GenomicRanges — Comprehensive Skill Guide

Domain: Genomics Infrastructure Bioconductor: GenomicRanges Paper: Lawrence M et al. (2013). PLOS Computational Biology, 9:e1003118.

Infrastructure for representing and manipulating genomic intervals. The core workhorse for any genomic coordinate arithmetic in Bioconductor.

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.64.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: BiocGenerics, S4Vectors, IRanges, Seqinfo
• Install: BiocManager::install("GenomicRanges")

When to Use

• Overlap queries between genomic features
• Peak annotation and TSS proximity
• Windowed genome coverage computation
• Import/export BED/GFF/VCF/BigWig regions
• Tiling genome for ChIP-seq/ATAC-seq windows

Alternatives: IRanges (integer ranges only), plyranges (tidyverse-style)

Do NOT Use When

• Sequence manipulation (use Biostrings instead).
• Annotation retrieval (use AnnotationHub, biomaRt, or TxDb).
• Genome-wide statistics directly (use BSgenome or rtracklayer for coverage).

Data Requirements

Requirement	Details
Input Format	BED, GFF/GTF (via rtracklayer), or programmatic construction from data frames

Installation

if (!require("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("GenomicRanges")
library(GenomicRanges)

Workflows

Genomic interval manipulation and overlap analysis

Core operations: construct, manipulate, and intersect GRanges objects

Build and manipulate GRanges

library(GenomicRanges)

# Create GRanges from scratch
gr <- GRanges(
    seqnames = c("chr1","chr1","chr2"),
    ranges   = IRanges(start=c(100,200,150), end=c(200,300,250)),
    strand   = c("+","-","*"),
    gene_id  = c("GENE1","GENE2","GENE3")
)

# Resize to 500bp around centre (e.g. for peak centering)
gr_centered <- resize(gr, width=500, fix="center")

# Extend 200bp upstream
gr_extended <- flank(gr, width=200, start=TRUE)

Find overlaps between two sets

peaks <- readRDS("chip_peaks.rds")    # GRanges of ChIP peaks
genes <- readRDS("gene_models.rds")   # GRanges of gene bodies

# Which peaks overlap any gene?
hits <- findOverlaps(peaks, genes)
peak_in_gene <- peaks[queryHits(hits)]

# Count overlaps per peak
ov_counts <- countOverlaps(peaks, genes)
peaks$n_genes_overlapped <- ov_counts

Coverage and binning

# Per-base coverage
cov <- coverage(gr)

# Bin genome into 1kb windows and count reads
bins <- tileGenome(seqinfo(gr), tilewidth=1000, cut.last.tile.in.chrom=TRUE)
bin_counts <- countOverlaps(bins, gr)

Key Functions & Parameters

GRanges()

Create genomic ranges object

Parameter	Description
seqnames	chromosome names (Rle)
ranges	IRanges object with start/end
strand	'+' | '-' | '*'
...	any additional metadata columns

findOverlaps()

Find overlaps between two GRanges; returns Hits object

Parameter	Description
query	GRanges
subject	GRanges
type	'any' (default) | 'start' | 'end' | 'within' | 'equal'
select	'all' | 'first' | 'last' | 'arbitrary'
maxgap	max gap allowed between ranges (default -1L = 0 gap)
minoverlap	min overlap required (default 1L)
ignore.strand	logical (default FALSE)

subsetByOverlaps()

Subset GRanges to those overlapping another set

Parameter	Description
x	GRanges to subset
ranges	GRanges to overlap with
maxgap	same as findOverlaps
minoverlap	same as findOverlaps

coverage()

Compute per-base coverage as Rle/RleList

Parameter	Description
x	GRanges or GAlignments
shift	shift each range (default 0)
width	seqlength per chromosome
weight	per-range weights (default 1)

resize()

Resize ranges (e.g. extend TSS windows)

Parameter	Description
x	GRanges
width	new width in bp
fix	'start' | 'end' | 'center'
ignore.strand	logical

promoters()

Extract promoter regions upstream of TSS

Parameter	Description
x	TxDb or GRanges of transcripts
upstream	bp upstream of TSS (default 2000)
downstream	bp downstream of TSS (default 200)

Scientific Assumptions

The method assumes the following about your data and experiment:

• Genomic intervals are 1-based, closed by default in R (not 0-based BED); subtract 1 when importing BED files.

Result Interpretation

• findOverlaps() result: queryHits = indices into query, subjectHits = indices into subject; many-to-many mapping.
• nearest() / distanceToNearest(): returns index of nearest feature in subject and distance in bp.
• coverage() result: RleList per chromosome; sum() gives total depth.

Best Practices

• Always use consistent chromosome naming style (UCSC: chr1 vs NCBI: 1); use seqlevelsStyle() to convert.
• Set seqinfo(gr) <- seqinfo(bsgenome) to attach chromosome lengths before coverage().
• Use plyranges package for a dplyr-style API: gr %>% filter_by_overlaps(other).
• Import BED files with rtracklayer::import('file.bed') → returns GRanges directly.
• Sort GRanges with sort() before most operations for efficiency.
• Use reduce() to merge overlapping ranges; disjoin() to split at every boundary.

When to Choose This vs Alternatives

Alternative	Prefer this tool when	Prefer alternative when
pybedtools (Python)	You work in Python or need BEDTools command-line compatibility.	You are in the Bioconductor R ecosystem and want integration with GRanges-aware packages.

Benchmark Evidence

GenomicRanges is foundational infrastructure; citations measure ecosystem adoption.

• Lawrence M et al. (2013). PLoS Comput Biol 9:e1003118. PMID:23950696 Original GenomicRanges paper. Demonstrates O(n log n) interval overlap algorithm scaling to whole-genome datasets (billions of intervals) with <1GB RAM footprint.

• Huber W et al. (2015). Nature Methods 12:115. PMID:25633503 Bioconductor overview paper; GenomicRanges is cited as the core interoperability layer used by >500 downstream Bioconductor packages, indicating community validation.

Common Errors & Troubleshooting

seqlevels mismatch between query and subject

Cause: chr1 vs 1 naming discrepancy

Fix: seqlevelsStyle(gr1) <- seqlevelsStyle(gr2) or keepStandardChromosomes()

Additional Notes from Official Documentation

Extracted from the GenomicRanges Bioconductor vignette(s)

Contents

1 Introduction 2 GRanges : Genomic Ranges 2.1 Splitting and combining GRanges objects 2.2 Subsetting GRanges objects 2.3 Basic interval operations for GRanges objects 2.4 Interval set operations for GRanges objects 2.1 Splitting and combining GRanges objects 2.2 Subsetting GRanges objects 2.3 Basic interval operations for GRanges objects 2.4 Interval set operations for GRanges objects 3 GRangesList : Groups of Genomic Ranges 3.1 Basic GRangesList accessors 3.2 Combining GRangesList objects 3.3 Basic interval operations for GRangesList objects 3.4 Subsetting GRangesList objects 3.5 Looping over GRangesList objects 3.1 Basic GRangesList accessors 3.2 Combining GRangesList objects 3.3 Basic interval operations for GRangesList objects 3.4 Subsetting GRangesList objects 3.5 Looping over GRange

1 Introduction

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 i

if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("GenomicRanges")

library(GenomicRanges)

2 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 the GRanges constructor function. For example, creates 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

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 seqlengths

2.1 Splitting and combining GRanges objects

GRanges objects can be divided into groups using the split method. This produces a GRangesList object, a class that will be discussed in detail in the next section. Separate GRanges instances can be concatenated by using the c and append methods.

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 genome

2.2 Subsetting GRanges objects

GRanges objects act like vectors of ranges, with the expected vector-like subsetting operations available A second argument to the [ subset operator can be used to specify which metadata columns to extract from the GRanges object. For example, Elements 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 . There are methods to repeat, reverse, or select specific portions of GRanges objects.

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 genome

2.3 Basic interval operations for GRanges objects

Basic interval characteristics of GRanges objects can be extracted using the start , end , width , and range methods. The 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: And to include the downstream bases: Other examples of intra-range methods include resize and shift . The shift method will move the ranges by a specific number of base pairs

g <- gr[1:3]
g <- append(g, singles[[10]])
start(g)

## [1] 101 102 103 110

2.4 Interval set operations for GRanges objects

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 ). Related 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 o

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 genome

3 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. The 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 structur

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 seqlengths

Resources

• Bioconductor page: https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html
• GitHub: https://github.com/Bioconductor/GenomicRanges
• Bioconductor support forum: https://support.bioconductor.org/
• Paper: Lawrence M et al. (2013). PLOS Computational Biology, 9:e1003118.

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