ggtranscript

https://dzhang32.github.io/ggtranscript/
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ggtranscript is a ggplot2 extension that makes it easy to visualize transcript structure and...
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### Install ggtranscript from GitHub

Source: https://dzhang32.github.io/ggtranscript/index.html

Install the development version of ggtranscript from GitHub using devtools. Requires the devtools package to be installed first.

```R
# you can install the development version of ggtranscript from GitHub:
# install.packages("devtools")
devtools::install_github("dzhang32/ggtranscript")
```

--------------------------------

### Example: comparing transcripts to reference with to_diff()

Source: https://dzhang32.github.io/ggtranscript/reference/to_diff.html

Complete example demonstrating how to load the package, inspect example data, filter exons, identify a reference transcript, compute differences with to_diff(), and visualize the results using geom_range() and geom_intron().

```r
library(magrittr)
library(ggplot2)

# 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
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>

# extract exons
sod1_exons <- sod1_annotation %>% dplyr::filter(type == "exon")
sod1_exons %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type  gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct> <chr>     <chr>          
#> 1 21       31659666 31659784 +      exon  SOD1      SOD1-202       
#> 2 21       31663790 31663886 +      exon  SOD1      SOD1-202       
#> 3 21       31666449 31666518 +      exon  SOD1      SOD1-202       
#> 4 21       31667258 31667375 +      exon  SOD1      SOD1-202       
#> 5 21       31668471 31668931 +      exon  SOD1      SOD1-202       
#> 6 21       31659693 31659841 +      exon  SOD1      SOD1-204       
#> # ℹ 1 more variable: transcript_biotype <chr>

# for this example, let's compare transcripts to the MANE-select transcript
sod1_mane <- sod1_exons %>% dplyr::filter(transcript_name == "SOD1-201")
sod1_not_mane <- sod1_exons %>% dplyr::filter(transcript_name != "SOD1-201")

# to_diff() obtains the differences between the exons as ranges
sod1_diffs <- to_diff(
    exons = sod1_not_mane,
    ref_exons = sod1_mane,
    group_var = "transcript_name"
)

sod1_diffs %>% head()
#>   seqnames    start      end width strand type  diff_type transcript_name
#> 1       21 31659666 31659692    27      + diff not_in_ref        SOD1-202
#> 2       21 31659785 31659841    57      + diff     in_ref        SOD1-202
#> 3       21 31661549 31661734   186      + diff not_in_ref        SOD1-204
#> 4       21 31667342 31667375    34      + diff     in_ref        SOD1-204
#> 5       21 31668471 31668931   461      + diff     in_ref        SOD1-204
#> 6       21 31659693 31659708    16      + diff     in_ref        SOD1-203

# using geom_range(), it can be useful to visually overlay
# the differences on top of the transcript annotation
sod1_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range() +
    geom_intron(
        data = to_intron(sod1_exons, "transcript_name")
    ) +
    geom_range(
        data = sod1_diffs,
        ggplot2::aes(fill = diff_type),
        alpha = 0.2
    )
```

--------------------------------

### Example: convert exons to introns and visualize

Source: https://dzhang32.github.io/ggtranscript/reference/to_intron.html

Complete example that loads required libraries, filters exons from the example annotation, converts them to introns using to_intron(), and visualizes the exons and introns with geom_range() and geom_intron().

```r
library(magrittr)
library(ggplot2)

# 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
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>

# extract exons
sod1_exons <- sod1_annotation %>% dplyr::filter(type == "exon")
sod1_exons %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type  gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct> <chr>     <chr>          
#> 1 21       31659666 31659784 +      exon  SOD1      SOD1-202       
#> 2 21       31663790 31663886 +      exon  SOD1      SOD1-202       
#> 3 21       31666449 31666518 +      exon  SOD1      SOD1-202       
#> 4 21       31667258 31667375 +      exon  SOD1      SOD1-202       
#> 5 21       31668471 31668931 +      exon  SOD1      SOD1-202       
#> 6 21       31659693 31659841 +      exon  SOD1      SOD1-204       
#> # ℹ 1 more variable: transcript_biotype <chr>

# to_intron() is a helper function included in ggtranscript
# which is useful for converting exon co-ordinates to introns
sod1_introns <- sod1_exons %>% to_intron(group_var = "transcript_name")
sod1_introns %>% head()
#> # A tibble: 6 × 8
#>   seqnames strand type   gene_name transcript_name transcript_biotype      start
#>   <fct>    <fct>  <chr>  <chr>     <chr>           <chr>                   <int>
#> 1 21       +      intron SOD1      SOD1-204        processed_transcript 31659841
#> 2 21       +      intron SOD1      SOD1-202        protein_coding       31659784
#> 3 21       +      intron SOD1      SOD1-204        processed_transcript 31661734
#> 4 21       +      intron SOD1      SOD1-201        protein_coding       31659841
#> 5 21       +      intron SOD1      SOD1-203        processed_transcript 31660708
#> 6 21       +      intron SOD1      SOD1-202        protein_coding       31663886
#> # ℹ 1 more variable: end <int>

# this can be particular useful when combined with
# geom_range() and geom_intron()
# to visualize the core components of transcript annotation
sod1_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range() +
    geom_intron(
        data = to_intron(sod1_exons, "transcript_name")
    )
```

--------------------------------

### Load libraries and inspect example data

Source: https://dzhang32.github.io/ggtranscript/reference/geom_junction.html

Loads magrittr and ggplot2, then displays the head of sod1_annotation and sod1_junctions to illustrate the package's example data. The annotation includes transcript features, and junctions originate from GTEx via snapcount.

```R
library(magrittr)
library(ggplot2)

# 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
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>

# as well as 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
```

--------------------------------

### Load libraries and inspect example annotation data

Source: https://dzhang32.github.io/ggtranscript/reference/geom_range.html

Loads magrittr and ggplot2, then displays the first rows of the sod1_annotation tibble to illustrate the package's example data structure.

```r
library(magrittr)
library(ggplot2)

# 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
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>
```

--------------------------------

### Example: labeling junctions with geom_junction_label_repel

Source: https://dzhang32.github.io/ggtranscript/reference/geom_junction_label_repel.html

This example loads required libraries, inspects example data, extracts exons, adds transcript names to junctions, and creates a plot with geom_junction_label_repel to label junction counts. It uses the same junction.y.max parameter as geom_junction to ensure consistent curve midpoints.

```R
library(magrittr)
library(ggplot2)

# 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
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>

# as well as 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

# extract exons
sod1_exons <- sod1_annotation %>% dplyr::filter(
    type == "exon",
    transcript_name == "SOD1-201"
)
sod1_exons %>% head()
#> # A tibble: 5 × 8
#>   seqnames    start      end strand type  gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct> <chr>     <chr>          
#> 1 21       31659693 31659841 +      exon  SOD1      SOD1-201       
#> 2 21       31663790 31663886 +      exon  SOD1      SOD1-201       
#> 3 21       31666449 31666518 +      exon  SOD1      SOD1-201       
#> 4 21       31667258 31667375 +      exon  SOD1      SOD1-201       
#> 5 21       31668471 31668931 +      exon  SOD1      SOD1-201       
#> # ℹ 1 more variable: transcript_biotype <chr>

# add transcript_name to junctions for plotting
sod1_junctions <- sod1_junctions %>%
    dplyr::mutate(transcript_name = "SOD1-201")

# geom_junction_label_repel() can be used to label junctions
base <- sod1_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range() +
    geom_intron(
        data = to_intron(sod1_exons, "transcript_name")
    )

# this can be useful to label junctions with their counts
base +
    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
    )
```

--------------------------------

### Load libraries and inspect example annotation

Source: https://dzhang32.github.io/ggtranscript/reference/geom_intron.html

Loads magrittr and ggplot2, then displays the first rows of the pknox1_annotation dataset included with ggtranscript to illustrate its structure.

```r
library(magrittr)
library(ggplot2)

# to illustrate the package's functionality
# ggtranscript includes example transcript annotation
pknox1_annotation %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type       gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct>      <chr>     <chr>          
#> 1 21       42974510 43033931 +      gene       PKNOX1    NA             
#> 2 21       42974510 43033931 +      transcript PKNOX1    PKNOX1-203     
#> 3 21       42974510 42974664 +      exon       PKNOX1    PKNOX1-203     
#> 4 21       43004326 43004432 +      exon       PKNOX1    PKNOX1-203     
#> 5 21       43007491 43007618 +      exon       PKNOX1    PKNOX1-203     
#> 6 21       43013068 43013238 +      exon       PKNOX1    PKNOX1-203     
#> # ℹ 1 more variable: transcript_biotype <chr>
```

--------------------------------

### R example: add_utr() workflow with PKNOX1 annotation

Source: https://dzhang32.github.io/ggtranscript/reference/add_utr.html

This example demonstrates the full add_utr() workflow using the built-in pknox1_annotation dataset. It shows how to extract exons and CDS, manually add stop codons to the CDS (since Ensembl omits them), call add_utr() to generate UTR ranges, and optionally rescale introns with shorten_gaps() for visualization. The code includes output from head() calls showing the resulting tibbles.

```R
library(magrittr)
library(ggplot2)

# to illustrate the package's functionality
# ggtranscript includes example transcript annotation
pknox1_annotation %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type       gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct>      <chr>     <chr>          
#> 1 21       42974510 43033931 +      gene       PKNOX1    NA             
#> 2 21       42974510 43033931 +      transcript PKNOX1    PKNOX1-203     
#> 3 21       42974510 42974664 +      exon       PKNOX1    PKNOX1-203     
#> 4 21       43004326 43004432 +      exon       PKNOX1    PKNOX1-203     
#> 5 21       43007491 43007618 +      exon       PKNOX1    PKNOX1-203     
#> 6 21       43013068 43013238 +      exon       PKNOX1    PKNOX1-203     
#> # ℹ 1 more variable: transcript_biotype <chr>

# extract exons
pknox1_exons <- pknox1_annotation %>% dplyr::filter(type == "exon")
pknox1_exons %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type  gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct> <chr>     <chr>          
#> 1 21       42974510 42974664 +      exon  PKNOX1    PKNOX1-203     
#> 2 21       43004326 43004432 +      exon  PKNOX1    PKNOX1-203     
#> 3 21       43007491 43007618 +      exon  PKNOX1    PKNOX1-203     
#> 4 21       43013068 43013238 +      exon  PKNOX1    PKNOX1-203     
#> 5 21       43016908 43017007 +      exon  PKNOX1    PKNOX1-203     
#> 6 21       43018133 43018230 +      exon  PKNOX1    PKNOX1-203     
#> # ℹ 1 more variable: transcript_biotype <chr>

# extract cds
pknox1_cds <- pknox1_annotation %>% dplyr::filter(type == "CDS")
pknox1_cds %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type  gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct> <chr>     <chr>          
#> 1 21       43013068 43013238 +      CDS   PKNOX1    PKNOX1-203     
#> 2 21       43016908 43017007 +      CDS   PKNOX1    PKNOX1-203     
#> 3 21       43018133 43018230 +      CDS   PKNOX1    PKNOX1-203     
#> 4 21       43021303 43021431 +      CDS   PKNOX1    PKNOX1-203     
#> 5 21       43024871 43024947 +      CDS   PKNOX1    PKNOX1-203     
#> 6 21       43028702 43028874 +      CDS   PKNOX1    PKNOX1-203     
#> # ℹ 1 more variable: transcript_biotype <chr>

# the CDS definition originating from the Ensembl reference annotation
# does not include the stop codon
# we must incorporate the stop codons into the CDS manually
# by adding 3 base pairs to the end of the CDS of each transcript
pknox1_cds_w_stop <- pknox1_cds %>%
    dplyr::group_by(transcript_name) %>%
    dplyr::mutate(
        end = ifelse(end == max(end), end + 3, end)
    ) %>%
    dplyr::ungroup()

# add_utr() adds ranges that represent the UTRs
pknox1_cds_utr <- add_utr(
    pknox1_exons,
    pknox1_cds_w_stop,
    group_var = "transcript_name"
)

pknox1_cds_utr %>% head()
#> # A tibble: 6 × 9
#>   seqnames    start      end width strand transcript_name type  gene_name
#>   <fct>       <int>    <int> <int> <fct>  <chr>           <chr> <chr>    
#> 1 21       42974510 42974664   155 +      PKNOX1-203      UTR   NA       
#> 2 21       43004326 43004432   107 +      PKNOX1-203      UTR   NA       
#> 3 21       43007491 43007618   128 +      PKNOX1-203      UTR   NA       
#> 4 21       43013068 43013238   171 +      PKNOX1-203      CDS   PKNOX1   
#> 5 21       43016908 43017007   100 +      PKNOX1-203      CDS   PKNOX1   
#> 6 21       43018133 43018230    98 +      PKNOX1-203      CDS   PKNOX1   
#> # ℹ 1 more variable: transcript_biotype <chr>

# this can be useful when combined with shorten_gaps()
# to visualize transcripts with long introns whilst differentiating UTRs
pknox1_cds_utr_rescaled <-
    shorten_gaps(
        exons = pknox1_cds_utr,
        introns = to_intron(pknox1_cds_utr, "transcript_name"),
        group_var = "transcript_name"
    )

pknox1_cds_utr_rescaled %>%
    dplyr::filter(type == "CDS") %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range() +
    geom_range(
        data = pknox1_cds_utr_rescaled %>% dplyr::filter(type == "UTR"),
        height = 0.25,
        fill = "white"
    ) +
    geom_intron(
        data = to_intron(
            pknox1_cds_utr_rescaled %>% dplyr::filter(type != "intron"),
            "transcript_name"
        ),
        arrow.min.intron.length = 110
    )
```

--------------------------------

### Example workflow: add exon numbers and plot with ggplot2

Source: https://dzhang32.github.io/ggtranscript/reference/add_exon_number.html

Complete example from the documentation. Loads libraries, inspects the sod1_annotation dataset, filters exons, adds exon numbers, creates a base plot with geom_range and geom_intron, and shows two annotation approaches: geom_text and ggrepel::geom_label_repel. Requires the 'strand' column and group_var for multiple transcripts.

```r
library(magrittr)
library(ggplot2)

# 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
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>

# extract exons
sod1_exons <- sod1_annotation %>% dplyr::filter(type == "exon")
sod1_exons %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type  gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct> <chr>     <chr>          
#> 1 21       31659666 31659784 +      exon  SOD1      SOD1-202       
#> 2 21       31663790 31663886 +      exon  SOD1      SOD1-202       
#> 3 21       31666449 31666518 +      exon  SOD1      SOD1-202       
#> 4 21       31667258 31667375 +      exon  SOD1      SOD1-202       
#> 5 21       31668471 31668931 +      exon  SOD1      SOD1-202       
#> 6 21       31659693 31659841 +      exon  SOD1      SOD1-204       
#> # ℹ 1 more variable: transcript_biotype <chr>

# add the exon number for each transcript
sod1_exons <- sod1_exons %>% add_exon_number(group_var = "transcript_name")

base <- sod1_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    )) +
    geom_range() +
    geom_intron(
        data = to_intron(sod1_exons, "transcript_name"),
        strand = "+"
    )

# it can be useful to annotate exons with their exon number
# using ggplot2::geom_text()
base +
    geom_text(aes(
        x = (start + end) / 2, # plot label at midpoint of exon
        label = exon_number
    ),
    size = 3.5,
    nudge_y = 0.4
    )


# Or alternatively, using ggrepel::geom_label_repel()
# to separate labels from exons
base +
    ggrepel::geom_label_repel(ggplot2::aes(
        x = (start + end) / 2,
        label = exon_number
    ),
    size = 3.5,
    min.segment.length = 0
    )
```

--------------------------------

### Plot exons and introns with geom_range and geom_intron

Source: https://dzhang32.github.io/ggtranscript/index.html

Load required libraries and use ggtranscript's geom_range and geom_intron to plot exons and introns from example annotation data. The example uses the sod1_annotation dataset included with the package and converts exons to introns with to_intron().

```R
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
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      <NA>           
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>

# 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)
    )
```

--------------------------------

### View PKNOX1 annotation data with head()

Source: https://dzhang32.github.io/ggtranscript/articles/ggtranscript.html

Displays the first rows of the pknox1_annotation tibble, showing the same required columns as the SOD1 example. The output is a tibble with 6 rows and 8 columns.

```r
pknox1_annotation %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type       gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct>      <chr>     <chr>          
#> 1 21       42974510 43033931 +      gene       PKNOX1    NA             
#> 2 21       42974510 43033931 +      transcript PKNOX1    PKNOX1-203     
#> 3 21       42974510 42974664 +      exon       PKNOX1    PKNOX1-203     
#> 4 21       43004326 43004432 +      exon       PKNOX1    PKNOX1-203     
#> 5 21       43007491 43007618 +      exon       PKNOX1    PKNOX1-203     
#> 6 21       43013068 43013238 +      exon       PKNOX1    PKNOX1-203     
#> # ℹ 1 more variable: transcript_biotype <chr>
```

--------------------------------

### Import a BED file with rtracklayer::import.bed()

Source: https://dzhang32.github.io/ggtranscript/articles/ggtranscript.html

Imports a BED file using rtracklayer::import.bed(), creating a UCSCData object. The example uses the test BED file provided by rtracklayer.

```r
# 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"
```

--------------------------------

### Create base plot with exons

Source: https://dzhang32.github.io/ggtranscript/reference/geom_range.html

Builds a ggplot object 'base' using sod1_exons with xstart, xend, and y aesthetics, which serves as the foundation for subsequent geom_range() and geom_half_range() examples.

```r
base <- sod1_exons %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    ))
```

--------------------------------

### Create base ggplot for introns

Source: https://dzhang32.github.io/ggtranscript/reference/geom_intron.html

Creates a ggplot object using the intron data, mapping xstart, xend, and y aesthetics. This base plot is used in subsequent examples.

```r
base <- pknox1_introns %>%
    ggplot(aes(
        xstart = start,
        xend = end,
        y = transcript_name
    ))
```

--------------------------------

### View SOD1 annotation data with head()

Source: https://dzhang32.github.io/ggtranscript/articles/ggtranscript.html

Displays the first rows of the sod1_annotation tibble, showing the required columns: seqnames, start, end, strand, type, gene_name, transcript_name, and transcript_biotype. The output is a tibble with 6 rows and 8 columns.

```r
sod1_annotation %>% head()
#> # A tibble: 6 × 8
#>   seqnames    start      end strand type        gene_name transcript_name
#>   <fct>       <int>    <int> <fct>  <fct>       <chr>     <chr>          
#> 1 21       31659666 31668931 +      gene        SOD1      NA             
#> 2 21       31659666 31668931 +      transcript  SOD1      SOD1-202       
#> 3 21       31659666 31659784 +      exon        SOD1      SOD1-202       
#> 4 21       31659770 31659784 +      CDS         SOD1      SOD1-202       
#> 5 21       31659770 31659772 +      start_codon SOD1      SOD1-202       
#> 6 21       31663790 31663886 +      exon        SOD1      SOD1-202       
#> # ℹ 1 more variable: transcript_biotype <chr>
```
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