karyoploteR

A tutorial and a set of examples on using the R/Bioconductor package karyoploteR to plot whole genomes with arbitrary data on them

Getting Started Citing Tutorial Examples

karyoploteR is an R package to create karyoplots, that is, representations of whole genomes with arbitrary data plotted on them. It is inspired by the R base graphics system and does not depend on other graphics packages. The aim of karyoploteR is to offer the user an easy way to plot data along the genome to get broad genome-wide view to facilitate the identification of genome wide relations and distributions.

 

karyoploteR is based on base R graphics and mimicks its interface. You first create a plot with a call to the plotKaryotype function and then sequentially call a number of plotting functions (kpLines, kpPoints, kpBars…) to add data to the genome plot.

karyoploteR is a plotting tool and only a plotting tool. That means that it is not able to download or retrieve any data. The downside of this is that the user is responsible of getting the data into R. The upside is that it is not tied to any data provider and thus can be used to plot genomic data coming from anywhere. The only exception to this are the ideograms cytobands, that by default are plotted using predownloaded data from UCSC.

karyoploteR is useful in any situation where a general genome-wide view of data is desirable. It can be used to plot somatic copy-number changes (SCNA) in cancer genomes obteined from exome, aCGH or SNP-array data; to plot the global BAM coverage from a WGS experiment; to create manhattan plots from GWAS studies; to create rainfall plots to detect kataegis. Since it is not tied to any data type or source, karyoploteR can be used to plot almost anything on a genome-wide scale.

Getting Started

karyoploteR is part of Bioconductor since version BioC 3.5. The package documentation, including the vignette and user manual is available at the karyoploteR’s Bioconductor landing page at http://bioconductor.org/packages/karyoploteR.

To install the package you’ll need to use Bioconductor’s own package manager, called BiocManager.

To do so, simply start R and enter the following code:

  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  BiocManager::install("karyoploteR")

Usign the development version

To use the development version of karyoploteR you should use the devel version of Bioconductor. The devel version of the package might work with release version of Bioconductor, althought that’s not expected to be always the case. You should be able to install the development version from the github repo using install_github() from the devtools package.

Citing karyoploteR

karyoploteR has been developed by Bernat Gel and Eduard Serra at IGTP Hereditary Cancer Group.

If you use karyoploteR in your research, please cite the Bioinformatics paper describing it:

Bernat Gel & Eduard Serra. (2017). karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics, 31–33. doi:10.1093/bioinformatics/btx346

Tutorial

Important Note: All documentation here refers to the latest released version of Bioconductor. Some functionality may be missing in older version. If a documented function is only available in the devel version of the package, it will be clearly stated that it’s not yet available in release.

The tutorial is a work in progress yet. Feel free to contact us to ask for any clarification or propose a a new section.

Ideograms and other non-data graphical elements

Low-level Plotting Functions

  • Overview of low-level plotting functions

    • Overview of the low-level plotting functions to plot basic graphical primitives (points, lines, arrows, poylgons...)

  • Points

    • Plot points to create scatter plots

  • Lines

    • Plot lines on the genome

  • Text

    • Add text labels on the data part of a karyoplot

  • Polygons

    • Add polygons to a karyoplot

  • Area

    • Plot a line and shade the area below

  • Segments

    • Plot segments on a karyoplot

  • Rectangles

    • Plot rectangles on a karyoplot

  • Arrows

    • Plot arrows on a karyoplot

  • Bars

    • Plot bars on a karyoplot

High-level Plotting Functions

Examples

Image of example Multiple Data Types

Multiple Data Types

A plot with multiple data types plotted together.
Image of example Gene Expression - Drosophila

Gene Expression - Drosophila

Create a plot with differential expression results from the DESeq2 vignette
Image of example Epigenetic data from ENCODE

Epigenetic data from ENCODE

Plot ChIP-seq data from ENCODE BigWig files representing histone modifications and DNA-binding proteins
Image of example Nucleotide Frequency

Nucleotide Frequency

Compute the frequency of each nucleotide along the genome and plot them as a stacked area plot.
Image of example SNP-array raw data

SNP-array raw data

Create a plot with raw data from an Illumina SNP array starting with real SNP-array data as extracted from GenomeStudio
Image of example Rainfall Plots

Rainfall Plots

Create a rainfall plot with the distance between somatic mutations with data from a pancretic tumor WGS
Image of example Plot Genes

Plot Genes

Plot the location of a set of genes on the genome.
Image of example Plasmodium Vivax Genes from GFF

Plasmodium Vivax Genes from GFF

Get the genes and genome of Plasmodium Vivax from a GFF file and plot them
Image of example Gene Density on the Ideogram

Gene Density on the Ideogram

Plot the density of genes over the genome instead of the ideograms.
Image of example Gene Density

Gene Density

Plot the density of genes along the genome.
Image of example Plot CpG Islands

Plot CpG Islands

Get the CpG islands using AnnotationHub and plot them on the genome along with their density