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https://showteeth.github.io/ggcoverage/

Visualize and annotate genomic coverage with ggplot2
https://showteeth.github.io/ggcoverage/

base-frequency coverage gc-content gene genome-coverage ggplot2 ideogram peak track transcripts visualization

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Visualize and annotate genomic coverage with ggplot2

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README 

          
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%",

  dpi = 60,

  crop = NULL

)

```



# ggcoverage - Visualize and annotate omics coverage with ggplot2






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## Introduction



The goal of `ggcoverage` is to visualize coverage tracks from genomics, transcriptomics or proteomics data. It contains functions to load data from BAM, BigWig, BedGraph, txt, or xlsx files, create genome/protein coverage plots, and add various annotations including base and amino acid composition, GC content, copy number variation (CNV), genes, transcripts, ideograms, peak highlights, HiC contact maps, contact links and protein features. It is based on and integrates well with `ggplot2`.



It contains three main parts:



* **Load the data**: `ggcoverage` can load BAM, BigWig (.bw), BedGraph, txt/xlsx files from various omics data, including WGS, RNA-seq, ChIP-seq, ATAC-seq, proteomics, et al.

* **Create omics coverage plot**

* **Add annotations**: `ggcoverage` supports six different annotations:

  * **base and amino acid annotation**: Visualize genome coverage at single-nucleotide level with bases and amino acids.

  * **GC annotation**: Visualize genome coverage with GC content

  * **CNV annotation**: Visualize genome coverage with copy number variation (CNV)

  * **gene annotation**: Visualize genome coverage across genes

  * **transcription annotation**: Visualize genome coverage across different transcripts

  * **ideogram annotation**: Visualize the region showing on whole chromosome

  * **peak annotation**: Visualize genome coverage and peak identified

  * **contact map annotation**: Visualize genome coverage with Hi-C contact map

  * **link annotation**: Visualize genome coverage with contacts

  * **peotein feature annotation**: Visualize protein coverage with features



## Installation



`ggcoverage` is an R package distributed as part of the [CRAN repository](https://cran.r-project.org/).

To install the package, start R and enter one of the following commands:

  

```{r install, eval = FALSE}

# install via CRAN (not yet available)

install.packages("ggcoverage")



# OR install via Github

install.package("remotes")

remotes::install_github("showteeth/ggcoverage")

```



In general, it is **recommended** to install from the [Github repository](https://github.com/showteeth/ggcoverage) (updated more regularly).



Once `ggcoverage` is installed, it can be loaded like every other package:



```{r library, message = FALSE, warning = FALSE}

library("ggcoverage")

```



## Manual



`ggcoverage` provides two [vignettes](https://showteeth.github.io/ggcoverage/): 



* **detailed manual**: step-by-step usage

* **customize the plot**: customize the plot and add additional layers



## RNA-seq data



### Load the data



The RNA-seq data used here is from [Transcription profiling by high throughput sequencing of HNRNPC knockdown and control HeLa cells](https://bioconductor.org/packages/release/data/experiment/html/RNAseqData.HNRNPC.bam.chr14.html). We select four samples to use as example: `ERR127307_chr14`, `ERR127306_chr14`, `ERR127303_chr14`, `ERR127302_chr14`, and all bam files were converted to bigwig files with [deeptools](https://deeptools.readthedocs.io/en/develop/).



Load metadata:



```{r load_metadata}

# load metadata

meta_file <-

  system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")

sample_meta <- read.csv(meta_file)

sample_meta

```



Load track files:



```{r load_track}

# track folder

track_folder <- system.file("extdata", "RNA-seq", package = "ggcoverage")

# load bigwig file

track_df <- LoadTrackFile(

  track.folder = track_folder,

  format = "bw",

  region = "chr14:21,677,306-21,737,601",

  extend = 2000,

  meta.info = sample_meta

)

# check data

head(track_df)

```



Prepare mark region:



```{r prepare_mark}

# create mark region

mark_region <- data.frame(

  start = c(21678900, 21732001, 21737590),

  end = c(21679900, 21732400, 21737650),

  label = c("M1", "M2", "M3")

)

# check data

mark_region

```



### Load GTF



To add **gene annotation**, the gtf file should contain **gene_type** and **gene_name** attributes in **column 9**; to add **transcript annotation**, the gtf file should contain a **transcript_name** attribute in **column 9**.



```{r load_gtf}

gtf_file <-

  system.file("extdata", "used_hg19.gtf", package = "ggcoverage")

gtf_gr <- rtracklayer::import.gff(con = gtf_file, format = "gtf")

```



### Basic coverage



The basic coverage plot has **two types**: 



* **facet**: Create subplot for every track (specified by `facet.key`). This is default.

* **joint**: Visualize all tracks in a single plot.



#### joint view



Create line plot for **every sample** (`facet.key = "Type"`) and color by **every sample** (`group.key = "Type"`):



```{r basic_coverage_joint, warning = FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}

basic_coverage <- ggcoverage(

  data = track_df,

  plot.type = "joint",

  facet.key = "Type",

  group.key = "Type",

  mark.region = mark_region,

  range.position = "out"

)



basic_coverage

```



Create **group average line plot** (sample is indicated by `facet.key = "Type"`, group is indicated by `group.key = "Group"`):



```{r basic_coverage_joint_avg, warning = FALSE, fig.height = 4, fig.width = 12, fig.align = "center"}

basic_coverage <- ggcoverage(

  data = track_df,

  plot.type = "joint",

  facet.key = "Type",

  group.key = "Group",

  joint.avg = TRUE,

  mark.region = mark_region,

  range.position = "out"

)



basic_coverage

```



#### Facet view



```{r basic_coverage, warning = FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}

basic_coverage <- ggcoverage(

  data = track_df,

  plot.type = "facet",

  mark.region = mark_region,

  range.position = "out"

)



basic_coverage

```



#### Custom Y-axis style



**Change the Y-axis scale label in/out of plot region with `range.position`**:



```{r basic_coverage_2, warning = FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}

basic_coverage <- ggcoverage(

  data = track_df,

  plot.type = "facet",

  mark.region = mark_region,

  range.position = "in"

)



basic_coverage

```



**Shared/Free Y-axis scale with `facet.y.scale`**:



```{r basic_coverage_3, warning = FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}

basic_coverage <- ggcoverage(

  data = track_df,

  plot.type = "facet",

  mark.region = mark_region,

  range.position = "in",

  facet.y.scale = "fixed"

)



basic_coverage

```



### Add gene annotation



- default behavior is to draw genes (transcripts), exons and UTRs with different line width

- can bec adjusted using `gene.size`, `exon.size` and `utr.size` parameters

- frequency of intermittent arrows (light color) can be adjusted using the `arrow.num` and `arrow.gap` parameters

- genomic features are colored by `strand` by default, which can be changed using the `color.by` parameter



```{r gene_coverage, warning = FALSE, fig.height = 8, fig.width = 12, fig.align = "center"}

basic_coverage +

  geom_gene(gtf.gr = gtf_gr)

```



### Add transcript annotation



**In "loose" style (default style; each transcript occupies one line)**:



```{r transcript_coverage, warning = FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}

basic_coverage +

  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5)

```



**In "tight" style (attempted to place non-overlapping transcripts in one line)**:



```{r transcript_coverage_tight, warning = FALSE, fig.height = 12, fig.width = 12, fig.align = "center"}

basic_coverage +

  geom_transcript(

    gtf.gr = gtf_gr,

    overlap.style = "tight",

    label.vjust = 1.5

  )

```



### Add ideogram



The ideogram is an overview plot about the respective position on a chromosome.

The plotting of the ideogram is implemented by the `ggbio` package.

This package needs to be installed separately (it is only 'Suggested' by `ggcoverage`).



```{r ideogram_coverage_1, warning = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}

library(ggbio)



basic_coverage +

  geom_gene(gtf.gr = gtf_gr) +

  geom_ideogram(genome = "hg19", plot.space = 0)

```



```{r ideogram_coverage_2, warning = FALSE, fig.height = 14, fig.width = 12, fig.align = "center"}

basic_coverage +

  geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5) +

  geom_ideogram(genome = "hg19", plot.space = 0)

```



## DNA-seq data



### CNV



#### Example 1



##### Load the data



The DNA-seq data used here are from [Copy number work flow](https://bioconductor.org/help/course-materials/2014/SeattleOct2014/B02.2.3_CopyNumber.html), we select tumor sample, and get bin counts with `cn.mops::getReadCountsFromBAM` with `WL` 1000.



```{r load_bin_counts}

# prepare metafile

cnv_meta_info <- data.frame(

  SampleName = c("CNV_example"),

  Type = c("tumor"),

  Group = c("tumor")

)



# track file

track_file <- system.file("extdata",

  "DNA-seq", "CNV_example.txt",

  package = "ggcoverage"

)



# load txt file

track_df <- LoadTrackFile(

  track.file = track_file,

  format = "txt",

  region = "chr4:61750000-62,700,000",

  meta.info = cnv_meta_info

)



# check data

head(track_df)

```



##### Basic coverage



```{r basic_coverage_dna, warning = FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}

basic_coverage <- ggcoverage(

  data = track_df,

  color = "grey",

  mark.region = NULL,

  range.position = "out"

)

basic_coverage

```



##### Add GC annotations



Add **GC**, **ideogram** and **gene** annotations.

The plotting of the GC content requires the genome annotation package `BSgenome.Hsapiens.UCSC.hg19`.

This package needs to be installed separately (it is only 'Suggested' by `ggcoverage`).



```{r gc_coverage, warning = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}

# load genome data

library("BSgenome.Hsapiens.UCSC.hg19")



# create plot

basic_coverage +

  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +

  geom_gene(gtf.gr = gtf_gr) +

  geom_ideogram(genome = "hg19")

```



#### Example 2



##### Load the data



The DNA-seq data used here are from [Genome-wide copy number analysis of single cells](https://www.nature.com/articles/nprot.2012.039), and the accession number is [SRR054616](https://trace.ncbi.nlm.nih.gov/Traces/index.html?run=SRR054616).



```{r cnv_load_track_file}

# track file

track_file <-

  system.file("extdata", "DNA-seq", "SRR054616.bw", package = "ggcoverage")



# load track

track_df <- LoadTrackFile(

  track.file = track_file,

  format = "bw",

  region = "4:1-160000000"

)



# add chr prefix

track_df$seqnames <- paste0("chr", track_df$seqnames)



# check data

head(track_df)

```



##### Basic coverage



```{r cnv_basic_coverage_dna}

basic_coverage <- ggcoverage(

  data = track_df,

  color = "grey",

  mark.region = NULL,

  range.position = "out"

)



basic_coverage

```



##### Load CNV file



```{r cnv_load_cnv}

# prepare files

cnv_file <-

  system.file("extdata", "DNA-seq", "SRR054616_copynumber.txt",

    package = "ggcoverage"

  )



# read CNV

cnv_df <- read.table(file = cnv_file, sep = "\t", header = TRUE)



# check data

head(cnv_df)

```



##### Add annotations



Add **GC**, **ideogram** and **CNV** annotations.



```{r cnv_gc_coverage}

# create plot

basic_coverage +

  geom_gc(bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19) +

  geom_cnv(

    cnv.df = cnv_df,

    bin.col = 3,

    cn.col = 4

  ) +

  geom_ideogram(

    genome = "hg19",

    plot.space = 0,

    highlight.centromere = TRUE

  )

```



### Single-nucleotide level



#### Load the data



```{r load_single_nuc}

# prepare sample metadata

sample_meta <- data.frame(

  SampleName = c("tumorA.chr4.selected"),

  Type = c("tumorA"),

  Group = c("tumorA")

)



# load bam file

bam_file <- system.file("extdata",

  "DNA-seq", "tumorA.chr4.selected.bam",

  package = "ggcoverage"

)



track_df <- LoadTrackFile(

  track.file = bam_file,

  meta.info = sample_meta,

  single.nuc = TRUE,

  single.nuc.region = "chr4:62474235-62474295"

)



head(track_df)

```



#### Default color scheme



For base and amino acid annotation, the package comes with the following default color schemes. Color schemes can be changed with `nuc.color` and `aa.color` parameters.



THe default color scheme for base annotation is `Clustal-style`, more popular color schemes are available [here](https://www.biostars.org/p/171056/).



```{r base_color_scheme, warning = FALSE, fig.height = 2, fig.width = 6, fig.align = "center"}

# color scheme

nuc_color <- c(

  "A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d"

)



# create plot

graphics::image(

  seq_along(nuc_color),

  1,

  as.matrix(seq_along(nuc_color)),

  col = nuc_color,

  xlab = "",

  ylab = "",

  xaxt = "n",

  yaxt = "n",

  bty = "n"

)

graphics::text(seq_along(nuc_color), 1, names(nuc_color))

graphics::mtext(

  text = "Base",

  adj = 1,

  las = 1,

  side = 2

)

```



Default color scheme for amino acid annotation is from [Residual colours: a proposal for aminochromography](https://pubmed.ncbi.nlm.nih.gov/9342138/):



```{r aa_color_scheme, warning = FALSE, fig.height = 9, fig.width = 10, fig.align = "center"}

aa_color <- c(

  "D" = "#FF0000", "S" = "#FF2400", "T" = "#E34234", "G" = "#FF8000",

  "P" = "#F28500", "C" = "#FFFF00", "A" = "#FDFF00", "V" = "#E3FF00",

  "I" = "#C0FF00", "L" = "#89318C", "M" = "#00FF00", "F" = "#50C878",

  "Y" = "#30D5C8", "W" = "#00FFFF", "H" = "#0F2CB3", "R" = "#0000FF",

  "K" = "#4b0082", "N" = "#800080", "Q" = "#FF00FF", "E" = "#8F00FF",

  "*" = "#FFC0CB", " " = "#FFFFFF", " " = "#FFFFFF", " " = "#FFFFFF",

  " " = "#FFFFFF"

)



graphics::image(

  1:5,

  1:5,

  matrix(seq_along(aa_color), nrow = 5),

  col = rev(aa_color),

  xlab = "",

  ylab = "",

  xaxt = "n",

  yaxt = "n",

  bty = "n"

)



graphics::text(expand.grid(1:5, 1:5), names(rev(aa_color)))

graphics::mtext(

  text = "Amino acids",

  adj = 1,

  las = 1,

  side = 2

)

```



#### Add base and amino acid annotation



**Use twill to mark position with SNV**:



```{r, echo = FALSE}

# wait some time to avoid 'Too Many Requests' error

Sys.sleep(60)

```



```{r base_aa_coverage, warning = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}

# create plot with twill mark

ggcoverage(

  data = track_df,

  color = "grey",

  range.position = "out",

  single.nuc = TRUE,

  rect.color = "white"

) +

  geom_base(

    bam.file = bam_file,

    bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,

    mark.type = "twill"

  ) +

  geom_ideogram(genome = "hg19", plot.space = 0)

```



**Use star to mark position with SNV**:



```{r, echo = FALSE}

# wait some time to avoid 'Too Many Requests' error

Sys.sleep(60)

```



```{r base_aa_coverage_star, warning = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}

# create plot with star mark

ggcoverage(

  data = track_df,

  color = "grey",

  range.position = "out",

  single.nuc = TRUE,

  rect.color = "white"

) +

  geom_base(

    bam.file = bam_file,

    bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,

    mark.type = "star"

  ) +

  geom_ideogram(genome = "hg19", plot.space = 0)

```



**Highlight position with SNV**:



```{r, echo = FALSE}

# wait some time to avoid 'Too Many Requests' error

Sys.sleep(60)

```



```{r base_aa_coverage_highlight, warning = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}

# highlight one base

ggcoverage(

  data = track_df,

  color = "grey",

  range.position = "out",

  single.nuc = TRUE,

  rect.color = "white"

) +

  geom_base(

    bam.file = bam_file,

    bs.fa.seq = BSgenome.Hsapiens.UCSC.hg19,

    mark.type = "highlight"

  ) +

  geom_ideogram(genome = "hg19", plot.space = 0)

```



## ChIP-seq data



The ChIP-seq data used here is from [DiffBind](https://bioconductor.org/packages/release/bioc/html/DiffBind.html). Four samples are selected as examples: `Chr18_MCF7_input`, `Chr18_MCF7_ER_1`, `Chr18_MCF7_ER_3`, `Chr18_MCF7_ER_2`, and all bam files were converted to bigwig files with [deeptools](https://deeptools.readthedocs.io/en/develop/).



Create metadata:



```{r load_metadata_chip}

# load metadata

sample_meta <- data.frame(

  SampleName = c(

    "Chr18_MCF7_ER_1",

    "Chr18_MCF7_ER_2",

    "Chr18_MCF7_ER_3",

    "Chr18_MCF7_input"

  ),

  Type = c("MCF7_ER_1", "MCF7_ER_2", "MCF7_ER_3", "MCF7_input"),

  Group = c("IP", "IP", "IP", "Input")

)



sample_meta

```



Load track files:



```{r load_track_chip}

# track folder

track_folder <- system.file("extdata", "ChIP-seq", package = "ggcoverage")



# load bigwig file

track_df <- LoadTrackFile(

  track.folder = track_folder,

  format = "bw",

  region = "chr18:76822285-76900000",

  meta.info = sample_meta

)



# check data

head(track_df)

```



Prepare mark region:



```{r prepare_mark_chip}

# create mark region

mark_region <- data.frame(

  start = c(76822533),

  end = c(76823743),

  label = c("Promoter")

)



# check data

mark_region

```



### Basic coverage



```{r basic_coverage_chip, warning = FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}

basic_coverage <- ggcoverage(

  data = track_df,

  mark.region = mark_region,

  show.mark.label = FALSE

)

basic_coverage

```



### Add annotations



Add **gene**, **ideogram** and **peak** annotations. To create peak annotation, we first **get consensus peaks** with [MSPC](https://github.com/Genometric/MSPC).



```{r, echo = FALSE}

# wait some time to avoid 'Too Many Requests' error

Sys.sleep(60)

```



```{r peak_coverage, warning = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}

# get consensus peak file

peak_file <- system.file("extdata",

  "ChIP-seq",

  "consensus.peak",

  package = "ggcoverage"

)



basic_coverage +

  geom_gene(gtf.gr = gtf_gr) +

  geom_peak(bed.file = peak_file) +

  geom_ideogram(genome = "hg19", plot.space = 0)

```



## Hi-C data



The Hi-C method maps chromosome contacts in eukaryotic cells.

For this purpose, DNA and protein complexes are cross-linked and DNA fragments then purified.

As a result, even distant chromatin fragments can be found to interact due to the spatial organization of the DNA and histones in the cell. Hi-C data shows these interactions for example as a contact map.



The Hi-C data is taken from [pyGenomeTracks: reproducible plots for multivariate genomic datasets](https://pubmed.ncbi.nlm.nih.gov/32745185/).



The Hi-C matrix visualization is implemented by [`HiCBricks`](https://github.com/koustav-pal/HiCBricks).

This package needs to be installed separately (it is only 'Suggested' by `ggcoverage`).



### Load track data



```{r hic_track}

# prepare track dataframe

track_file <-

  system.file("extdata", "HiC", "H3K36me3.bw", package = "ggcoverage")



track_df <- LoadTrackFile(

  track.file = track_file,

  format = "bw",

  region = "chr2L:8050000-8300000",

  extend = 0

)



track_df$score <- ifelse(track_df$score < 0, 0, track_df$score)



# check the data

head(track_df)

```



### Load Hi-C data



Matrix:



```{r hic_load_hic_matrix}

## matrix

hic_mat_file <- system.file("extdata",

  "HiC", "HiC_mat.txt",

  package = "ggcoverage"

)

hic_mat <- read.table(file = hic_mat_file, sep = "\t")

hic_mat <- as.matrix(hic_mat)

```



Bin table:



```{r hic_load_hic_bin}

## bin

hic_bin_file <-

  system.file("extdata", "HiC", "HiC_bin.txt", package = "ggcoverage")

hic_bin <- read.table(file = hic_bin_file, sep = "\t")

colnames(hic_bin) <- c("chr", "start", "end")

hic_bin_gr <- GenomicRanges::makeGRangesFromDataFrame(df = hic_bin)



## transfrom func

failsafe_log10 <- function(x) {

  x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0

  return(log10(x + 1))

}

```



Data transfromation method:



### Load link



```{r hic_load_link}

# prepare arcs

link_file <-

  system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")

```



### Basic coverage



```{r basic_coverage_hic, warning = FALSE, fig.height = 6, fig.width = 12, fig.align = "center"}

basic_coverage <-

  ggcoverage(

    data = track_df,

    color = "grey",

    mark.region = NULL,

    range.position = "out"

  )



basic_coverage

```



### Add annotations



Add **link**, **contact map**annotations:



```{r hic_coverage, warning = FALSE, fig.height = 10, fig.width = 12, fig.align = "center"}

library(HiCBricks)



basic_coverage +

  geom_tad(

    matrix = hic_mat,

    granges = hic_bin_gr,

    value.cut = 0.99,

    color.palette = "viridis",

    transform.fun = failsafe_log10,

    top = FALSE,

    show.rect = TRUE

  ) +

  geom_link(

    link.file = link_file,

    file.type = "bedpe",

    show.rect = TRUE

  )

```



## Mass spectrometry protein coverage



[Mass spectrometry](https://en.wikipedia.org/wiki/Protein_mass_spectrometry) (MS) is an important method for the accurate mass determination and characterization of proteins, and a variety of methods and instruments have been developed for its many uses. With `ggcoverage`, we can easily inspect the peptide coverage of a protein in order to learn about the quality of the data.



### Load coverage



The exported coverage from [Proteome Discoverer](https://doi.org/10.3390/proteomes9010015):



```{r ms_coverage_data}

library(openxlsx)

# prepare coverage dataframe

coverage_file <-

  system.file("extdata",

    "Proteomics", "MS_BSA_coverage.xlsx",

    package = "ggcoverage"

  )

coverage_df <- openxlsx::read.xlsx(coverage_file, sheet = "Sheet1")

# check the data

head(coverage_df)

```



The input protein fasta:



```{r ms_coverage_fasta}

fasta_file <-

  system.file("extdata",

    "Proteomics", "MS_BSA_coverage.fasta",

    package = "ggcoverage"

  )



# prepare track dataframe

protein_set <- Biostrings::readAAStringSet(fasta_file)



# check the data

protein_set

```



### Protein coverage



```{r basic_coverage_protein, warning = FALSE, fig.height = 6, fig.width = 10, fig.align = "center"}

protein_coverage <- ggprotein(

  coverage.df = coverage_df,

  fasta.file = fasta_file,

  protein.id = "sp|P02769|ALBU_BOVIN",

  range.position = "out"

)



protein_coverage

```



### Add annotation



We can obtain features of the protein from [UniProt](https://www.uniprot.org/). For example, the above protein coverage plot shows that there is empty region in 1-24, and this empty region in [UniProt](https://www.uniprot.org/uniprotkb/P02769/entry) is annotated as Signal peptide and Propeptide peptide. When the protein is mature and released extracellular, these peptides will be cleaved. This is the reason why there is empty region in 1-24.



```{r basic_coverage_protein_feature, warning = FALSE, fig.height = 6, fig.width = 10, fig.align = "center"}

# protein feature obtained from UniProt

protein_feature_df <- data.frame(

  ProteinID = "sp|P02769|ALBU_BOVIN",

  start = c(1, 19, 25),

  end = c(18, 24, 607),

  Type = c("Signal", "Propeptide", "Chain")

)



# add annotation

protein_coverage +

  geom_feature(

    feature.df = protein_feature_df,

    feature.color = c("#4d81be", "#173b5e", "#6a521d")

  )

```



## Code of Conduct



Please note that the `ggcoverage` project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.