ggcoverage - Visualize and annotate genomic coverage with ggplot2

Introduction

The goal of ggcoverage is simplify the process of visualizing genomic coverage. It contains three main parts:

• Load the data: ggcoverage can load bam, bigwig (.bw), bedgraph file from various NGS data, including RNA-seq, ChIP-seq, ATAC-seq, et al.
• Create genomic coverage plot
• Add annotaions: ggcoverage supports four different annotaions:
  • gene annotaion: Visualize genomic coverage across whole gene
  • transcription annotion: Visualize genomic coverage across different transcripts
  • ideogram annotation: Visualize the region showing on whole chromosome
  • peak annotation: Visualize genomic coverage and peak identified.

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

Installation

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

# install via Bioconductor
if (!requireNamespace("BiocManager", quietly=TRUE))
  install.packages("BiocManager")
BiocManager::install("ggcoverage")

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

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

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

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

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",
                         meta.info = sample.meta)
# check data
head(track.df)
#>   seqnames    start      end score    Type Group
#> 1    chr14 21572751 21630650     0 KO_rep1    KO
#> 2    chr14 21630651 21630700     1 KO_rep1    KO
#> 3    chr14 21630701 21630800     4 KO_rep1    KO
#> 4    chr14 21630801 21657350     0 KO_rep1    KO
#> 5    chr14 21657351 21657450     1 KO_rep1    KO
#> 6    chr14 21657451 21663550     0 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 file:

gtf.file = system.file("extdata", "used_hg19.gtf", package = "ggcoverage")
gtf.gr = rtracklayer::import.gff(con = gtf.file, format = 'gtf')

Basic coverage

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

You can also change Y axis style:

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

Add gene annotation

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

Add transcript annotation

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

Add ideogram

basic.coverage +
  geom_gene(gtf.gr=gtf.gr) +
  geom_ideogram(genome = "hg19",plot.space = 0)
#> [1] "hg19"
#> 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)
#> [1] "hg19"
#> 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",
                         meta.info = sample.meta)
# check data
head(track.df)
#>   seqnames    start      end   score      Type Group
#> 1    chr18 76799701 76800000 439.316 MCF7_ER_1    IP
#> 2    chr18 76800001 76800300 658.974 MCF7_ER_1    IP
#> 3    chr18 76800301 76800600 219.658 MCF7_ER_1    IP
#> 4    chr18 76800601 76800900 658.974 MCF7_ER_1    IP
#> 5    chr18 76800901 76801200   0.000 MCF7_ER_1    IP
#> 6    chr18 76801201 76801500 219.658 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 track

basic.coverage = ggcoverage(data = track.df, color = "auto", region = "chr18:76822285-76900000", 
                            mark.region=mark.region, show.mark.label = FALSE)
basic.coverage

Add annotations

Add gene, ideogram and peak annotaions. To create peak annotaion, we first get consensus peaks with MSPC, you can also use DEbChIP’s GetConsensusPeak (MSPC’s wrapper) to do this.

# 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)
#> [1] "hg19"
#> Loading ideogram...
#> Loading ranges...
#> Scale for 'x' is already present. Adding another scale for 'x', which will
#> replace the existing scale.

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.