ggcoverage - Visualize and annotate omics coverage with ggplot2

Introduction

The goal of ggcoverage is simplify the process of visualizing omics coverage. 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

ggcoverage utilizes ggplot2 plotting system, so its usage is ggplot2-style!

Installation

ggcoverage is an R package distributed as part of the CRAN. To install the package, start R and enter:

# install via CRAN
install.packages("ggcoverage")

# install via Github
# install.package("remotes")   #In case you have not installed it.
remotes::install_github("showteeth/ggcoverage")

In general, it is recommended to install from Github repository (update more timely).

Once ggcoverage is installed, it can be loaded by the following command.

library("rtracklayer")
library("ggcoverage")
library("ggpattern")

Manual

ggcoverage provides two vignettes:

• detailed manual: step-by-step usage
• customize the plot: customize the plot and add additional layer

RNA-seq data

Load the data

The RNA-seq data used here are from Transcription profiling by high throughput sequencing of HNRNPC knockdown and control HeLa cells, we select four sample to use as example: ERR127307_chr14, ERR127306_chr14, ERR127303_chr14, ERR127302_chr14, and all bam files are converted to bigwig file with deeptools.

Load metadata:

# load metadata
meta.file <- system.file("extdata", "RNA-seq", "meta_info.csv", package = "ggcoverage")
sample.meta = read.csv(meta.file)
sample.meta
#>        SampleName    Type Group
#> 1 ERR127302_chr14 KO_rep1    KO
#> 2 ERR127303_chr14 KO_rep2    KO
#> 3 ERR127306_chr14 WT_rep1    WT
#> 4 ERR127307_chr14 WT_rep2    WT

Load track files:

# 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)
#>   seqnames    start      end score    Type Group
#> 1    chr14 21675306 21675950     0 KO_rep1    KO
#> 2    chr14 21675951 21676000     1 KO_rep1    KO
#> 3    chr14 21676001 21676100     2 KO_rep1    KO
#> 4    chr14 21676101 21676150     1 KO_rep1    KO
#> 5    chr14 21676151 21677100     0 KO_rep1    KO
#> 6    chr14 21677101 21677200     2 KO_rep1    KO

Prepare mark region:

# 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
#>      start      end label
#> 1 21678900 21679900    M1
#> 2 21732001 21732400    M2
#> 3 21737590 21737650    M3

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 transcript_name attribute in column 9.

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"):

basic.coverage = ggcoverage(data = track.df, color = "auto", 
                            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"):

basic.coverage = ggcoverage(data = track.df, color = "auto", 
                            plot.type = "joint", facet.key = "Type", group.key = "Group", 
                            joint.avg = TRUE,
                            mark.region = mark.region, range.position = "out")
basic.coverage

facet view

basic.coverage = ggcoverage(data = track.df, color = "auto", 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:

basic.coverage = ggcoverage(data = track.df, color = "auto", plot.type = "facet",
                            mark.region = mark.region, range.position = "in")
basic.coverage

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

basic.coverage = ggcoverage(data = track.df, color = "auto", plot.type = "facet",
                            mark.region = mark.region, range.position = "in", 
                            facet.y.scale = "fixed")
basic.coverage

Add gene annotation

basic.coverage + 
  geom_gene(gtf.gr=gtf.gr)

Add transcript annotation

In “loose” stype (default style; each transcript occupies one line):

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

In “tight” style (place non-overlap transcripts in one line):

basic.coverage +
  geom_transcript(gtf.gr=gtf.gr, overlap.style = "tight", label.vjust = 1.5)

Add ideogram

basic.coverage +
  geom_gene(gtf.gr=gtf.gr) +
  geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

basic.coverage +
  geom_transcript(gtf.gr=gtf.gr,label.vjust = 1.5) +
  geom_ideogram(genome = "hg19",plot.space = 0)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

DNA-seq data

CNV

Example 1

Load the data

The DNA-seq data used here are from Copy number work flow, we select tumor sample, and get bin counts with cn.mops::getReadCountsFromBAM with WL 1000.

# 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)
#>   seqnames    start      end score  Type Group
#> 1     chr4 61748000 61748000    25 tumor tumor
#> 2     chr4 61748001 61749000    24 tumor tumor
#> 3     chr4 61749001 61750000    17 tumor tumor
#> 4     chr4 61750001 61751000    23 tumor tumor
#> 5     chr4 61751001 61752000    14 tumor tumor
#> 6     chr4 61752001 61753000    22 tumor tumor

Basic coverage

basic.coverage = ggcoverage(data = track.df,color = "grey", mark.region = NULL,
                            range.position = "out")
basic.coverage

Add GC annotations

Add GC, ideogram and gene annotaions.

# load genome data
library("BSgenome.Hsapiens.UCSC.hg19")
#> Loading required package: BSgenome
#> Loading required package: Biostrings
#> Loading required package: XVector
#> 
#> Attaching package: 'Biostrings'
#> The following object is masked from 'package:base':
#> 
#>     strsplit
# create plot
basic.coverage +
  geom_gc(bs.fa.seq=BSgenome.Hsapiens.UCSC.hg19) +
  geom_gene(gtf.gr=gtf.gr) +
  geom_ideogram(genome = "hg19")
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

Example 2

Load the data

The DNA-seq data used here are from Genome-wide copy number analysis of single cells, and the accession number is SRR054616.

# 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")
#> Sample without metadata!
# add chr prefix
track.df$seqnames = paste0("chr", track.df$seqnames)
# check data
head(track.df)
#>   seqnames  start    end score         Type        Group
#> 1     chr4      1  50000   197 SRR054616.bw SRR054616.bw
#> 2     chr4  50001 100000   598 SRR054616.bw SRR054616.bw
#> 3     chr4 100001 150000   287 SRR054616.bw SRR054616.bw
#> 4     chr4 150001 200000   179 SRR054616.bw SRR054616.bw
#> 5     chr4 200001 250000   282 SRR054616.bw SRR054616.bw
#> 6     chr4 250001 300000   212 SRR054616.bw SRR054616.bw

Basic coverage

basic.coverage = ggcoverage(data = track.df, color = "grey",
                            mark.region = NULL, range.position = "out")
basic.coverage

Load CNV file

# 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)
#>   chrom chrompos  cn.ratio copy.number
#> 1  chr4        1 11.518554           5
#> 2  chr4    90501  5.648878           5
#> 3  chr4   145220  4.031609           5
#> 4  chr4   209519  5.005852           5
#> 5  chr4   268944  4.874096           5
#> 6  chr4   330272  4.605368           5

Add annotations

Add GC, ideogram and CNV annotations.

# load genome data
library("BSgenome.Hsapiens.UCSC.hg19")
# 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)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

Single-nucleotide level

Load the data

# 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)
#>   seqnames    start      end score   Type  Group
#> 1     chr4 62474235 62474236     5 tumorA tumorA
#> 2     chr4 62474236 62474237     5 tumorA tumorA
#> 3     chr4 62474237 62474238     5 tumorA tumorA
#> 4     chr4 62474238 62474239     6 tumorA tumorA
#> 5     chr4 62474239 62474240     6 tumorA tumorA
#> 6     chr4 62474240 62474241     6 tumorA tumorA

Default color scheme

For base and amino acid annotation, we have following default color schemes, you can change with nuc.color and aa.color parameters.

Default color scheme for base annotation is Clustal-style, more popular color schemes is available here.

# color scheme
nuc.color = c("A" = "#ff2b08", "C" = "#009aff", "G" = "#ffb507", "T" = "#00bc0d")
opar <- graphics::par() 
# create plot
graphics::par(mar = c(1, 5, 1, 1))
graphics::image(
  1:length(nuc.color), 1, as.matrix(1:length(nuc.color)),
  col = nuc.color,
  xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n"
)
graphics::text(1:length(nuc.color), 1, names(nuc.color))
graphics::mtext(
  text = "Base", adj = 1, las = 1,
  side = 2
)

# reset par default
graphics::par(opar)

Default color scheme for amino acid annotation is from Residual colours: a proposal for aminochromography:

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::par(mar = c(1, 5, 1, 1))
graphics::image(
  1:5, 1:5, matrix(1:length(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
)

# reset par default
graphics::par(opar)

Add base and amino acid annotation

Use twill to mark position with SNV:

library(ggpattern)
# create plot with twill mark
ggcoverage(data = track.df, color = "grey", range.position = "out", 
           single.nuc=T, 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)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

Use star to mark position with SNV:

# create plot with star mark
ggcoverage(data = track.df, color = "grey", range.position = "out", 
           single.nuc=T, 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)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

Highlight position with SNV:

# highlight
ggcoverage(data = track.df, color = "grey", range.position = "out", 
           single.nuc=T, 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)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

ChIP-seq data

The ChIP-seq data used here are from DiffBind, I select four sample to use as example: Chr18_MCF7_input, Chr18_MCF7_ER_1, Chr18_MCF7_ER_3, Chr18_MCF7_ER_2, and all bam files are converted to bigwig file with deeptools.

Create metadata:

# 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
#>         SampleName       Type Group
#> 1  Chr18_MCF7_ER_1  MCF7_ER_1    IP
#> 2  Chr18_MCF7_ER_2  MCF7_ER_2    IP
#> 3  Chr18_MCF7_ER_3  MCF7_ER_3    IP
#> 4 Chr18_MCF7_input MCF7_input Input

Load track files:

# 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)
#>   seqnames    start      end      score      Type Group
#> 1    chr18 76820285 76820400 219.658005 MCF7_ER_1    IP
#> 2    chr18 76820401 76820700   0.000000 MCF7_ER_1    IP
#> 3    chr18 76820701 76821000 439.316010 MCF7_ER_1    IP
#> 4    chr18 76821001 76821300 219.658005 MCF7_ER_1    IP
#> 5    chr18 76821301 76821600   0.000000 MCF7_ER_1    IP
#> 6    chr18 76821601 76821900 219.658005 MCF7_ER_1    IP

Prepare mark region:

# create mark region
mark.region=data.frame(start=c(76822533),
                       end=c(76823743),
                       label=c("Promoter"))
# check data
mark.region
#>      start      end    label
#> 1 76822533 76823743 Promoter

Basic coverage

basic.coverage = ggcoverage(data = track.df, color = "auto", 
                            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.

# 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)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

Hi-C data

The Hi-C data are from pyGenomeTracks: reproducible plots for multivariate genomic datasets.

The Hi-C matrix visualization is implemented by HiCBricks.

Load track data

library(ggcoverage)
library(GenomicRanges)
# 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)
#> Sample without metadata!
track.df$score = ifelse(track.df$score <0, 0, track.df$score)
# check the data
head(track.df)
#>   seqnames   start     end      score        Type       Group
#> 1    chr2L 8050000 8050009 1.66490245 H3K36me3.bw H3K36me3.bw
#> 2    chr2L 8050015 8050049 1.59976900 H3K36me3.bw H3K36me3.bw
#> 3    chr2L 8050057 8050091 1.60730922 H3K36me3.bw H3K36me3.bw
#> 4    chr2L 8050097 8050131 1.65555012 H3K36me3.bw H3K36me3.bw
#> 5    chr2L 8050137 8050171 1.71025538 H3K36me3.bw H3K36me3.bw
#> 6    chr2L 8050176 8050210 1.75198197 H3K36me3.bw H3K36me3.bw

Load Hi-C data

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:

## 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:

## transfrom func
FailSafe_log10 <- function(x){
  x[is.na(x) | is.nan(x) | is.infinite(x)] <- 0
  return(log10(x+1))
}

Load link

# prepare arcs
link.file = system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")

Basic coverage

basic.coverage = ggcoverage(data = track.df, color = "grey",
                            mark.region = NULL, range.position = "out")
basic.coverage

Add annotations

Add link, contact mapannotations:

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)
#> Read 534 lines after Skipping 0 lines
#> Inserting Data at location: 1
#> Data length: 534
#> Loaded 2315864 bytes of data...
#> Read 534 records...
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

Mass spectrometry protein coverage

Mass spectrometry (MS) is an important method for the accurate mass determination and characterization of proteins, and a variety of methods and instrumentations have been developed for its many uses. After MS, we can check the coverage of protein to check the quality of the data and find the reason why the segment did not appear and improve the experiment.

Load coverage

The exported coverage from Proteome Discoverer:

library(openxlsx)
# prepare coverage dataframe
coverage.file <- system.file("extdata", "Proteomics", "MS_BSA_coverage.xlsx", package = "ggcoverage")
coverage.df <- openxlsx::read.xlsx(coverage.file)
# check the data
head(coverage.df)
#>   Confidence                            Annotated.Sequence
#> 1       High  [K].ATEEQLKTVMENFVAFVDKCCAADDKEACFAVEGPK.[L]
#> 2       High  [K].ATEEQLKTVMENFVAFVDKCCAADDKEACFAVEGPK.[L]
#> 3       High         [K].TVMENFVAFVDKCCAADDKEACFAVEGPK.[L]
#> 4       High      [K].HLVDEPQNLIKQNCDQFEKLGEYGFQNALIVR.[Y]
#> 5       High [R].RHPYFYAPELLYYANKYNGVFQECCQAEDKGACLLPK.[I]
#> 6       High             [K].AFDEKLFTFHADICTLPDTEKQIKK.[Q]
#>                                          Modifications Contaminant
#> 1                    3xCarbamidomethyl [C20; C21; C29]        TRUE
#> 2 3xCarbamidomethyl [C20; C21; C29]; 1xOxidation [M10]        TRUE
#> 3  3xCarbamidomethyl [C13; C14; C22]; 1xOxidation [M3]        TRUE
#> 4                              1xCarbamidomethyl [C14]        TRUE
#> 5                    3xCarbamidomethyl [C24; C25; C33]        TRUE
#> 6                              1xCarbamidomethyl [C14]        TRUE
#>   #.Protein.Groups #.Proteins #.PSMs Master.Protein.Accessions
#> 1                1          2     15                ALBU_BOVIN
#> 2                1          2     26                ALBU_BOVIN
#> 3                1          2     14                ALBU_BOVIN
#> 4                1          2     41                ALBU_BOVIN
#> 5                1          2     37                ALBU_BOVIN
#> 6                1          2     40                ALBU_BOVIN
#>   Positions.in.Master.Proteins Modifications.in.Master.Proteins
#> 1         ALBU_BOVIN [562-597]                               NA
#> 2         ALBU_BOVIN [562-597]                               NA
#> 3         ALBU_BOVIN [569-597]                               NA
#> 4         ALBU_BOVIN [402-433]                               NA
#> 5         ALBU_BOVIN [168-204]                               NA
#> 6         ALBU_BOVIN [524-548]                               NA
#>   #.Missed.Cleavages Theo..MH+.[Da] Abundance:.F3:.Sample Quan.Info
#> 1                  3     4107.88065            18692597.5      <NA>
#> 2                  3     4123.87556            87767162.0      <NA>
#> 3                  2     3324.46798            19803927.2      <NA>
#> 4                  2     3815.91737           204933705.0      <NA>
#> 5                  3     4513.12024            57012156.5      <NA>
#> 6                  3     2995.52337           183934556.7      <NA>
#>   Found.in.Sample:.[S3].F3:.Sample Confidence.(by.Search.Engine):.Sequest.HT
#> 1                             High                                      High
#> 2                             High                                      High
#> 3                             High                                      High
#> 4                             High                                      High
#> 5                             High                                      High
#> 6                             High                                      High
#>   XCorr.(by.Search.Engine):.Sequest.HT Top.Apex.RT.[min]
#> 1                                11.96             97.50
#> 2                                10.91             90.09
#> 3                                 9.89             84.90
#> 4                                 9.75             91.84
#> 5                                 8.94             93.30
#> 6                                 8.90             75.40

The input protein fasta:

library(Biostrings)
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
#> AAStringSet object of length 2:
#>     width seq                                               names               
#> [1]   607 MKWVTFISLLLLFSSAYSRGVFR...DDKEACFAVEGPKLVVSTQTALA sp|P02769|ALBU_BOVIN
#> [2]   583 DTHKSEIAHRFKDLGEEHFKGLV...DDKEACFAVEGPKLVVSTQTALA decoy

Protein coverage

protein.coverage = ggprotein(coverage.file = coverage.file, 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. For example, the above protein coverage plot shows that there is empty region in 1-24, and this empty region in UniProt 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.

# 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. By contributing to this project, you agree to abide by its terms.