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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(ggtranscript)
library(ggplot2)
library(rtracklayer)
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> 
#> Attaching package: 'BiocGenerics'
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#>     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
#>     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
#>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
#>     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
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ggtranscript is designed to make it easy to visualize transcript structure and annotation using ggplot2.

As the intended users are those who work with genetic and/or transcriptomic data in R, this tutorial assumes a basic understanding of transcript annotation and familiarity with ggplot2.


Input data

Example data

In order to showcase the package’s functionality, ggtranscript includes example transcript annotation for the genes SOD1 and PKNOX1, as well as a set of unannotated junctions associated with SOD1. These specific genes are unimportant, chosen arbitrarily for illustration. However, it worth noting that the input data for ggtranscript, as a ggplot2 extension, is required be a data.frame or tibble.


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

pknox1_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       4.30e7 4.30e7 +      gene  PKNOX1    NA              NA              
#> 2 21       4.30e7 4.30e7 +      tran… PKNOX1    PKNOX1-203      protein_coding  
#> 3 21       4.30e7 4.30e7 +      exon  PKNOX1    PKNOX1-203      protein_coding  
#> 4 21       4.30e7 4.30e7 +      exon  PKNOX1    PKNOX1-203      protein_coding  
#> 5 21       4.30e7 4.30e7 +      exon  PKNOX1    PKNOX1-203      protein_coding  
#> 6 21       4.30e7 4.30e7 +      exon  PKNOX1    PKNOX1-203      protein_coding

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

Importing data from a gtf

You may be asking, what if I have a gtf file or a GRanges object? The below demonstrates how to wrangle a gtf into the required format for ggtranscript and extract the relevant annotation for a particular gene of interest.

For the purposes of the vignette, here we download a gtf (Ensembl version 105), then load the gtf into R using rtracklayer::import().


# download ens 105 gtf into a temporary directory
gtf_path <- file.path(tempdir(), "Homo_sapiens.GRCh38.105.chr.gtf.gz")

download.file(
    paste0(
        "http://ftp.ensembl.org/pub/release-105/gtf/homo_sapiens/",
        "Homo_sapiens.GRCh38.105.chr.gtf.gz"
    ),
    destfile = gtf_path
)

gtf <- rtracklayer::import(gtf_path)

class(gtf)
#> [1] "GRanges"
#> attr(,"package")
#> [1] "GenomicRanges"

To note, the loaded gtf is a GRanges class object. The input data for ggtranscript, as a ggplot2 extension, is required be a data.frame or tibble. We can convert a GRanges to a data.frame using as.data.frame or a tibble via dplyr::as_tibble(). Either is fine with respect to ggtranscript, however we prefer tibbles over data.frames for several reasons.


gtf <- gtf %>% dplyr::as_tibble()

class(gtf)
#> [1] "tbl_df"     "tbl"        "data.frame"

Now that the gtf is a tibble (or data.frame object), we can dplyr::filter() rows and dplyr::select() columns to keep the annotation columns required for any specific gene of interest. Here, we illustrate how you would obtain the annotation for the gene SOD1, ready for plotting with ggtranscript.


# filter your gtf for the gene of interest, here "SOD1"
gene_of_interest <- "SOD1"

sod1_annotation_from_gtf <- gtf %>% 
  dplyr::filter(
    !is.na(gene_name), 
    gene_name == gene_of_interest
  ) 

# extract the required annotation columns
sod1_annotation_from_gtf <- sod1_annotation_from_gtf %>% 
  dplyr::select(
    seqnames,
    start,
    end,
    strand,
    type,
    gene_name,
    transcript_name,
    transcript_biotype
  )

sod1_annotation_from_gtf %>% 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

Importing data from a bed file

If users would like to plot ranges from a bed file using ggtranscript, they can first import the bed file into R using rtracklayer::import.bed(). This method will create a UCSCData object.


# for the example, we'll use the test bed file provided by rtracklayer 
test_bed <- system.file("tests/test.bed", package = "rtracklayer")

bed <- rtracklayer::import.bed(test_bed)

class(bed)
#> [1] "UCSCData"
#> attr(,"package")
#> [1] "rtracklayer"

A UCSCData object can be coerced into a tibble, a data structure which can be plotted using ggplot2/ggtranscript, using dplyr::as_tibble().


bed <- bed %>% dplyr::as_tibble()

class(bed)
#> [1] "tbl_df"     "tbl"        "data.frame"

bed %>% head()
#> # A tibble: 5 × 12
#>   seqnames   start    end width strand name  score itemRgb thick.start thick.end
#>   <fct>      <int>  <int> <int> <fct>  <chr> <dbl> <chr>         <int>     <int>
#> 1 chr7      1.27e8 1.27e8  1167 +      Pos1      0 #FF0000   127471197 127472363
#> 2 chr7      1.27e8 1.27e8  1167 +      Pos2      2 #FF0000   127472364 127473530
#> 3 chr7      1.27e8 1.27e8  1167 -      Neg1      0 #FF0000   127473531 127474697
#> 4 chr9      1.27e8 1.27e8  1167 +      Pos3      5 #FF0000   127474698 127475864
#> 5 chr9      1.27e8 1.27e8  1167 -      Neg2      5 #0000FF   127475865 127477031
#> # … with 2 more variables: thick.width <int>, blocks <list>


Basic usage

Required aesthetics

ggtranscript introduces 5 new geoms designed to simplify the visualization of transcript structure and annotation; geom_range(), geom_half_range(), geom_intron(), geom_junction() and geom_junction_label_repel(). The required aesthetics (aes()) for these geoms are designed to match the data formats which are widely used in genetic and transcriptomic analyses:

Required aes() Type Description Required by
xstart integer Start position in base pairs All geoms
xend integer End position in base pairs All geoms
y charactor or factor The group used for the y axis, most often a transcript id or name All geoms
label integer or charactor Variable used to label junction curves Only geom_junction_label_repel()

Plotting exons and introns

In the simplest case, the core components of transcript structure are exons and introns, which can be plotted using geom_range() and geom_intron(). In order to facilitate this, ggtranscript also provides to_intron(), which converts exon co-ordinates into introns. Therefore, you can plot transcript structures with only exons as input and just a few lines of code.

📌: As ggtranscript geoms share required aesthetics, it is recommended to set these via ggplot(), rather than in the individual geom_*() calls to avoid code duplication.


# 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)
    )

Differentiating UTRs from the coding sequence

As suggested by it’s name, geom_range() is designed to visualize range-based transcript annotation. This includes but is not limited to exons. For instance, for protein coding transcripts it can be useful to visually distinguish the coding sequence (CDS) of a transcript from it’s UTRs. This can be achieved by adjusting the height and fill of geom_range() and overlaying the CDS on top of the exons (including 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,
    )

Plotting junctions

geom_junction() plots curved lines that are intended to represent junction reads. Junctions are reads obtained through RNA-sequencing (RNA-seq) data that map with gapped alignment to the genome. Often, this gap is indicative of a splicing event, but can also originate from other genomic events such as indels.

It can be useful to visually overlay junctions on top of an existing transcript structure. For example, this can help to understand which existing transcripts are expressed in the RNA-seq sample or inform the location or interpretation of the novel splice sites.

geom_junction_label_repel() adds labels to junction curves. This can useful for labeling junctions with a measure of their expression or support such as read counts or percent-spliced-in. Alternatively, you may choose to visually map this measure to the thickness of the junction curves by adjusting the the size aes(). Or, as shown below, both of these options can be combined.


# extract exons and cds for the MANE-select transcript
sod1_201_exons <- sod1_exons %>% dplyr::filter(transcript_name == "SOD1-201")
sod1_201_cds <- sod1_cds %>% dplyr::filter(transcript_name == "SOD1-201")

# add transcript name column 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,
    aes(size = mean_count),
    junction.y.max = 0.5
  ) +
  geom_junction_label_repel(
    data = sod1_junctions,
    aes(label = round(mean_count, 2)),
    junction.y.max = 0.5
  ) + 
  scale_size_continuous(range = c(0.1, 1))


Visualizing transcript structure differences

Context

One of the primary reasons for visualizing transcript structures is to better observe the differences between them. Often this can be achieved by simply plotting the exons and introns as shown in basic usage. However, for longer, complex transcripts this may not be as straight forward.

For example, the transcripts of PKNOX1 have relatively long introns, which makes the comparison between transcript structures (especially small differences in exons) more difficult.

📌: For relatively short introns, strand arrows may overlap exons. In such cases, the arrow.min.intron.length parameter of geom_intron() can be used to set the minimum intron length for a strand arrow to be plotted.


# extract exons
pknox1_exons <- pknox1_annotation %>% dplyr::filter(type == "exon")

pknox1_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range(
        aes(fill = transcript_biotype)
    ) +
    geom_intron(
        data = to_intron(pknox1_exons, "transcript_name"),
        aes(strand = strand), 
        arrow.min.intron.length = 3500
    )

Improving transcript structure visualisation using shorten_gaps()

ggtranscript provides the helper function shorten_gaps(), which reduces the size of the gaps (regions that do not overlap an exon). shorten_gaps() then rescales the exon and intron co-ordinates, preserving the original exon alignment. This allows you to hone in the differences of interest, such as the exonic structure.

📌: The rescaled co-ordinates returned by shorten_gaps() will not match the original genomic positions. Therefore, it is recommended that shorten_gaps() is used for visualizations purposes only.


# extract exons
pknox1_exons <- pknox1_annotation %>% dplyr::filter(type == "exon")

pknox1_rescaled <- shorten_gaps(
  exons = pknox1_exons, 
  introns = to_intron(pknox1_exons, "transcript_name"), 
  group_var = "transcript_name"
)

# shorten_gaps() returns exons and introns all in one data.frame()
# let's split these for plotting 
pknox1_rescaled_exons <- pknox1_rescaled %>% dplyr::filter(type == "exon") 
pknox1_rescaled_introns <- pknox1_rescaled %>% dplyr::filter(type == "intron") 

pknox1_rescaled_exons %>% 
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range(
        aes(fill = transcript_biotype)
    ) +
    geom_intron(
        data = pknox1_rescaled_introns,
        aes(strand = strand), 
        arrow.min.intron.length = 300
    )

Comparing between two transcripts using geom_half_range()

If you are interested in the differences between two transcripts, you can use geom_half_range() whilst adjusting range.orientation to plot the exons from each on the opposite sides of the transcript structure. This can reveal small differences in exon structure, such as those observed here at the 5’ ends of PKNOX1-201 and PKNOX1-203.


# extract the two transcripts to be compared
pknox1_rescaled_201_exons <- pknox1_rescaled_exons %>% 
  dplyr::filter(transcript_name == "PKNOX1-201")
pknox1_rescaled_203_exons <- pknox1_rescaled_exons %>% 
  dplyr::filter(transcript_name == "PKNOX1-203")

pknox1_rescaled_201_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = "PKNOX1-201/203"
    )) +
    geom_half_range() +
    geom_intron(
        data = to_intron(pknox1_rescaled_201_exons, "transcript_name"), 
        arrow.min.intron.length = 300
    ) +
    geom_half_range(
        data = pknox1_rescaled_203_exons,
        range.orientation = "top", 
        fill = "purple"
    ) +
    geom_intron(
        data = to_intron(pknox1_rescaled_203_exons, "transcript_name"), 
        arrow.min.intron.length = 300
    )

Comparing many transcripts to a single reference transcript using to_diff()

Sometimes, it can be useful to visualize the differences of several transcripts with respect to one transcript. For example, you may be interested in how other transcripts differ in structure to the MANE-select transcript. This exploration can reveal whether certain important regions are missing or novel regions are added, hinting at differences in transcript function.

to_diff() is a helper function designed for this situation. Here, we apply this to PKNOX1, finding the differences between all other transcripts and the MANE-select transcript (PKNOX1-201).

📌: Although below, we apply to_diff() to the rescaled exons and intron (outputted by shorten_gaps()), to_diff() can also be applied to the original, unscaled transcripts with the same effect.


mane <- pknox1_rescaled_201_exons

not_mane <- pknox1_rescaled_exons %>% 
  dplyr::filter(transcript_name != "PKNOX1-201")

pknox1_rescaled_diffs <- to_diff(
    exons = not_mane,
    ref_exons = mane,
    group_var = "transcript_name"
)

pknox1_rescaled_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range() +
    geom_intron(
        data = pknox1_rescaled_introns,
        arrow.min.intron.length = 300
    ) +
    geom_range(
        data = pknox1_rescaled_diffs,
        aes(fill = diff_type),
        alpha = 0.2
    )


Integrating existing ggplot2 functionality

As a ggplot2 extension, ggtranscript inherits ggplot2’s familiarity and flexibility, enabling users to intuitively adjust aesthetics, parameters, scales etc as well as complement ggtranscript geoms with existing ggplot2 geoms to create informative, publication-ready plots.

Below is a list outlining some examples of complementing ggtranscript with existing ggplot2 functionality that we have found useful:


base_sod1_plot <- 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)
  ) 

base_sod1_plot + 
  geom_text(
    data = add_exon_number(sod1_exons, "transcript_name"),
    aes(
      x = (start + end) / 2, # plot label at midpoint of exon
      label = exon_number
    ),
    size = 3.5,
    nudge_y = 0.4
  )


base_sod1_plot + 
  coord_cartesian(xlim = c(31665500, 31669000))


example_mutation <- dplyr::tibble(
  transcript_name = "SOD1-204", 
  position = 31661600
)

# xstart and xend are set here to override the default aes()
base_sod1_plot + 
  geom_vline(
    data = example_mutation, 
    aes(
      xintercept = position, 
      xstart = NULL,
      xend = NULL
      ), 
    linetype = 2,
    colour = "red"
  )

  • Beautifying plots using themes and scales

base_sod1_plot + 
  theme_bw() + 
  scale_x_continuous(name = "Position") + 
  scale_y_discrete(name = "Transcript name") + 
  scale_fill_discrete(
    name = "Transcript biotype",
    labels = c("Processed transcript", "Protein-coding")
    )


Session info

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#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.0 (2022-04-22)
#>  os       Ubuntu 20.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language en
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       UTC
#>  date     2022-05-20
#>  pandoc   2.17.1.1 @ /usr/local/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
#>  package              * version  date (UTC) lib source
#>  Biobase                2.56.0   2022-04-26 [1] Bioconductor
#>  BiocGenerics         * 0.42.0   2022-04-26 [1] Bioconductor
#>  BiocIO                 1.6.0    2022-04-26 [1] Bioconductor
#>  BiocManager            1.30.18  2022-05-18 [1] RSPM (R 4.2.0)
#>  BiocParallel           1.30.2   2022-05-15 [1] Bioconductor
#>  BiocStyle            * 2.24.0   2022-04-26 [1] Bioconductor
#>  Biostrings             2.64.0   2022-04-26 [1] Bioconductor
#>  bitops                 1.0-7    2021-04-24 [1] RSPM (R 4.1.0)
#>  bookdown               0.26     2022-04-15 [1] RSPM (R 4.2.0)
#>  bslib                  0.3.1    2021-10-06 [1] RSPM (R 4.2.0)
#>  cachem                 1.0.6    2021-08-19 [2] RSPM (R 4.2.0)
#>  cli                    3.3.0    2022-04-25 [2] RSPM (R 4.2.0)
#>  colorspace             2.0-3    2022-02-21 [1] RSPM (R 4.2.0)
#>  crayon                 1.5.1    2022-03-26 [2] RSPM (R 4.2.0)
#>  DelayedArray           0.22.0   2022-04-26 [1] Bioconductor
#>  desc                   1.4.1    2022-03-06 [2] RSPM (R 4.2.0)
#>  digest                 0.6.29   2021-12-01 [2] RSPM (R 4.2.0)
#>  dplyr                * 1.0.9    2022-04-28 [1] RSPM (R 4.2.0)
#>  ellipsis               0.3.2    2021-04-29 [2] RSPM (R 4.2.0)
#>  evaluate               0.15     2022-02-18 [2] RSPM (R 4.2.0)
#>  fansi                  1.0.3    2022-03-24 [2] RSPM (R 4.2.0)
#>  farver                 2.1.0    2021-02-28 [1] RSPM (R 4.1.0)
#>  fastmap                1.1.0    2021-01-25 [2] RSPM (R 4.2.0)
#>  fs                     1.5.2    2021-12-08 [2] RSPM (R 4.2.0)
#>  generics               0.1.2    2022-01-31 [1] RSPM (R 4.2.0)
#>  GenomeInfoDb         * 1.32.2   2022-05-15 [1] Bioconductor
#>  GenomeInfoDbData       1.2.8    2022-05-17 [1] Bioconductor
#>  GenomicAlignments      1.32.0   2022-04-26 [1] Bioconductor
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