918e-14 7.43801e-12
    ##    [97]         4 3.70981e-14 3.50241e-12
    ##    [98]        16 1.96604e-21 4.27797e-19
    ##    [99]         3 9.81029e-14 8.80063e-12
    ##   [100]         3 4.88820e-12 3.47082e-10
    ##   -------
    ##   regions: 129090 ranges and 0 metadata columns
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

    k562.cis = k562.rep1[is.cis(k562.rep1)]

The length of each interaction is not stored as metadata, but we can calculate the distance of each interaction using either the inner edge, outer edge or midpoints of the anchors. Since this is undefinable for trans-chromosomal interactions it is best to first subset only cis interactions before calling `calculateDistances`, otherwise `NA`s will be present in the returned vector.

    head(calculateDistances(k562.cis, method="midpoint"))

    ## [1]  10415  96581 115325  81363  79098  59153

    k562.short = k562.cis[calculateDistances(k562.cis) < 1e6] # subset shorter interactions
    hist(calculateDistances(k562.short))

We can also subset based on the properties of the linked `GRanges` objects.

    chrom = c("chr17", "chr18")
    sub = as.vector(seqnames(anchorOne(k562.rep1)) %in% chrom & seqnames(anchorTwo(k562.rep1)) %in% chrom)
    k562.rep1 = k562.rep1[sub]

## Annotation

Genomic Interaction data is often used to look at the interactions between different elements in the genome, which are believed to have different functional roles. Interactions between promoters and their transcription termination sites, for example, are thought to be a by-product of the transcription process, whereas long-range interactions with enhancers play a role in gene regulation.

Since `GenomicInteractions` is based on `GenomicRanges`, it is very easy to interrogate `GenomicInteractions` objects using `GenomicRanges` data. In the example, we want to annotate interactions that overlap the promoters, transcription termination sites or the body of any gene. Since this can be a time-consuming and data-heavy process, this example runs the analysis for only chromosomes 17 & 18.

First we need the list of RefSeq transcripts:

    library(GenomicFeatures)

    hg19.refseq.db <- makeTxDbFromUCSC(genome="hg19", table="refGene")
    refseq.genes = genes(hg19.refseq.db)
    refseq.transcripts = transcriptsBy(hg19.refseq.db, by="gene")
    non_pseudogene = names(refseq.transcripts) %in% unlist(refseq.genes$gene_id)
    refseq.transcripts = refseq.transcripts[non_pseudogene]

Rather than downloading the whole Refseq database, these are provided for chromosomes 17 & 18:

    data("hg19.refseq.transcripts")
    refseq.transcripts = hg19.refseq.transcripts

We can then use functions from `GenomicRanges` to call promoters and terminators for these transcripts. We have taken promoter regions to be within 2.5kb of an annotated TSS and terminators to be within 1kb of the end of an annotated transcript. Since genes can have multiple transcripts, they can also have multiple promoters/terminators, so these are `GRangesList` objects, which makes handling these objects slightly more complicated.

    refseq.promoters = promoters(refseq.transcripts, upstream=2500, downstream=2500)
    # unlist object so "strand" is one vector
    refseq.transcripts.ul = unlist(refseq.transcripts)
    # terminators can be called as promoters with the strand reversed
    strand(refseq.transcripts.ul) = ifelse(strand(refseq.transcripts.ul) == "+", "-", "+")
    refseq.terminators.ul = promoters(refseq.transcripts.ul, upstream=1000, downstream=1000)
    # change back to original strand
    strand(refseq.terminators.ul) = ifelse(strand(refseq.terminators.ul) == "+", "-", "+")
    # `relist' maintains the original names and structure of the list
    refseq.terminators = relist(refseq.terminators.ul, refseq.transcripts)

These can be used to subset a `GenomicInteractions` object directly from `GRanges` using the `GenomicRanges` overlaps methods. `findOverlaps` called on a `GenomicInteractions` object will return a list containing `Hits` objects for both anchors.

We can finds any interactions involving a RefSeq promoter:

    subsetByFeatures(k562.rep1, unlist(refseq.promoters))

    ## GenomicInteractions object with 2907 interactions and 3 metadata columns:
    ##          seqnames1           ranges1     seqnames2           ranges2 |
    ##              <Rle>         <IRanges>         <Rle>         <IRanges> |
    ##      [1]     chr17     616579-621961 ---     chr17     620668-626263 |
    ##      [2]     chr17     632527-638035 ---     chr17     636589-641349 |
    ##      [3]     chr17     634119-651606 ---     chr17     642299-659172 |
    ##      [4]     chr17     654892-657597 ---     chr17     683191-687275 |
    ##      [5]     chr17     656002-658841 ---     chr17     679595-682692 |
    ##      ...       ...               ... ...       ...               ... .
    ##   [2903]     chr18 77781151-77783476 ---     chr18 77792968-77795855 |
    ##   [2904]     chr18 77784590-77787797 ---     chr18 77792148-77795822 |
    ##   [2905]     chr18 77792093-77797983 ---     chr18 77797455-77803127 |
    ##   [2906]     chr18 77793365-77797939 ---     chr18 77864889-77868321 |
    ##   [2907]     chr18 77863992-77870413 ---     chr18 77868294-77877151 |
    ##             counts     p.value         fdr
    ##          <integer>   <numeric>   <numeric>
    ##      [1]         7 2.06358e-32 8.50563e-30
    ##      [2]         9 2.39500e-36 1.15180e-33
    ##      [3]        55 0.00000e+00 0.00000e+00
    ##      [4]         6 4.86283e-24 1.27856e-21
    ##      [5]         3 6.23098e-14 5.72161e-12
    ##      ...       ...         ...         ...
    ##   [2903]         3 8.97548e-14 8.09366e-12
    ##   [2904]         6 4.16341e-26 1.24576e-23
    ##   [2905]        13 0.00000e+00 0.00000e+00
    ##   [2906]         8 6.61618e-30 2.41717e-27
    ##   [2907]        18 0.00000e+00 0.00000e+00
    ##   -------
    ##   regions: 129090 ranges and 0 metadata columns
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

However, one of the most powerful features in the `GenomicInteractions` package is the ability to annotate each anchor with a list of genomic regions and then summarise interactions according to these features. This annotation is implemented as metadata columns for the anchors in the `GenomicInteractions` object and so is fast, and facilitates more complex analyses.

The order in which we annotate the anchors is important, since each anchor can only have one `node.class`. The first listed take precedence. Any regions not overlapping ranges in `annotation.features` will be labelled as `distal`.

    annotation.features = list(promoter=refseq.promoters,
                               terminator=refseq.terminators,
                               gene.body=refseq.transcripts)
    annotateInteractions(k562.rep1, annotation.features)

    ## Annotating with promoter ...

    ## Annotating with terminator ...

    ## Annotating with gene.body ...

    annotationFeatures(k562.rep1)

    ## [1] "distal"     "gene.body"  "promoter"   "terminator"

We can now find interactions involving promoters using the annotated `node.class` for each anchor:

    p.one = anchorOne(k562.rep1)$node.class == "promoter"
    p.two = anchorTwo(k562.rep1)$node.class == "promoter"
    k562.rep1[p.one|p.two]

    ## GenomicInteractions object with 2907 interactions and 3 metadata columns:
    ##          seqnames1           ranges1     seqnames2           ranges2 |
    ##              <Rle>         <IRanges>         <Rle>         <IRanges> |
    ##      [1]     chr17     616579-621961 ---     chr17     620668-626263 |
    ##      [2]     chr17     632527-638035 ---     chr17     636589-641349 |
    ##      [3]     chr17     634119-651606 ---     chr17     642299-659172 |
    ##      [4]     chr17     654892-657597 ---     chr17     683191-687275 |
    ##      [5]     chr17     656002-658841 ---     chr17     679595-682692 |
    ##      ...       ...               ... ...       ...               ... .
    ##   [2903]     chr18 77781151-77783476 ---     chr18 77792968-77795855 |
    ##   [2904]     chr18 77784590-77787797 ---     chr18 77792148-77795822 |
    ##   [2905]     chr18 77792093-77797983 ---     chr18 77797455-77803127 |
    ##   [2906]     chr18 77793365-77797939 ---     chr18 77864889-77868321 |
    ##   [2907]     chr18 77863992-77870413 ---     chr18 77868294-77877151 |
    ##             counts     p.value         fdr
    ##          <integer>   <numeric>   <numeric>
    ##      [1]         7 2.06358e-32 8.50563e-30
    ##      [2]         9 2.39500e-36 1.15180e-33
    ##      [3]        55 0.00000e+00 0.00000e+00
    ##      [4]         6 4.86283e-24 1.27856e-21
    ##      [5]         3 6.23098e-14 5.72161e-12
    ##      ...       ...         ...         ...
    ##   [2903]         3 8.97548e-14 8.09366e-12
    ##   [2904]         6 4.16341e-26 1.24576e-23
    ##   [2905]        13 0.00000e+00 0.00000e+00
    ##   [2906]         8 6.61618e-30 2.41717e-27
    ##   [2907]        18 0.00000e+00 0.00000e+00
    ##   -------
    ##   regions: 129090 ranges and 4 metadata columns
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

This information can be used to categorise interactions into promoter-distal, promoter-terminator etc. A table of interaction types can be generated with `categoriseInteractions`:

    categoriseInteractions(k562.rep1)

    ##                 category count
    ## 1          distal-distal   396
    ## 2       distal-gene.body    76
    ## 3        distal-promoter   519
    ## 4      distal-terminator   101
    ## 5    gene.body-gene.body   795
    ## 6     gene.body-promoter   917
    ## 7   gene.body-terminator   164
    ## 8      promoter-promoter  1187
    ## 9    promoter-terminator   284
    ## 10 terminator-terminator    70

Alternatively, we can subset the object based on interaction type:

    k562.rep1[isInteractionType(k562.rep1, "terminator", "gene.body")]

    ## GenomicInteractions object with 164 interactions and 3 metadata columns:
    ##         seqnames1           ranges1     seqnames2           ranges2 |    counts
    ##             <Rle>         <IRanges>         <Rle>         <IRanges> | <integer>
    ##     [1]     chr17   1471460-1474306 ---     chr17   1476212-1479585 |         4
    ##     [2]     chr17   1632603-1638741 ---     chr17   1636657-1642967 |        12
    ##     [3]     chr17   3845975-3849573 ---     chr17   3908008-3910817 |         5
    ##     [4]     chr17   4055645-4058706 ---     chr17   4063341-4068158 |         4
    ##     [5]     chr17   4443889-4451615 ---     chr17   4446814-4454998 |        11
    ##     ...       ...               ... ...       ...               ... .       ...
    ##   [160]     chr18 32873043-32876330 ---     chr18 32911004-32914907 |         4
    ##   [161]     chr18 33566139-33569380 ---     chr18 33571111-33574360 |         3
    ##   [162]     chr18 33689982-33692774 ---     chr18 33695495-33699363 |         4
    ##   [163]     chr18 42638745-42641928 ---     chr18 42647110-42649707 |         3
    ##   [164]     chr18 77914125-77916781 ---     chr18 77919050-77922761 |         3
    ##             p.value         fdr
    ##           <numeric>   <numeric>
    ##     [1] 1.14380e-18 1.92712e-16
    ##     [2] 0.00000e+00 4.06377e-44
    ##     [3] 1.46527e-19 2.69952e-17
    ##     [4] 4.96495e-19 8.66918e-17
    ##     [5] 7.22930e-42 4.17953e-39
    ##     ...         ...         ...
    ##   [160] 1.37802e-15 1.62547e-13
    ##   [161] 5.37957e-15 5.74333e-13
    ##   [162] 1.35703e-19 2.50960e-17
    ##   [163] 1.03659e-15 1.24713e-13
    ##   [164] 2.90970e-15 3.24878e-13
    ##   -------
    ##   regions: 129090 ranges and 4 metadata columns
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

The 3 most common `node.class` values have short functions defined for convenience (see `?is.pp` for a complete list):

    k562.rep1[is.pp(k562.rep1)] # promoter-promoter interactions
    k562.rep1[is.dd(k562.rep1)] # distal-distal interactions
    k562.rep1[is.pt(k562.rep1)] # promoter-terminator interactions

Summary plots of interactions classes can easily be produced to get an overall feel for the data:

    plotInteractionAnnotations(k562.rep1, other=5)

    ## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
    ## ℹ Please use tidy evaluation idioms with `aes()`.
    ## ℹ See also `vignette("ggplot2-in-packages")` for more information.
    ## ℹ The deprecated feature was likely used in the GenomicInteractions package.
    ##   Please report the issue to the authors.
    ## This warning is displayed once per session.
    ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
    ## generated.

`viewpoints` will only take those interactions with a certain `node.class`:

    plotInteractionAnnotations(k562.rep1, other=5, viewpoints="promoter")

These are also combined in the function `plotSummaryStats`.

## Feature Summaries

The `summariseByFeatures` allows us to look in more detail at interactions involving a specific set of loci. In this example we use all RefSeq promoters, which we already have loaded in a `GRangesList` object.

It is however possible to use any dataset which can be represented as a named `GRanges` object, for example transcription-factor ChIP data, predicted cis-regulatory sites or certain categories of genes.

The categories are generated automatically from the annotated `node.class` values in the object.

    k562.rep1.promoter.annotation = summariseByFeatures(k562.rep1, refseq.promoters,
                                                        "promoter", distance.method="midpoint",
                                                        annotate.self=TRUE)
    colnames(k562.rep1.promoter.annotation)

    ##  [1] "Promoter.id"
    ##  [2] "numberOfPromoterInteractions"
    ##  [3] "numberOfPromoterUniqueInteractions"
    ##  [4] "numberOfPromoterInterChromosomalInteractions"
    ##  [5] "numberOfPromoterUniqueInterChromosomalInteractions"
    ##  [6] "numberOfPromoterDistalInteractions"
    ##  [7] "numberOfPromoterGene.bodyInteractions"
    ##  [8] "numberOfPromoterPromoterInteractions"
    ##  [9] "numberOfPromoterTerminatorInteractions"
    ## [10] "numberOfUniquePromoterDistalInteractions"
    ## [11] "numberOfUniquePromoterGene.bodyInteractions"
    ## [12] "numberOfUniquePromoterPromoterInteractions"
    ## [13] "numberOfUniquePromoterTerminatorInteractions"
    ## [14] "PromoterDistanceMedian"
    ## [15] "PromoterDistanceMean"
    ## [16] "PromoterDistanceMinimum"
    ## [17] "PromoterDistanceMaximum"
    ## [18] "PromoterDistanceWeightedMedian"
    ## [19] "numberOfSelfPromoterGene.bodyInteractions"
    ## [20] "numberOfSelfPromoterPromoterInteractions"
    ## [21] "numberOfSelfPromoterTerminatorInteractions"
    ## [22] "numberOfSelfUniquePromoterGene.bodyInteractions"
    ## [23] "numberOfSelfUniquePromoterPromoterInteractions"
    ## [24] "numberOfSelfUniquePromoterTerminatorInteractions"

This allows us to very quickly generate summaries of the data and provides a quick method to isolate genes of interest. In this case we produce lists of RefSeq IDs, which can easily be converted to EntrezIDs or gene symbols through existing BioConductor packages (in this case `org.Hs.eg.db` provides bimaps between common human genome annotations).

Which promoters have the strongest Promoter-Promoter interactions based on PET-counts?

    i = order(k562.rep1.promoter.annotation$numberOfPromoterPromoterInteractions,
              decreasing=TRUE)[1:10]
    k562.rep1.promoter.annotation[i,"Promoter.id"]

    ##  [1] "100506779" "9256"      "406934"    "54894"     "100616220" "6827"
    ##  [7] "56155"     "5889"      "5034"      "396"

Which promoters are contacting the largest number of distal elements?

    i = order(k562.rep1.promoter.annotation$numberOfUniquePromoterDistalInteractions,
              decreasing=TRUE)[1:10]
    k562.rep1.promoter.annotation[i,"Promoter.id"]

    ##  [1] "10140"     "400604"    "7050"      "100130581" "100616277" "26118"
    ##  [7] "100874261" "101927666" "140735"    "5366"

What percentage of promoters are in contact with transcription termination sites?

    total = sum(k562.rep1.promoter.annotation$numberOfPromoterTerminatorInteractions > 0)
    sprintf("%.2f%% of promoters have P-T interactions", 100*total/nrow(k562.rep1.promoter.annotation))

    ## [1] "16.43% of promoters have P-T interactions"

## References

1. Li, Guoliang, et al. “Software ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing.” Genome Biol 11 (2010): R22.

2. Li, Guoliang, et al. “Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation.” Cell 148.1 (2012): 84-98