The GenomicRanges package serves as the foundation for representing genomic locations within the Bioconductor project. In the Bioconductor package hierarchy, it builds upon the IRanges (infrastructure) package and provides support for the BSgenome (infrastructure), Rsamtools (I/O), ShortRead (I/O & QA), rtracklayer (I/O), GenomicFeatures (infrastructure), GenomicAlignments (sequence reads), VariantAnnotation (called variants), and many other Bioconductor packages.
This package lays a foundation for genomic analysis by introducing three classes (GRanges, GPos, and GRangesList), which are used to represent genomic ranges, genomic positions, and groups of genomic ranges. This vignette focuses on the GRanges and GRangesList classes and their associated methods.
The GenomicRanges package is available at
https://bioconductor.org and can be
installed via BiocManager::install:
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("GenomicRanges")A package only needs to be installed once. Load the package into an R session with
library(GenomicRanges)The GRanges class represents a collection of genomic ranges
that each have a single start and end location on the genome. It can be
used to store the location of genomic features such as contiguous binding
sites, transcripts, and exons. These objects can be created by using the
GRanges constructor function. For example,
gr <- GRanges(
seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
ranges = IRanges(101:110, end = 111:120, names = head(letters, 10)),
strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)),
score = 1:10,
GC = seq(1, 0, length=10))
gr## GRanges object with 10 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 101-111 - | 1 1.000000
## b chr2 102-112 + | 2 0.888889
## c chr2 103-113 + | 3 0.777778
## d chr2 104-114 * | 4 0.666667
## e chr1 105-115 * | 5 0.555556
## f chr1 106-116 + | 6 0.444444
## g chr3 107-117 + | 7 0.333333
## h chr3 108-118 + | 8 0.222222
## i chr3 109-119 - | 9 0.111111
## j chr3 110-120 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genome; no seqlengthscreates a GRanges object with 10 genomic ranges.
The output of the GRanges show method separates the
information into a left and right hand region that are separated by
| symbols. The genomic coordinates (seqnames, ranges, and strand)
are located on the left-hand side and the metadata columns (annotation)
are located on the right. For this example, the metadata is
comprised of score and GC information, but almost
anything can be stored in the metadata portion of a GRanges
object.
The components of the genomic coordinates within a GRanges
object can be extracted using the seqnames, ranges,
and strand accessor functions.
seqnames(gr)## factor-Rle of length 10 with 4 runs
## Lengths: 1 3 2 4
## Values : chr1 chr2 chr1 chr3
## Levels(3): chr1 chr2 chr3ranges(gr)## IRanges object with 10 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## a 101 111 11
## b 102 112 11
## c 103 113 11
## d 104 114 11
## e 105 115 11
## f 106 116 11
## g 107 117 11
## h 108 118 11
## i 109 119 11
## j 110 120 11strand(gr)## factor-Rle of length 10 with 5 runs
## Lengths: 1 2 2 3 2
## Values : - + * + -
## Levels(3): + - *The genomic ranges can be extracted without corresponding metadata
with granges
granges(gr)## GRanges object with 10 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## a chr1 101-111 -
## b chr2 102-112 +
## c chr2 103-113 +
## d chr2 104-114 *
## e chr1 105-115 *
## f chr1 106-116 +
## g chr3 107-117 +
## h chr3 108-118 +
## i chr3 109-119 -
## j chr3 110-120 -
## -------
## seqinfo: 3 sequences from an unspecified genome; no seqlengthsAnnotations for these coordinates can be extracted as a
DataFrame object using the mcols accessor.
mcols(gr)## DataFrame with 10 rows and 2 columns
## score GC
## <integer> <numeric>
## a 1 1.000000
## b 2 0.888889
## c 3 0.777778
## d 4 0.666667
## e 5 0.555556
## f 6 0.444444
## g 7 0.333333
## h 8 0.222222
## i 9 0.111111
## j 10 0.000000mcols(gr)$score## [1] 1 2 3 4 5 6 7 8 9 10Information about the lengths of the various sequences that the ranges are aligned to can also be stored in the GRanges object. So if this is data from Homo sapiens, we can set the values as:
seqlengths(gr) <- c(249250621, 243199373, 198022430)And then retrieves as:
seqlengths(gr)## chr1 chr2 chr3
## 249250621 243199373 198022430Methods for accessing the length and names have
also been defined.
names(gr)## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"length(gr)## [1] 10GRanges objects can be devided into groups using the
split method. This produces a GRangesList object,
a class that will be discussed in detail in the next section.
sp <- split(gr, rep(1:2, each=5))
sp## GRangesList object of length 2:
## $`1`
## GRanges object with 5 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 101-111 - | 1 1.000000
## b chr2 102-112 + | 2 0.888889
## c chr2 103-113 + | 3 0.777778
## d chr2 104-114 * | 4 0.666667
## e chr1 105-115 * | 5 0.555556
## -------
## seqinfo: 3 sequences from an unspecified genome
##
## $`2`
## GRanges object with 5 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## f chr1 106-116 + | 6 0.444444
## g chr3 107-117 + | 7 0.333333
## h chr3 108-118 + | 8 0.222222
## i chr3 109-119 - | 9 0.111111
## j chr3 110-120 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genomeSeparate GRanges instances can be concatenated by using the
c and append methods.
c(sp[[1]], sp[[2]])## GRanges object with 10 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 101-111 - | 1 1.000000
## b chr2 102-112 + | 2 0.888889
## c chr2 103-113 + | 3 0.777778
## d chr2 104-114 * | 4 0.666667
## e chr1 105-115 * | 5 0.555556
## f chr1 106-116 + | 6 0.444444
## g chr3 107-117 + | 7 0.333333
## h chr3 108-118 + | 8 0.222222
## i chr3 109-119 - | 9 0.111111
## j chr3 110-120 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genomeGRanges objects act like vectors of ranges, with the expected vector-like subsetting operations available
gr[2:3]## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## b chr2 102-112 + | 2 0.888889
## c chr2 103-113 + | 3 0.777778
## -------
## seqinfo: 3 sequences from an unspecified genomeA second argument to the [ subset operator can be used
to specify which metadata columns to extract from the
GRanges object. For example,
gr[2:3, "GC"]## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | GC
## <Rle> <IRanges> <Rle> | <numeric>
## b chr2 102-112 + | 0.888889
## c chr2 103-113 + | 0.777778
## -------
## seqinfo: 3 sequences from an unspecified genomeElements can also be assigned to the GRanges object. Here is
an example where the second row of a GRanges object is
replaced with the first row of gr.
singles <- split(gr, names(gr))
grMod <- gr
grMod[2] <- singles[[1]]
head(grMod, n=3)## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 101-111 - | 1 1.000000
## b chr1 101-111 - | 1 1.000000
## c chr2 103-113 + | 3 0.777778
## -------
## seqinfo: 3 sequences from an unspecified genomeThere are methods to repeat, reverse, or select specific portions of GRanges objects.
rep(singles[[2]], times = 3)## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## b chr2 102-112 + | 2 0.888889
## b chr2 102-112 + | 2 0.888889
## b chr2 102-112 + | 2 0.888889
## -------
## seqinfo: 3 sequences from an unspecified genomerev(gr)## GRanges object with 10 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## j chr3 110-120 - | 10 0.000000
## i chr3 109-119 - | 9 0.111111
## h chr3 108-118 + | 8 0.222222
## g chr3 107-117 + | 7 0.333333
## f chr1 106-116 + | 6 0.444444
## e chr1 105-115 * | 5 0.555556
## d chr2 104-114 * | 4 0.666667
## c chr2 103-113 + | 3 0.777778
## b chr2 102-112 + | 2 0.888889
## a chr1 101-111 - | 1 1.000000
## -------
## seqinfo: 3 sequences from an unspecified genomehead(gr,n=2)## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 101-111 - | 1 1.000000
## b chr2 102-112 + | 2 0.888889
## -------
## seqinfo: 3 sequences from an unspecified genometail(gr,n=2)## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## i chr3 109-119 - | 9 0.111111
## j chr3 110-120 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genomewindow(gr, start=2,end=4)## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## b chr2 102-112 + | 2 0.888889
## c chr2 103-113 + | 3 0.777778
## d chr2 104-114 * | 4 0.666667
## -------
## seqinfo: 3 sequences from an unspecified genomegr[IRanges(start=c(2,7), end=c(3,9))]## GRanges object with 5 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## b chr2 102-112 + | 2 0.888889
## c chr2 103-113 + | 3 0.777778
## g chr3 107-117 + | 7 0.333333
## h chr3 108-118 + | 8 0.222222
## i chr3 109-119 - | 9 0.111111
## -------
## seqinfo: 3 sequences from an unspecified genomeBasic interval characteristics of GRanges objects can
be extracted using the start, end, width,
and range methods.
g <- gr[1:3]
g <- append(g, singles[[10]])
start(g)## [1] 101 102 103 110end(g)## [1] 111 112 113 120width(g)## [1] 11 11 11 11range(g)## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 101-111 -
## [2] chr2 102-113 +
## [3] chr3 110-120 -
## -------
## seqinfo: 3 sequences from an unspecified genomeThe GRanges class also has many methods for manipulating the ranges. The methods can be classified as intra-range methods, inter-range methods, and between-range methods.
Intra-range methods operate on each element of a
GRanges object independent of the other ranges in the
object. For example, the flank method can be used to recover
regions flanking the set of ranges represented by the GRanges
object. So to get a GRanges object containing the ranges that
include the 10 bases upstream of the ranges:
flank(g, 10)## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 112-121 - | 1 1.000000
## b chr2 92-101 + | 2 0.888889
## c chr2 93-102 + | 3 0.777778
## j chr3 121-130 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genomeAnd to include the downstream bases:
flank(g, 10, start=FALSE)## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 91-100 - | 1 1.000000
## b chr2 113-122 + | 2 0.888889
## c chr2 114-123 + | 3 0.777778
## j chr3 100-109 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genomeOther examples of intra-range methods include resize and
shift. The shift method will move the ranges by a
specific number of base pairs, and the resize method will
extend the ranges by a specified width.
shift(g, 5)## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 106-116 - | 1 1.000000
## b chr2 107-117 + | 2 0.888889
## c chr2 108-118 + | 3 0.777778
## j chr3 115-125 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genomeresize(g, 30)## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## a chr1 82-111 - | 1 1.000000
## b chr2 102-131 + | 2 0.888889
## c chr2 103-132 + | 3 0.777778
## j chr3 91-120 - | 10 0.000000
## -------
## seqinfo: 3 sequences from an unspecified genomeThe GenomicRanges help page ?"intra-range-methods"
summarizes these methods.
Inter-range methods involve comparisons between ranges in a
single GRanges object. For instance, the reduce
method will align the ranges and merge overlapping ranges to produce a
simplified set.
reduce(g)## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 101-111 -
## [2] chr2 102-113 +
## [3] chr3 110-120 -
## -------
## seqinfo: 3 sequences from an unspecified genomeSometimes one is interested in the gaps or the qualities of the gaps
between the ranges represented by your GRanges object. The
gaps method provides this information:
reduced version of your ranges:
gaps(g)## GRanges object with 12 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 1-249250621 +
## [2] chr1 1-100 -
## [3] chr1 112-249250621 -
## [4] chr1 1-249250621 *
## [5] chr2 1-101 +
## ... ... ... ...
## [8] chr2 1-243199373 *
## [9] chr3 1-198022430 +
## [10] chr3 1-109 -
## [11] chr3 121-198022430 -
## [12] chr3 1-198022430 *
## -------
## seqinfo: 3 sequences from an unspecified genomeThe disjoin method represents a GRanges object as a
collection of non-overlapping ranges:
disjoin(g)## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 101-111 -
## [2] chr2 102 +
## [3] chr2 103-112 +
## [4] chr2 113 +
## [5] chr3 110-120 -
## -------
## seqinfo: 3 sequences from an unspecified genomeThe coverage method quantifies the degree of overlap for all
the ranges in a GRanges object.
coverage(g)## RleList of length 3
## $chr1
## integer-Rle of length 249250621 with 3 runs
## Lengths: 100 11 249250510
## Values : 0 1 0
##
## $chr2
## integer-Rle of length 243199373 with 5 runs
## Lengths: 101 1 10 1 243199260
## Values : 0 1 2 1 0
##
## $chr3
## integer-Rle of length 198022430 with 3 runs
## Lengths: 109 11 198022310
## Values : 0 1 0See the GenomicRanges help page
?"inter-range-methods" for additional help.
Between-range methods involve operations between two GRanges objects; some of these are summarized in the next section.
Between-range methods calculate relationships between different
GRanges objects. Of central importance are
findOverlaps and related operations; these are discussed
below. Additional operations treat GRanges as mathematical
sets of coordinates; union(g, g2) is the union of the
coordinates in g and g2. Here are examples for
calculating the union, the intersect and the
asymmetric difference (using setdiff).
g2 <- head(gr, n=2)
union(g, g2)## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 101-111 -
## [2] chr2 102-113 +
## [3] chr3 110-120 -
## -------
## seqinfo: 3 sequences from an unspecified genomeintersect(g, g2)## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 101-111 -
## [2] chr2 102-112 +
## -------
## seqinfo: 3 sequences from an unspecified genomesetdiff(g, g2)## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr2 113 +
## [2] chr3 110-120 -
## -------
## seqinfo: 3 sequences from an unspecified genomeRelated methods are available when the structure of the
GRanges objects are ‘parallel’ to one another, i.e., element
1 of object 1 is related to element 1 of object 2, and so on. These
operations all begin with a p, which is short for
parallel. The methods then perform element-wise, e.g., the union of
element 1 of object 1 with element 1 of object 2, etc. A requirement
for these operations is that the number of elements in each
GRanges object is the same, and that both of the objects have
the same seqnames and strand assignments throughout.
g3 <- g[1:2]
ranges(g3[1]) <- IRanges(start=105, end=112)
punion(g2, g3)## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## a chr1 101-112 -
## b chr2 102-112 +
## -------
## seqinfo: 3 sequences from an unspecified genomepintersect(g2, g3)## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | score GC hit
## <Rle> <IRanges> <Rle> | <integer> <numeric> <logical>
## a chr1 105-111 - | 1 1.000000 TRUE
## b chr2 102-112 + | 2 0.888889 TRUE
## -------
## seqinfo: 3 sequences from an unspecified genomepsetdiff(g2, g3)## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## a chr1 101-104 -
## b chr2 102-101 +
## -------
## seqinfo: 3 sequences from an unspecified genomeFor more information on the GRanges class be sure to consult
the manual page.
?GRangesA relatively comprehensive list of available methods is discovered with
methods(class="GRanges")Some important genomic features, such as spliced transcripts that
are comprised of exons, are inherently compound structures. Such a
feature makes much more sense when expressed as a compound object
such as a GRangesList. Whenever genomic features consist of
multiple ranges that are grouped by a parent feature, they can be
represented as a GRangesList object. Consider the simple
example of the two transcript GRangesList below created
using the GRangesList constructor.
gr1 <- GRanges(
seqnames = "chr2",
ranges = IRanges(103, 106),
strand = "+",
score = 5L, GC = 0.45)
gr2 <- GRanges(
seqnames = c("chr1", "chr1"),
ranges = IRanges(c(107, 113), width = 3),
strand = c("+", "-"),
score = 3:4, GC = c(0.3, 0.5))
grl <- GRangesList("txA" = gr1, "txB" = gr2)
grl## GRangesList object of length 2:
## $txA
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txB
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsThe show method for a GRangesList object displays
it as a named list of GRanges objects, where the names of
this list are considered to be the names of the grouping feature. In
the example above, the groups of individual exon ranges are represented
as separate GRanges objects which are further organized into a
list structure where each element name is a transcript name. Many
other combinations of grouped and labeled GRanges objects are
possible of course, but this example is expected to be a common
arrangement.
Just as with GRanges object, the components of the genomic coordinates within a GRangesList object can be extracted using simple accessor methods. Not surprisingly, the GRangesList objects have many of the same accessors as GRanges objects. The difference is that many of these methods return a list since the input is now essentially a list of GRanges objects. Here are a few examples:
seqnames(grl)## RleList of length 2
## $txA
## factor-Rle of length 1 with 1 run
## Lengths: 1
## Values : chr2
## Levels(2): chr2 chr1
##
## $txB
## factor-Rle of length 2 with 1 run
## Lengths: 2
## Values : chr1
## Levels(2): chr2 chr1ranges(grl)## IRangesList object of length 2:
## $txA
## IRanges object with 1 range and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 103 106 4
##
## $txB
## IRanges object with 2 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 107 109 3
## [2] 113 115 3strand(grl)## RleList of length 2
## $txA
## factor-Rle of length 1 with 1 run
## Lengths: 1
## Values : +
## Levels(3): + - *
##
## $txB
## factor-Rle of length 2 with 2 runs
## Lengths: 1 1
## Values : + -
## Levels(3): + - *The length and names methods will return the length
or names of the list and the seqlengths method will return the
set of sequence lengths.
length(grl)## [1] 2names(grl)## [1] "txA" "txB"seqlengths(grl)## chr2 chr1
## NA NAThe elementNROWS method returns a list of integers
corresponding to the result of calling NROW on each
individual GRanges object contained by the
GRangesList. This is a faster alternative to calling
lapply on the GRangesList.
elementNROWS(grl)## txA txB
## 1 2isEmpty tests if a GRangesList object contains
anything.
isEmpty(grl)## [1] FALSEIn the context of a GRangesList object, the mcols
method performs a similar operation to what it does on a
GRanges object. However, this metadata now refers to
information at the list level instead of the level of the individual
GRanges objects.
mcols(grl) <- c("Transcript A","Transcript B")
mcols(grl)## DataFrame with 2 rows and 1 column
## value
## <character>
## txA Transcript A
## txB Transcript BElement-level metadata can be retrieved by unlisting the
GRangesList, and extracting the metadata
mcols(unlist(grl))## DataFrame with 3 rows and 2 columns
## score GC
## <integer> <numeric>
## txA 5 0.45
## txB 3 0.30
## txB 4 0.50GRangesList objects can be unlisted to combine the separate GRanges objects that they contain as an expanded GRanges.
ul <- unlist(grl)
ul## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## txA chr2 103-106 + | 5 0.45
## txB chr1 107-109 + | 3 0.30
## txB chr1 113-115 - | 4 0.50
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsAppend lists using append or c.
A support site user
had two GRangesList objects with ‘parallel’ elements, and
wanted to combined these element-wise into a single
GRangesList. One solution is to use pc() – parallel
(element-wise) c(). A more general solution is to concatenate
the lists and then re-group by some factor, in this case the names of
the elements.
grl1 <- GRangesList(
gr1 = GRanges("chr2", IRanges(3, 6)),
gr2 = GRanges("chr1", IRanges(c(7,13), width = 3)))
grl2 <- GRangesList(
gr1 = GRanges("chr2", IRanges(9, 12)),
gr2 = GRanges("chr1", IRanges(c(25,38), width = 3)))
pc(grl1, grl2)## GRangesList object of length 2:
## $gr1
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr2 3-6 *
## [2] chr2 9-12 *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $gr2
## GRanges object with 4 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 7-9 *
## [2] chr1 13-15 *
## [3] chr1 25-27 *
## [4] chr1 38-40 *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsgrl3 <- c(grl1, grl2)
regroup(grl3, names(grl3))## GRangesList object of length 2:
## $gr1
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr2 3-6 *
## [2] chr2 9-12 *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $gr2
## GRanges object with 4 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 7-9 *
## [2] chr1 13-15 *
## [3] chr1 25-27 *
## [4] chr1 38-40 *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsFor interval operations, many of the same methods exist for GRangesList objects that exist for GRanges objects.
start(grl)## IntegerList of length 2
## [["txA"]] 103
## [["txB"]] 107 113end(grl)## IntegerList of length 2
## [["txA"]] 106
## [["txB"]] 109 115width(grl)## IntegerList of length 2
## [["txA"]] 4
## [["txB"]] 3 3These operations return a data structure representing, e.g., IntegerList, a list where all elements are integers; it can be convenient to use mathematical and other operations on List objects that work on each element, e.g.,
sum(width(grl)) # sum of widths of each grl element## txA txB
## 4 6Most of the intra-, inter- and between-range methods operate on GRangesList objects, e.g., to shift all the GRanges objects in a GRangesList object, or calculate the coverage. Both of these operations are also carried out across each GRanges list member.
shift(grl, 20)## GRangesList object of length 2:
## $txA
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 123-126 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txB
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 127-129 + | 3 0.3
## [2] chr1 133-135 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthscoverage(grl)## RleList of length 2
## $chr2
## integer-Rle of length 106 with 2 runs
## Lengths: 102 4
## Values : 0 1
##
## $chr1
## integer-Rle of length 115 with 4 runs
## Lengths: 106 3 3 3
## Values : 0 1 0 1A GRangesList object behaves like a list:
[ returns a GRangesList containing a subset of the
original object; [[ or $ returns the
GRanges object at that location in the list.
grl[1]
grl[[1]]
grl["txA"]
grl$txBIn addition, subsetting a GRangesList also accepts a second parameter to specify which of the metadata columns you wish to select.
grl[1, "score"]## GRangesList object of length 1:
## $txA
## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr2 103-106 + | 5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsgrl["txB", "GC"]## GRangesList object of length 1:
## $txB
## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | GC
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr1 107-109 + | 0.3
## [2] chr1 113-115 - | 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsThe head, tail, rep, rev, and
window methods all behave as you would expect them to for a
list object. For example, the elements referred to by window
are now list elements instead of GRanges elements.
rep(grl[[1]], times = 3)## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## [2] chr2 103-106 + | 5 0.45
## [3] chr2 103-106 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsrev(grl)## GRangesList object of length 2:
## $txB
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txA
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthshead(grl, n=1)## GRangesList object of length 1:
## $txA
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthstail(grl, n=1)## GRangesList object of length 1:
## $txB
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthswindow(grl, start=1, end=1)## GRangesList object of length 1:
## $txA
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsgrl[IRanges(start=2, end=2)]## GRangesList object of length 1:
## $txB
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsFor GRangesList objects there is also a family of
apply methods. These include lapply, sapply,
mapply, endoapply, mendoapply, Map,
and Reduce.
The different looping methods defined for GRangesList objects
are useful for returning different kinds of results. The standard
lapply and sapply behave according to convention,
with the lapply method returning a list and sapply
returning a more simplified output.
lapply(grl, length)## $txA
## [1] 1
##
## $txB
## [1] 2sapply(grl, length)## txA txB
## 1 2As with IRanges objects, there is also a multivariate version
of sapply, called mapply, defined for
GRangesList objects. And, if you don’t want the results
simplified, you can call the Map method, which does the same
things as mapply but without simplifying the output.
grl2 <- shift(grl, 10)
names(grl2) <- c("shiftTxA", "shiftTxB")
mapply(c, grl, grl2)## $txA
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## [2] chr2 113-116 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txB
## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## [3] chr1 117-119 + | 3 0.3
## [4] chr1 123-125 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsMap(c, grl, grl2)## $txA
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## [2] chr2 113-116 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txB
## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## [3] chr1 117-119 + | 3 0.3
## [4] chr1 123-125 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsSometimes you will want to get back a modified version of the GRangesList that you originally passed in.
An endomorphism is a transformation of an object to another instance
of the same class . This is achieved using the endoapply
method, which will return the results as a GRangesList
object.
endoapply(grl, rev)## GRangesList object of length 2:
## $txA
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txB
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 113-115 - | 4 0.5
## [2] chr1 107-109 + | 3 0.3
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsmendoapply(c, grl, grl2)## GRangesList object of length 2:
## $txA
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## [2] chr2 113-116 + | 5 0.45
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txB
## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## [3] chr1 117-119 + | 3 0.3
## [4] chr1 123-125 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsThe Reduce method will allow the GRanges objects to
be collapsed across the whole of the GRangesList object.
% Again, this seems like a sub-optimal example to me.
Reduce(c, grl)## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## [2] chr1 107-109 + | 3 0.30
## [3] chr1 113-115 - | 4 0.50
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsExplicit element-wise operations (lapply() and friends) on
GRangesList objects with many elements can be slow. It is
therefore beneficial to explore operations that work on List
objects directly (e.g., many of the ‘group generic’ operators, see
?S4groupGeneric, and the set and parallel set operators (e.g.,
union, punion). A useful and fast strategy is to
unlist the GRangesList to a GRanges object,
operate on the GRanges object, then relist the
result, e.g.,
gr <- unlist(grl)
gr$log_score <- log(gr$score)
grl <- relist(gr, grl)
grl## GRangesList object of length 2:
## $txA
## GRanges object with 1 range and 3 metadata columns:
## seqnames ranges strand | score GC log_score
## <Rle> <IRanges> <Rle> | <integer> <numeric> <numeric>
## txA chr2 103-106 + | 5 0.45 1.60944
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
##
## $txB
## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | score GC log_score
## <Rle> <IRanges> <Rle> | <integer> <numeric> <numeric>
## txB chr1 107-109 + | 3 0.3 1.09861
## txB chr1 113-115 - | 4 0.5 1.38629
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsSee also ?extractList.
For more information on the GRangesList class be sure to
consult the manual page and available methods
?GRangesList
methods(class="GRangesList") # _partial_ listInterval overlapping is the process of comparing the ranges in two
objects to determine if and when they overlap. As such, it is perhaps
the most common operation performed on GRanges and
GRangesList objects. To this end, the GenomicRanges
package provides a family of interval overlap functions. The most general
of these functions is findOverlaps, which takes a query and a
subject as inputs and returns a Hits object containing
the index pairings for the overlapping elements.
findOverlaps(gr, grl)## Hits object with 3 hits and 0 metadata columns:
## queryHits subjectHits
## <integer> <integer>
## [1] 1 1
## [2] 2 2
## [3] 3 2
## -------
## queryLength: 3 / subjectLength: 2As suggested in the sections discussing the nature of the GRanges and GRangesList classes, the index in the above Hits object for a GRanges object is a single range while for a GRangesList object it is the set of ranges that define a “feature”.
Another function in the overlaps family is countOverlaps,
which tabulates the number of overlaps for each element in the query.
countOverlaps(gr, grl)## txA txB txB
## 1 1 1A third function in this family is subsetByOverlaps,
which extracts the elements in the query that overlap at least one
element in the subject.
subsetByOverlaps(gr,grl)## GRanges object with 3 ranges and 3 metadata columns:
## seqnames ranges strand | score GC log_score
## <Rle> <IRanges> <Rle> | <integer> <numeric> <numeric>
## txA chr2 103-106 + | 5 0.45 1.60944
## txB chr1 107-109 + | 3 0.30 1.09861
## txB chr1 113-115 - | 4 0.50 1.38629
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsFinally, you can use the select argument to get the index
of the first overlapping element in the subject for each element
in the query.
findOverlaps(gr, grl, select="first")## [1] 1 2 2findOverlaps(grl, gr, select="first")## [1] 1 2The GenomicRanges package provides multiple functions to
facilitate the indentification of neighboring genomic positions.
For the following examples, we define an arbitrary GRanges object for x and
we define the GRanges object subject as the collection of genes in
TxDb.Hsapiens.UCSC.hg19.knownGene extracted using the genes
method from the GenomicFeatures package.
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
broads <- GenomicFeatures::genes(txdb)## 403 genes were dropped because they have exons located on both strands
## of the same reference sequence or on more than one reference sequence,
## so cannot be represented by a single genomic range.
## Use 'single.strand.genes.only=FALSE' to get all the genes in a
## GRangesList object, or use suppressMessages() to suppress this message.x <- GRanges(
seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
ranges = IRanges(101:110, end = 111:120, names = head(letters, 10)),
strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)),
score = 1:10, GC = seq(1, 0, length=10))
subject <- broads[ seqnames(broads) %in% seqlevels(gr) ]The nearest method performs conventional nearest neighbor finding. It
finds the nearest neighbor range in subject for each range in x.
Overlaps are included. If subject is not given as an argument, x will also
be treated as the subject.
nearest(x, subject)## [1] 2898 2224 2224 2765 73 73 NA NA NA NAnearest(x)## [1] 5 4 4 3 6 5 8 7 10 9The precede method will return the index of the range in subject that is
preceded by the range in x. Overlaps are excluded.
precede(x, subject)## [1] NA 2224 2224 2224 73 73 NA NA NA NAThe follow method will return the index of the range in subject that is
followed by the range in x.
follow(x, subject)## [1] 2898 NA NA 2765 2898 NA NA NA NA NAThe nearestKNeighbors method performs conventional k-nearest neighbor finding. For
each range in x, it will find the index of the k-nearest neighbors in
subject. The argument k can be specified to identify more than one nearest
neighbor. Overlaps are included. If subject is not given as an argument, x
will also be treated as the subject.
nearestKNeighbors(x, subject)## IntegerList of length 10
## [[1]] 2898
## [[2]] 2224
## [[3]] 2224
## [[4]] 2765
## [[5]] 73
## [[6]] 73
## [[7]] <NA>
## [[8]] <NA>
## [[9]] <NA>
## [[10]] <NA>nearestKNeighbors(x, subject, k=10)## IntegerList of length 10
## [[1]] 2898 3094 80 3240 1441 32 1312 3448 2609 1912
## [[2]] 2224 2260 3040 3104 2149 177 2695 3165 2468 2926
## [[3]] 2224 2260 3040 3104 2149 177 2695 3165 2468 2926
## [[4]] 2765 2224 2260 3040 3104 2149 177 2695 3165 2468
## [[5]] 73 2898 3191 2783 756 1606 3367 3722 1763 1234
## [[6]] 73 3191 2783 756 1606 3367 3722 1763 1234 1941
## [[7]] <NA>
## [[8]] <NA>
## [[9]] <NA>
## [[10]] <NA>nearestKNeighbors(x)## IntegerList of length 10
## [[1]] 5
## [[2]] 3
## [[3]] 2
## [[4]] 2
## [[5]] 1
## [[6]] 5
## [[7]] 8
## [[8]] 7
## [[9]] 10
## [[10]] 9nearestKNeighbors(x, k=10)## IntegerList of length 10
## [[1]] 5 5
## [[2]] 3 4
## [[3]] 2 4
## [[4]] 2 3
## [[5]] 1 6 1
## [[6]] 5
## [[7]] 8
## [[8]] 7
## [[9]] 10 10
## [[10]] 9 9All of the output in this vignette was produced under the following conditions:
sessionInfo()## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] GenomicRanges_1.48.0 GenomeInfoDb_1.32.0 IRanges_2.30.0
## [4] S4Vectors_0.34.0 BiocGenerics_0.42.0 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] MatrixGenerics_1.8.0
## [2] Biobase_2.56.0
## [3] httr_1.4.2
## [4] sass_0.4.1
## [5] bit64_4.0.5
## [6] jsonlite_1.8.0
## [7] bslib_0.3.1
## [8] assertthat_0.2.1
## [9] BiocManager_1.30.17
## [10] BiocFileCache_2.4.0
## [11] blob_1.2.3
## [12] GenomeInfoDbData_1.2.8
## [13] Rsamtools_2.12.0
## [14] yaml_2.3.5
## [15] progress_1.2.2
## [16] lattice_0.20-45
## [17] pillar_1.7.0
## [18] RSQLite_2.2.12
## [19] glue_1.6.2
## [20] digest_0.6.29
## [21] XVector_0.36.0
## [22] Matrix_1.4-1
## [23] htmltools_0.5.2
## [24] XML_3.99-0.9
## [25] pkgconfig_2.0.3
## [26] biomaRt_2.52.0
## [27] bookdown_0.26
## [28] zlibbioc_1.42.0
## [29] purrr_0.3.4
## [30] BiocParallel_1.30.0
## [31] tibble_3.1.6
## [32] KEGGREST_1.36.0
## [33] generics_0.1.2
## [34] ellipsis_0.3.2
## [35] SummarizedExperiment_1.26.0
## [36] cachem_1.0.6
## [37] GenomicFeatures_1.48.0
## [38] cli_3.3.0
## [39] magrittr_2.0.3
## [40] crayon_1.5.1
## [41] memoise_2.0.1
## [42] evaluate_0.15
## [43] fansi_1.0.3
## [44] xml2_1.3.3
## [45] tools_4.2.0
## [46] prettyunits_1.1.1
## [47] hms_1.1.1
## [48] matrixStats_0.62.0
## [49] BiocIO_1.6.0
## [50] lifecycle_1.0.1
## [51] stringr_1.4.0
## [52] DelayedArray_0.22.0
## [53] AnnotationDbi_1.58.0
## [54] Biostrings_2.64.0
## [55] compiler_4.2.0
## [56] jquerylib_0.1.4
## [57] rlang_1.0.2
## [58] grid_4.2.0
## [59] RCurl_1.98-1.6
## [60] rjson_0.2.21
## [61] rappdirs_0.3.3
## [62] bitops_1.0-7
## [63] rmarkdown_2.14
## [64] restfulr_0.0.13
## [65] DBI_1.1.2
## [66] curl_4.3.2
## [67] R6_2.5.1
## [68] GenomicAlignments_1.32.0
## [69] knitr_1.38
## [70] dplyr_1.0.8
## [71] rtracklayer_1.56.0
## [72] fastmap_1.1.0
## [73] bit_4.0.4
## [74] utf8_1.2.2
## [75] filelock_1.0.2
## [76] stringi_1.7.6
## [77] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [78] parallel_4.2.0
## [79] Rcpp_1.0.8.3
## [80] vctrs_0.4.1
## [81] png_0.1-7
## [82] dbplyr_2.1.1
## [83] tidyselect_1.1.2
## [84] xfun_0.30