An Introduction to the GenomicRanges Package (original) (raw)
Contents
- 1 Introduction
- 2 GRanges: Genomic Ranges
- 3 GRangesList: Groups of Genomic Ranges
- 4 Interval overlaps involving GRanges and GRangesList objects
- 5 Finding the nearest genomic position in GRanges objects
- 6 Session Information
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 athttps://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)GRanges: Genomic Ranges
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 theGRanges 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] 10Splitting and combining GRanges objects
GRanges objects can be divided into groups using thesplit 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 thec 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 genomeSubsetting GRanges objects
GRanges 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 operations for GRanges objects
Basic 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 andshift. 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 reducemethod 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. Thegaps 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.
Interval set operations for GRanges objects
Between-range methods calculate relationships between different_GRanges_ objects. Of central importance arefindOverlaps 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")GRangesList: Groups of Genomic Ranges
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.
Basic GRangesList accessors
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 callinglapply 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 mcolsmethod 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 theGRangesList, 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 0.50Combining GRangesList objects
GRangesList 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 userhad 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 seqlengthsBasic interval operations for GRangesList objects
For 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 1Subsetting GRangesList objects
A 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, andwindow methods all behave as you would expect them to for a list object. For example, the elements referred to by windoware 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 seqlengthsLooping over GRangesList objects
For GRangesList objects there is also a family ofapply 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 standardlapply and sapply behave according to convention, with the lapply method returning a list and sapplyreturning 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 endoapplymethod, 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
##
## $txB
## GRanges object with 4 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr1 107-109 + | 3 0.3
## [2] chr1 113-115 - | 4 0.5
## [3] chr1 117-119 + | 3 0.3
## [4] chr1 123-125 - | 4 0.5
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsThe Reduce method will allow the GRanges objects to be collapsed across the whole of the GRangesList object. % Again, this seems like a sub-optimal example to me.
Reduce(c, grl)## GRanges object with 3 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr2 103-106 + | 5 0.45
## [2] chr1 107-109 + | 3 0.30
## [3] chr1 113-115 - | 4 0.50
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsExplicit element-wise operations (lapply() and friends) on_GRangesList_ objects with many elements can be slow. It is therefore beneficial to explore operations that work on _List_objects directly (e.g., many of the ‘group generic’ operators, see?S4groupGeneric, and the set and parallel set operators (e.g.,union, punion). A useful and fast strategy is tounlist the GRangesList to a GRanges object, operate on the GRanges object, then relist the result, e.g.,
gr <- unlist(grl)
gr$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 overlaps involving GRanges and GRangesList objects
Interval 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 2Finding the nearest genomic position in GRanges objects
The 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.hg38.knownGene_ extracted using the genesmethod from the GenomicFeatures package.
library(TxDb.Hsapiens.UCSC.hg38.knownGene)## Loading required package: GenomicFeatures## Loading required package: AnnotationDbi## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
broads <- GenomicFeatures::genes(txdb)## 2169 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] 5093 4432 4432 4951 97 97 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 4432 4432 4432 97 97 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] 5093 NA NA 4951 5093 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 insubject. The argument k can be specified to identify more than one nearest neighbor. Overlaps are included. If subject is not given as an argument, xwill also be treated as the subject.
nearestKNeighbors(x, subject)## IntegerList of length 10
## [[1]] 5093
## [[2]] 4432
## [[3]] 4432
## [[4]] 4951
## [[5]] 97
## [[6]] 97
## [[7]] <NA>
## [[8]] <NA>
## [[9]] <NA>
## [[10]] <NA>nearestKNeighbors(x, subject, k=10)## IntegerList of length 10
## [[1]] 5093 814 4994 2253 5289 2312 435 800 2801 2311
## [[2]] 4432 514 2571 1164 444 524 1165 1166 1208 5227
## [[3]] 4432 514 2571 1164 444 524 1165 1166 1208 5227
## [[4]] 4951 4432 514 2571 1164 444 524 1165 1166 1208
## [[5]] 97 814 1921 131 5389 916 2313 1575 2314 4078
## [[6]] 97 1921 131 5389 916 2313 1575 2314 4078 4969
## [[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 9Session Information
All of the output in this vignette was produced under the following conditions:
sessionInfo()## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] TxDb.Hsapiens.UCSC.hg38.knownGene_3.21.0
## [2] GenomicFeatures_1.60.0
## [3] AnnotationDbi_1.70.0
## [4] Biobase_2.68.0
## [5] GenomicRanges_1.60.0
## [6] GenomeInfoDb_1.44.0
## [7] IRanges_2.42.0
## [8] S4Vectors_0.46.0
## [9] BiocGenerics_0.54.0
## [10] generics_0.1.3
## [11] BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.48.0 SummarizedExperiment_1.38.0
## [3] rjson_0.2.23 xfun_0.52
## [5] bslib_0.9.0 lattice_0.22-7
## [7] vctrs_0.6.5 tools_4.5.0
## [9] bitops_1.0-9 curl_6.2.2
## [11] parallel_4.5.0 RSQLite_2.3.9
## [13] blob_1.2.4 pkgconfig_2.0.3
## [15] Matrix_1.7-3 lifecycle_1.0.4
## [17] GenomeInfoDbData_1.2.14 compiler_4.5.0
## [19] Rsamtools_2.24.0 Biostrings_2.76.0
## [21] codetools_0.2-20 htmltools_0.5.8.1
## [23] sass_0.4.10 RCurl_1.98-1.17
## [25] yaml_2.3.10 crayon_1.5.3
## [27] jquerylib_0.1.4 BiocParallel_1.42.0
## [29] cachem_1.1.0 DelayedArray_0.34.0
## [31] abind_1.4-8 digest_0.6.37
## [33] restfulr_0.0.15 bookdown_0.43
## [35] fastmap_1.2.0 grid_4.5.0
## [37] SparseArray_1.8.0 cli_3.6.4
## [39] S4Arrays_1.8.0 XML_3.99-0.18
## [41] UCSC.utils_1.4.0 bit64_4.6.0-1
## [43] rmarkdown_2.29 XVector_0.48.0
## [45] httr_1.4.7 matrixStats_1.5.0
## [47] bit_4.6.0 png_0.1-8
## [49] memoise_2.0.1 evaluate_1.0.3
## [51] knitr_1.50 BiocIO_1.18.0
## [53] rtracklayer_1.68.0 rlang_1.1.6
## [55] DBI_1.2.3 BiocManager_1.30.25
## [57] jsonlite_2.0.0 R6_2.6.1
## [59] MatrixGenerics_1.20.0 GenomicAlignments_1.44.0