Additional examples of plyranges
Stuart Lee
2026-03-15
Source:vignettes/more-examples.Rmd
more-examples.RmdQuick overview
About Ranges
Ranges objects can either represent sets of integers as
IRanges (which have start, end and width attributes) or
represent genomic intervals (which have additional attributes, sequence
name, and strand) as GRanges. In addition, both types of
Ranges can store information about their intervals as
metadata columns (for example GC content over a genomic interval).
Ranges objects follow the tidy data principle: each row
of a Ranges object corresponds to an interval, while each
column will represent a variable about that interval, and generally each
object will represent a single unit of observation (like gene
annotations).
We can construct a IRanges object from a
data.frame with a start or width
using the as_iranges() method.
library(plyranges)
df <- data.frame(start = 1:5, width = 5)
as_iranges(df)## IRanges object with 5 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 5 5
## [2] 2 6 5
## [3] 3 7 5
## [4] 4 8 5
## [5] 5 9 5
# alternatively with end
df <- data.frame(start = 1:5, end = 5:9)
as_iranges(df)## IRanges object with 5 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 5 5
## [2] 2 6 5
## [3] 3 7 5
## [4] 4 8 5
## [5] 5 9 5We can also construct a GRanges object in a similar
manner. Note that a GRanges object requires at least a
seqnames column to be present in the data.frame (but not necessarily a
strand column).
df <- data.frame(seqnames = c("chr1", "chr2", "chr2", "chr1", "chr2"),
start = 1:5,
width = 5)
as_granges(df)## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 1-5 *
## [2] chr2 2-6 *
## [3] chr2 3-7 *
## [4] chr1 4-8 *
## [5] chr2 5-9 *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths
# strand can be specified with `+`, `*` (mising) and `-`
df$strand <- c("+", "+", "-", "-", "*")
as_granges(df)## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 1-5 +
## [2] chr2 2-6 +
## [3] chr2 3-7 -
## [4] chr1 4-8 -
## [5] chr2 5-9 *
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsExample: finding GWAS hits that overlap known exons
Let’s look at a more a realistic example (taken from HelloRanges vignette).
Suppose we have two GRanges objects: one containing coordinates of known exons and another containing SNPs from a GWAS.
The first and last 5 exons are printed below, there are two additional columns corresponding to the exon name, and a score.
We could check the number of exons per chromosome using
group_by and summarise.
exons## GRanges object with 459752 ranges and 2 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 11874-12227 + | NR_046018_exon_0_0_c.. 0
## [2] chr1 12613-12721 + | NR_046018_exon_1_0_c.. 0
## [3] chr1 13221-14409 + | NR_046018_exon_2_0_c.. 0
## [4] chr1 14362-14829 - | NR_024540_exon_0_0_c.. 0
## [5] chr1 14970-15038 - | NR_024540_exon_1_0_c.. 0
## ... ... ... ... . ... ...
## [459748] chrY 59338754-59338859 + | NM_002186_exon_6_0_c.. 0
## [459749] chrY 59338754-59338859 + | NM_176786_exon_7_0_c.. 0
## [459750] chrY 59340194-59340278 + | NM_002186_exon_7_0_c.. 0
## [459751] chrY 59342487-59343488 + | NM_002186_exon_8_0_c.. 0
## [459752] chrY 59342487-59343488 + | NM_176786_exon_8_0_c.. 0
## -------
## seqinfo: 93 sequences from an unspecified genome; no seqlengths## DataFrame with 49 rows and 2 columns
## seqnames n
## <Rle> <integer>
## 1 chr1 43366
## 2 chr10 19420
## 3 chr11 24476
## 4 chr12 24949
## 5 chr13 7974
## ... ... ...
## 45 chrUn_gl000222 20
## 46 chrUn_gl000223 22
## 47 chrUn_gl000228 85
## 48 chrX 18173
## 49 chrY 4128Next we create a column representing the transcript_id with
mutate:
To find all GWAS SNPs that overlap exons, we use
join_overlap_inner. This will create a new GRanges
with the coordinates of SNPs that overlap exons, as well as metadata
from both objects.
olap <- join_overlap_inner(gwas, exons)
olap## GRanges object with 3439 ranges and 4 metadata columns:
## seqnames ranges strand | name.x name.y
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr1 1079198 * | rs11260603 NR_038869_exon_2_0_c..
## [2] chr1 1247494 * | rs12103 NM_001256456_exon_1_..
## [3] chr1 1247494 * | rs12103 NM_001256460_exon_1_..
## [4] chr1 1247494 * | rs12103 NM_001256462_exon_1_..
## [5] chr1 1247494 * | rs12103 NM_001256463_exon_1_..
## ... ... ... ... . ... ...
## [3435] chrX 153764217 * | rs1050828 NM_001042351_exon_9_..
## [3436] chrX 153764217 * | rs1050828 NM_000402_exon_9_0_c..
## [3437] chrX 153764217 * | rs1050828 NM_001042351_exon_9_..
## [3438] chrX 153764217 * | rs1050828 NM_000402_exon_9_0_c..
## [3439] chrX 153764217 * | rs1050828 NM_001042351_exon_9_..
## score tx_id
## <numeric> <character>
## [1] 0 NR_038869
## [2] 0 NM_001256456
## [3] 0 NM_001256460
## [4] 0 NM_001256462
## [5] 0 NM_001256463
## ... ... ...
## [3435] 0 NM_001042351
## [3436] 0 NM_000402
## [3437] 0 NM_001042351
## [3438] 0 NM_000402
## [3439] 0 NM_001042351
## -------
## seqinfo: 93 sequences from an unspecified genome; no seqlengthsFor each SNP we can count the number of times it overlaps a transcript.
## DataFrame with 1619 rows and 3 columns
## name.x tx_id n
## <character> <character> <integer>
## 1 rs10043775 NM_001271723 1
## 2 rs10043775 NM_030793 1
## 3 rs10078 NM_001242412 1
## 4 rs10078 NM_020731 1
## 5 rs10089 NM_001046 1
## ... ... ... ...
## 1615 rs9906595 NM_001008777 1
## 1616 rs9948 NM_017623 1
## 1617 rs9948 NM_199078 1
## 1618 rs995030 NM_000899 4
## 1619 rs995030 NM_003994 4We can also generate 2bp splice sites on either side of the exon
using flank_left and flank_right. We add a
column indicating the side of flanking for illustrative purposes. The
interweave function pairs the left and right ranges
objects.
left_ss <- flank_left(exons, 2L)
right_ss <- flank_right(exons, 2L)
all_ss <- interweave(left_ss, right_ss, .id = "side")
all_ss## GRanges object with 919504 ranges and 4 metadata columns:
## seqnames ranges strand | name score
## <Rle> <IRanges> <Rle> | <character> <numeric>
## [1] chr1 11872-11873 + | NR_046018_exon_0_0_c.. 0
## [2] chr1 12228-12229 + | NR_046018_exon_0_0_c.. 0
## [3] chr1 12611-12612 + | NR_046018_exon_1_0_c.. 0
## [4] chr1 12722-12723 + | NR_046018_exon_1_0_c.. 0
## [5] chr1 13219-13220 + | NR_046018_exon_2_0_c.. 0
## ... ... ... ... . ... ...
## [919500] chrY 59340279-59340280 + | NM_002186_exon_7_0_c.. 0
## [919501] chrY 59342485-59342486 + | NM_002186_exon_8_0_c.. 0
## [919502] chrY 59343489-59343490 + | NM_002186_exon_8_0_c.. 0
## [919503] chrY 59342485-59342486 + | NM_176786_exon_8_0_c.. 0
## [919504] chrY 59343489-59343490 + | NM_176786_exon_8_0_c.. 0
## tx_id side
## <character> <character>
## [1] NR_046018 left
## [2] NR_046018 right
## [3] NR_046018 left
## [4] NR_046018 right
## [5] NR_046018 left
## ... ... ...
## [919500] NM_002186 right
## [919501] NM_002186 left
## [919502] NM_002186 right
## [919503] NM_176786 left
## [919504] NM_176786 right
## -------
## seqinfo: 93 sequences from an unspecified genome; no seqlengthsSession information
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] plyranges_1.31.4 dplyr_1.2.0 GenomicRanges_1.62.1
## [4] Seqinfo_1.0.0 IRanges_2.44.0 S4Vectors_0.48.0
## [7] BiocGenerics_0.56.0 generics_0.1.4 BiocStyle_2.38.0
##
## loaded via a namespace (and not attached):
## [1] SummarizedExperiment_1.40.0 rjson_0.2.23
## [3] xfun_0.56 bslib_0.10.0
## [5] htmlwidgets_1.6.4 Biobase_2.70.0
## [7] lattice_0.22-9 vctrs_0.7.1
## [9] tools_4.5.2 bitops_1.0-9
## [11] curl_7.0.0 parallel_4.5.2
## [13] tibble_3.3.1 pkgconfig_2.0.3
## [15] Matrix_1.7-4 desc_1.4.3
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