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:
ggcoveragecan 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:
ggcoveragesupports 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 is an R package distributed as part of the CRAN
repository. To install the package, start
R and enter one of the following commands:
# 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 (updated more regularly).
Once ggcoverage is installed, it can be loaded like every other
package:
library("ggcoverage")ggcoverage provides two
vignettes:
- detailed manual: step-by-step usage
- customize the plot: customize the plot and add additional layers
The RNA-seq data used here is from Transcription profiling by high
throughput sequencing of HNRNPC knockdown and control HeLa
cells.
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.
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 WTLoad 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 width strand score Type Group
#> 1 chr14 21675306 21675950 645 * 0 KO_rep1 KO
#> 2 chr14 21675951 21676000 50 * 1 KO_rep1 KO
#> 3 chr14 21676001 21676100 100 * 2 KO_rep1 KO
#> 4 chr14 21676101 21676150 50 * 1 KO_rep1 KO
#> 5 chr14 21676151 21677100 950 * 0 KO_rep1 KO
#> 6 chr14 21677101 21677200 100 * 2 KO_rep1 KOPrepare 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 M3To 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.
gtf_file <-
system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
gtf_gr <- rtracklayer::import.gff(con = gtf_file, format = "gtf")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.
Create line plot for every sample (facet.key = "Type") and color
by every sample (group.key = "Type"):
basic_coverage <- ggcoverage(
data = track_df,
plot.type = "joint",
facet.key = "Type",
group.key = "Type",
mark.region = mark_region,
range.position = "out"
)
basic_coverageCreate group average line plot (sample is indicated by
facet.key = "Type", group is indicated by group.key = "Group"):
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_coveragebasic_coverage <- ggcoverage(
data = track_df,
plot.type = "facet",
mark.region = mark_region,
range.position = "out"
)
basic_coverageChange the Y-axis scale label in/out of plot region with
range.position:
basic_coverage <- ggcoverage(
data = track_df,
plot.type = "facet",
mark.region = mark_region,
range.position = "in"
)
basic_coverageShared/Free Y-axis scale with facet.y.scale:
basic_coverage <- ggcoverage(
data = track_df,
plot.type = "facet",
mark.region = mark_region,
range.position = "in",
facet.y.scale = "fixed"
)
basic_coverage- default behavior is to draw genes (transcripts), exons and UTRs with different line width
- can bec adjusted using
gene.size,exon.sizeandutr.sizeparameters - frequency of intermittent arrows (light color) can be adjusted using
the
arrow.numandarrow.gapparameters - genomic features are colored by
strandby default, which can be changed using thecolor.byparameter
basic_coverage +
geom_gene(gtf.gr = gtf_gr)In “loose” style (default style; each transcript occupies one line):
basic_coverage +
geom_transcript(gtf.gr = gtf_gr, label.vjust = 1.5)In “tight” style (attempted to place non-overlapping transcripts in one line):
basic_coverage +
geom_transcript(
gtf.gr = gtf_gr,
overlap.style = "tight",
label.vjust = 1.5
)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).
library(ggbio)
#> Loading required package: BiocGenerics
#>
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:stats':
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#>
#> anyDuplicated, aperm, append, as.data.frame, basename, cbind,
#> colnames, 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, table,
#> tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: ggplot2
#> Registered S3 method overwritten by 'GGally':
#> method from
#> +.gg ggplot2
#> Need specific help about ggbio? try mailing
#> the maintainer or visit https://lawremi.github.io/ggbio/
#>
#> Attaching package: 'ggbio'
#> The following objects are masked from 'package:ggplot2':
#>
#> geom_bar, geom_rect, geom_segment, ggsave, stat_bin, stat_identity,
#> xlim
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.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 tumorbasic_coverage <- ggcoverage(
data = track_df,
color = "grey",
mark.region = NULL,
range.position = "out"
)
basic_coverageAdd 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).
# load genome data
library("BSgenome.Hsapiens.UCSC.hg19")
#> Loading required package: BSgenome
#> Loading required package: S4Vectors
#> Loading required package: stats4
#>
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:utils':
#>
#> findMatches
#> The following objects are masked from 'package:base':
#>
#> expand.grid, I, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: GenomicRanges
#> Loading required package: Biostrings
#> Loading required package: XVector
#>
#> Attaching package: 'Biostrings'
#> The following object is masked from 'package:base':
#>
#> strsplit
#> Loading required package: BiocIO
#> Loading required package: rtracklayer
#>
#> Attaching package: 'rtracklayer'
#> The following object is masked from 'package:BiocIO':
#>
#> FileForFormat
# 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.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"
)
#> No metadata provided, returning coverage as is.
# add chr prefix
track_df$seqnames <- paste0("chr", track_df$seqnames)
# check data
head(track_df)
#> seqnames start end width strand score Type Group
#> 1 chr4 1 50000 50000 * 197 SRR054616.bw SRR054616.bw
#> 2 chr4 50001 100000 50000 * 598 SRR054616.bw SRR054616.bw
#> 3 chr4 100001 150000 50000 * 287 SRR054616.bw SRR054616.bw
#> 4 chr4 150001 200000 50000 * 179 SRR054616.bw SRR054616.bw
#> 5 chr4 200001 250000 50000 * 282 SRR054616.bw SRR054616.bw
#> 6 chr4 250001 300000 50000 * 212 SRR054616.bw SRR054616.bwbasic_coverage <- ggcoverage(
data = track_df,
color = "grey",
mark.region = NULL,
range.position = "out"
)
basic_coverage# 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 5Add GC, ideogram and CNV annotations.
# 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.# 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"
)
#> No 'region' specified; extracting coverage for an example range
#> (<=100,000 bases, first annotated sequence)
#> Coverage extracted from sequence/chromosome: chr10
head(track_df)
#> seqnames start end width strand score Type Group
#> 1 chr4 62474235 62474236 1 * 5 tumorA tumorA
#> 2 chr4 62474236 62474237 1 * 5 tumorA tumorA
#> 3 chr4 62474237 62474238 1 * 5 tumorA tumorA
#> 4 chr4 62474238 62474239 1 * 6 tumorA tumorA
#> 5 chr4 62474239 62474240 1 * 6 tumorA tumorA
#> 6 chr4 62474240 62474241 1 * 6 tumorA tumorAFor 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.
# 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:
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
)Use twill to mark position with SNV:
# 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)
#> 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 = 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)
#> 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 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)
#> Loading ideogram...
#> Loading ranges...
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.The ChIP-seq data used here is from
DiffBind.
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.
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 InputLoad 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 width strand score Type Group
#> 1 chr18 76820285 76820400 116 * 219.658005 MCF7_ER_1 IP
#> 2 chr18 76820401 76820700 300 * 0.000000 MCF7_ER_1 IP
#> 3 chr18 76820701 76821000 300 * 439.316010 MCF7_ER_1 IP
#> 4 chr18 76821001 76821300 300 * 219.658005 MCF7_ER_1 IP
#> 5 chr18 76821301 76821600 300 * 0.000000 MCF7_ER_1 IP
#> 6 chr18 76821601 76821900 300 * 219.658005 MCF7_ER_1 IPPrepare 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 Promoterbasic_coverage <- ggcoverage(
data = track_df,
mark.region = mark_region,
show.mark.label = FALSE
)
basic_coverageAdd 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.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.
The Hi-C matrix visualization is implemented by
HiCBricks. This package
needs to be installed separately (it is only ‘Suggested’ by
ggcoverage).
# 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
)
#> No metadata provided, returning coverage as is.
track_df$score <- ifelse(track_df$score < 0, 0, track_df$score)
# check the data
head(track_df)
#> seqnames start end width strand score Type Group
#> 1 chr2L 8050000 8050009 10 * 1.66490245 H3K36me3.bw H3K36me3.bw
#> 2 chr2L 8050015 8050049 35 * 1.59976900 H3K36me3.bw H3K36me3.bw
#> 3 chr2L 8050057 8050091 35 * 1.60730922 H3K36me3.bw H3K36me3.bw
#> 4 chr2L 8050097 8050131 35 * 1.65555012 H3K36me3.bw H3K36me3.bw
#> 5 chr2L 8050137 8050171 35 * 1.71025538 H3K36me3.bw H3K36me3.bw
#> 6 chr2L 8050176 8050210 35 * 1.75198197 H3K36me3.bw H3K36me3.bwMatrix:
## 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:
# prepare arcs
link_file <-
system.file("extdata", "HiC", "HiC_link.bedpe", package = "ggcoverage")basic_coverage <-
ggcoverage(
data = track_df,
color = "grey",
mark.region = NULL,
range.position = "out"
)
basic_coverageAdd link, contact mapannotations:
library(HiCBricks)
#> Loading required package: curl
#> Using libcurl 7.81.0 with OpenSSL/3.0.2
#> Loading required package: rhdf5
#> Loading required package: R6
#> Loading required package: grid
#>
#> Attaching package: 'grid'
#> The following object is masked from 'package:Biostrings':
#>
#> pattern
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
(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.
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, sheet = "Sheet1")
# 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 75.40The input protein 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
#> AAStringSet object of length 2:
#> width seq names
#> [1] 607 MKWVTFISLLLLFSSAYSRGVFR...DDKEACFAVEGPKLVVSTQTALA sp|P02769|ALBU_BOVIN
#> [2] 583 DTHKSEIAHRFKDLGEEHFKGLV...DDKEACFAVEGPKLVVSTQTALA decoyprotein_coverage <- ggprotein(
coverage.df = coverage_df,
fasta.file = fasta_file,
protein.id = "sp|P02769|ALBU_BOVIN",
range.position = "out"
)
protein_coverageWe 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")
)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.