ggtranscript

ggtranscript is a ggplot2 extension that makes it easy visualize transcript structure and annotation.

Installation

devtools::install_github("dzhang32/ggtranscript")

Examples

ggtranscript introduces 3 new geoms designed to visualise transcript annotation; geom_range(), geom_intron() and geom_junction().

geom_range() and geom_intron() enable the plotting of exons and introns. ggtranscript also provides a useful helpful function to_intron to convert exon co-ordinates to the corresponding introns.

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)

# gene annotation for the an example gene (GBA)
gba_ens_105 %>% 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 1        1.55e8 1.55e8 -      gene  GBA       <NA>            <NA>            
#> 2 1        1.55e8 1.55e8 -      tran… GBA       GBA-202         protein_coding  
#> 3 1        1.55e8 1.55e8 -      exon  GBA       GBA-202         protein_coding  
#> 4 1        1.55e8 1.55e8 -      CDS   GBA       GBA-202         protein_coding  
#> 5 1        1.55e8 1.55e8 -      star… GBA       GBA-202         protein_coding  
#> 6 1        1.55e8 1.55e8 -      exon  GBA       GBA-202         protein_coding

# obtain exons
gba_ens_105_exons <- gba_ens_105 %>%
    dplyr::filter(type == "exon")

gba_ens_105_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range(
        aes(fill = transcript_biotype)
    ) +
    geom_intron(
        data = to_intron(gba_ens_105_exons, "transcript_name"),
        aes(strand = strand),
        arrow.min.intron.length = 500,
    )

geom_range can be used for any genomic range based annotation. For example, when plotting protein-coding transcripts, it can be useful to visually distinguish the coding segments from UTRs.

# keeping only the exons from protein coding transcripts
gba_ens_105_exons_prot_cod <- gba_ens_105_exons %>%
    dplyr::filter(transcript_biotype == "protein_coding")

# obtain cds
gba_ens_105_cds <- gba_ens_105 %>%
    dplyr::filter(type == "CDS")

gba_ens_105_exons_prot_cod %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range(
        fill = "white",
        height = 0.25
    ) +
    geom_range(
        data = gba_ens_105_cds
    ) +
    geom_intron(
        data = to_intron(gba_ens_105_exons_prot_cod, "transcript_name"),
        aes(strand = strand),
        arrow.min.intron.length = 500,
    )

When working with short-read RNA-sequencing data, it can be useful to check whether a known transcript structure has junction support using geom_junction().

# using two transcripts as an example
gba_ens_105_201_exons <- gba_ens_105_exons %>%
    dplyr::filter(transcript_name == c("GBA-201"))

gba_ens_105_201_cds <- gba_ens_105_cds %>%
    dplyr::filter(transcript_name == "GBA-201")

# simulate junction data, randomly keeping half of the junctions
gba_ens_105_201_introns <- gba_ens_105_201_exons %>%
    to_intron("transcript_name")

set.seed(32)

gba_ens_105_201_junctions <-
    gba_ens_105_201_introns[sample(seq_len(nrow(gba_ens_105_201_introns)), 6), ]

gba_ens_105_201_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range(
        fill = "white",
        height = 0.25
    ) +
    geom_range(
        data = gba_ens_105_201_cds
    ) +
    geom_intron(
        data = gba_ens_105_201_introns,
        aes(strand = strand),
        arrow.min.intron.length = 500,
    ) +
    geom_junction(
        data = gba_ens_105_201_junctions,
        colour = "red"
    )

Code of Conduct

Please note that the ggtranscript project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Development tools

• Continuous code testing is possible thanks to GitHub actions through usethis, remotes, and rcmdcheck customized to use Bioconductor’s docker containers and BiocCheck.
• Code coverage assessment is possible thanks to codecov and covr.
• The documentation website is automatically updated thanks to pkgdown.
• The code is styled automatically thanks to styler.
• The documentation is formatted thanks to devtools and roxygen2.

This package was developed using biocthis.