ggtranscript 
ggtranscript is a ggplot2 extension that makes it to easy to
visualize transcript structure and annotation.
Installation
# you can install the development version of ggtranscript from GitHub:
# install.packages("devtools")
devtools::install_github("dzhang32/ggtranscript")Usage
ggtranscript introduces 5 new geoms (geom_range(),
geom_half_range(), geom_intron(), geom_junction() and
geom_junction_label_repel()) and several helper functions designed to
facilitate the visualization of transcript structure and annotation. The
following guide takes you on a quick tour of using these geoms, for a
more detailed overview see the Getting Started
tutorial.
geom_range() and geom_intron() enable the plotting of exons and
introns, the core components of transcript annotation. ggtranscript
also provides to_intron(), which converts exon co-ordinates to the
corresponding introns. Together, ggtranscript enables users to plot
transcript structures with only exons as the required input and just a
few lines of code.
library(magrittr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(ggplot2)
library(ggtranscript)
# to illustrate the package's functionality
# ggtranscript includes example transcript annotation
sod1_annotation %>% head()
#> # A tibble: 6 × 8
#> seqnames start end strand type gene_name transcript_name transcript_biot…
#> <fct> <int> <int> <fct> <fct> <chr> <chr> <chr>
#> 1 21 3.17e7 3.17e7 + gene SOD1 <NA> <NA>
#> 2 21 3.17e7 3.17e7 + tran… SOD1 SOD1-202 protein_coding
#> 3 21 3.17e7 3.17e7 + exon SOD1 SOD1-202 protein_coding
#> 4 21 3.17e7 3.17e7 + CDS SOD1 SOD1-202 protein_coding
#> 5 21 3.17e7 3.17e7 + star… SOD1 SOD1-202 protein_coding
#> 6 21 3.17e7 3.17e7 + exon SOD1 SOD1-202 protein_coding
# extract exons
sod1_exons <- sod1_annotation %>% dplyr::filter(type == "exon")
sod1_exons %>%
ggplot(aes(
xstart = start,
xend = end,
y = transcript_name
)) +
geom_range(
aes(fill = transcript_biotype)
) +
geom_intron(
data = to_intron(sod1_exons, "transcript_name"),
aes(strand = strand)
)
ggtranscript provides the helper function shorten_gaps(), which
reduces the size of the gaps. shorten_gaps() then rescales the exon
and intron co-ordinates to preserve the original exon alignment. This
allows you to hone in the differences in the exonic structure, which can
be particularly useful if the transcript has relatively long introns.
sod1_rescaled <- shorten_gaps(
sod1_exons,
to_intron(sod1_exons, "transcript_name"),
group_var = "transcript_name"
)
sod1_rescaled %>%
dplyr::filter(type == "exon") %>%
ggplot(aes(
xstart = start,
xend = end,
y = transcript_name
)) +
geom_range(
aes(fill = transcript_biotype)
) +
geom_intron(
data = sod1_rescaled %>% dplyr::filter(type == "intron"),
arrow.min.intron.length = 200
)
geom_range() can be used for any range-based genomic annotation. For
example, when plotting protein-coding transcripts, users may find it
helpful to visually distinguish the coding segments from UTRs.
# filter for only exons from protein coding transcripts
sod1_exons_prot_cod <- sod1_exons %>%
dplyr::filter(transcript_biotype == "protein_coding")
# obtain cds
sod1_cds <- sod1_annotation %>% dplyr::filter(type == "CDS")
sod1_exons_prot_cod %>%
ggplot(aes(
xstart = start,
xend = end,
y = transcript_name
)) +
geom_range(
fill = "white",
height = 0.25
) +
geom_range(
data = sod1_cds
) +
geom_intron(
data = to_intron(sod1_exons_prot_cod, "transcript_name"),
aes(strand = strand),
arrow.min.intron.length = 500,
)
geom_half_range() takes advantage of the vertical symmetry of
transcript annotation by plotting only half of a range on the top or
bottom of a transcript structure. One use case of geom_half_range() is
to visualize the differences between transcript structure more clearly.
# extract exons and cds for the two transcripts to be compared
sod1_201_exons <- sod1_exons %>% dplyr::filter(transcript_name == "SOD1-201")
sod1_201_cds <- sod1_cds %>% dplyr::filter(transcript_name == "SOD1-201")
sod1_202_exons <- sod1_exons %>% dplyr::filter(transcript_name == "SOD1-202")
sod1_202_cds <- sod1_cds %>% dplyr::filter(transcript_name == "SOD1-202")
sod1_201_202_plot <- sod1_201_exons %>%
ggplot(aes(
xstart = start,
xend = end,
y = "SOD1-201/202"
)) +
geom_half_range(
fill = "white",
height = 0.125
) +
geom_half_range(
data = sod1_201_cds
) +
geom_intron(
data = to_intron(sod1_201_exons, "transcript_name")
) +
geom_half_range(
data = sod1_202_exons,
range.orientation = "top",
fill = "white",
height = 0.125
) +
geom_half_range(
data = sod1_202_cds,
range.orientation = "top",
fill = "purple"
) +
geom_intron(
data = to_intron(sod1_202_exons, "transcript_name")
)
sod1_201_202_plot
As a ggplot2 extension, ggtranscript inherits the the familiarity
and functionality of ggplot2. For instance, by leveraging
coord_cartesian() users can zoom in on regions of interest.
sod1_201_202_plot + coord_cartesian(xlim = c(31659500, 31660000))
geom_junction() enables to plotting of junction curves, which can be
overlaid across transcript structures. geom_junction_label_repel()
adds a label to junction curves, which can often be useful to mark
junctions with a metric of their usage such as read counts.
# ggtranscript includes a set of example (unannotated) junctions
# originating from GTEx and downloaded via the Bioconductor package snapcount
sod1_junctions
#> # A tibble: 5 × 5
#> seqnames start end strand mean_count
#> <fct> <int> <int> <fct> <dbl>
#> 1 chr21 31659787 31666448 + 0.463
#> 2 chr21 31659842 31660554 + 0.831
#> 3 chr21 31659842 31663794 + 0.316
#> 4 chr21 31659842 31667257 + 4.35
#> 5 chr21 31660351 31663789 + 0.324
# add transcript_name to junctions for plotting
sod1_junctions <- sod1_junctions %>%
dplyr::mutate(transcript_name = "SOD1-201")
sod1_201_exons %>%
ggplot(aes(
xstart = start,
xend = end,
y = transcript_name
)) +
geom_range(
fill = "white",
height = 0.25
) +
geom_range(
data = sod1_201_cds
) +
geom_intron(
data = to_intron(sod1_201_exons, "transcript_name")
) +
geom_junction(
data = sod1_junctions,
junction.y.max = 0.5
) +
geom_junction_label_repel(
data = sod1_junctions,
aes(label = round(mean_count, 2)),
junction.y.max = 0.5
)
Alternatively, users may prefer to map junction read counts to the
thickness of the junction curves. As a ggplot2 extension, this can be
done intuitively by modifying the size aes() of geom_junction(). In
addition, by modifying ggplot2 scales and themes, users can easily
create informative, publication-ready plots.
sod1_201_exons %>%
ggplot(aes(
xstart = start,
xend = end,
y = transcript_name
)) +
geom_range(
fill = "white",
height = 0.25
) +
geom_range(
data = sod1_201_cds
) +
geom_intron(
data = to_intron(sod1_201_exons, "transcript_name")
) +
geom_junction(
data = sod1_junctions,
aes(size = mean_count),
junction.y.max = 0.5,
ncp = 30,
colour = "purple"
) +
scale_size_continuous(range = c(0.1, 1), guide = "none") +
xlab("Genomic position (chr21)") +
ylab("Transcript name") +
theme_bw()
Citation
citation("ggtranscript")
#>
#> Gustavsson EK, Zhang D, Reynolds RH, Garcia-Ruiz S, Ryten M (2022).
#> "ggtranscript: an R package for the visualization and interpretation of
#> transcript isoforms using ggplot2." _Bioinformatics_. doi:
#> 10.1093/bioinformatics/btac409 (URL:
#> https://doi.org/10.1093/bioinformatics/btac409), <URL:
#> https://academic.oup.com/bioinformatics/article/38/15/3844/6617821>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2},
#> author = {Emil K Gustavsson and David Zhang and Regina H Reynolds and Sonia Garcia-Ruiz and Mina Ryten},
#> year = {2022},
#> journal = {Bioinformatics},
#> doi = {https://doi.org/10.1093/bioinformatics/btac409},
#> url = {https://academic.oup.com/bioinformatics/article/38/15/3844/6617821},
#> }Credits
ggtranscriptwas developed using biocthis.