Contents

1 Introduction

gmoviz logo

gmoviz logo

Genetically modified organisms (GMOs) and cell lines are widely used models in many aspects of biological research. As part of characterising these models, DNA sequencing technology and bioinformatic analyses are used systematically to study their genomes. Large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge with regards to representing all findings in an informative and concise manner. Scientific visualisation can be used to facilitate the explanation of complex genomic editing events such as intergration events, deletions, insertions, etc. However, current visualisation tools tend to focus on numerical data, ignoring the need to visualise editing events on a large yet biologically-relevant scale.

gmoviz is an R package designed to extend traditional bioinformatics workflows used for genomic characterisation with powerful visualisation capabilities based on the Circos (Krzywinski et al. 2009) plotting framework, as implemented in circlize (Gu et al. 2014). gmoviz offers the following key features (summarised in the diagram below):

Flowchart summarising the usage and key functions of gmoviz

Flowchart summarising the usage and key functions of gmoviz

1.1 How to read Circos plots

Circos plots have two key components: sectors and tracks. Each sector represents a sequence of interest (such as a chromosome, gene or any other region). Tracks on the other hand are used to display data. For example: In the figure above, red boxes have been drawn around each of the sectors. In the next panel, blue boxes have been drawn around each of the tracks

2 Installation

gmoviz can be installed from bioconductor.org or its GitHub repository

2.1 Bioconductor

To install gmoviz via the BiocManager, type in R console:

if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("gmoviz")

2.2 GitHub

To install the development version of gmoviz from GitHub, type in the R console:

BiocManager::install("malhamdoosh/gmoviz")

2.3 R package dependencies

gmoviz depends on several packages from the CRAN and Bioconductor repositories:

  • circlize provides the lower-level functions used to generate the circular plots. To install it, type in the R console:
BiocManager::install("circlize")
  • GenomicRanges and IRanges are required for the GRanges data structure that is used to store information for plotting. To install them, type in the R console:
BiocManager::install(c("GenomicRanges", "IRanges"))
  • gridBase faciliates the use of the circular plots (which are generated using base graphics) with the grid graphics system. To install it, type in the R console:
BiocManager::install("gridBase")
  • ComplexHeatmap is used to generate legends. To install it, type in the R console:
BiocManager::install("ComplexHeatmap")
  • Rsamtools is used to read information about the sequence names, lengths and coverage from .bam files. To install it, type in the R console:
BiocManager::install("Rsamtools")
  • Biostrings is used to read information about the sequence names and lengths from .fasta files. To install it, type in the R console:
BiocManager::install("Biostrings")
  • rtracklayer is used to read information on genomic features from .gff files. To install it, type in the R console:
BiocManager::install("rtracklayer")
  • pracma is used to apply moving-average smoothing to the coverage data. To install it, type in the R console:
BiocManager::install("pracma")
  • BiocGenerics is used to support the many Bioconductor data structures and functions used in gmoviz To install it, type in the R console:
BiocManager::install("BiocGenerics")
BiocManager::install(c("GenomeInfoDb", "GenomicAlignments"))

3 Quick start

This section will walk through the basic usage of gmoviz. For more advanced usage, such as the incremental apporach to generating plots and making finer modifications, please see the advanced guide here.

3.1 Higher-level plotting steps

3.1.1 Insertion diagram

insertionDiagram is the ‘star’ function of gmoviz, designed to make it easier to plot (and thus show the copy number of) tandem insertion events. It requires only one input: insertion_data (either a GRanges or a data frame) with the following columns.1 If using a GRanges these should be the metadata columns and each range should correspond to the start/end of the insertion site. For data frame input, use the columns chr, start and end to describe the insertion site’s location. :

  • name A character string indicating the name of the insert. Each insert must have a unique name.

  • colour A character string of a colour to use. Supports hex colours like #000000 and named R colours like red

  • shape The shape that will be used to represent the insert:
    • rectangle is a rectangle (the default)
    • forward_arrow is a forwards-facing arrow
    • reverse_arrow is a backwards (reverse) facing arrow

  • length The length of a single copy of the insert, in bp

  • in_tandem The number of tandem copies of the insert (defaults to 1 if not supplied)

Note that in_tandem, shape and colour are all optional: if any of these columns are not supplied the inserts will still be plotted and default values will be allocated.

insertionDiagram will plot the tandem insert(s) on one sector, and use a ‘link’ to show the position they have inserted into in their target sequence (the other sector).2 A link is a semi-transparent shape which connects two sectors in the circle. It is very useful for demonstrating insertion events, or to indicate zooming into a particular part of the sector. :

example_insertion <- GRanges(seqnames = "chr12",
                      ranges = IRanges(start = 70905597, end = 70917885),
                      name = "plasmid", colour = "#7270ea", length = 12000,
                      in_tandem = 11, shape = "forward_arrow")
insertionDiagram(insertion_data = example_insertion, 
                 space_between_sectors = 20, xaxis_spacing = 45)

The above diagram shows the insertion of 11 tandem copies of ‘plasmid’ at the site chr12:70905597-70917885.

3.1.1.1 Alternate styles

There are four different styles of insertionDiagram: above was style 1: the emphasis was placed on the inserted sequence(s) and a rectangle was drawn for the inserted sequences to highlight that they are inserted as one long tandem group.

The same diagram can be plotted with the emphasis on the target sequence (style 2):

insertionDiagram(example_insertion, space_between_sectors = 20, style = 2)

Notice how the target sequence (chr12) is now larger than the inserted sequence

Additionally, the rectangle outside of the inserted sequences can be removed, using styles 3 and 4 (which, like styles 1 and 2 differ only in the relative sizes of the target and inserted sequences)

Styles 3 & 4 are recommended for single-copy insertions, like the following:

single_copy_insertion <- GRanges(seqnames = "chr12",
                       ranges = IRanges(start = 70905597, end = 70917885),
                       name = "plasmid", length = 12000)
insertionDiagram(single_copy_insertion, space_between_sectors = 20, style = 3)
insertionDiagram(single_copy_insertion, space_between_sectors = 20, style = 4)

Notice how we were able to omit the in_tandem, colour and shape columns from the insertion data, because we were happy with the default values.

3.1.1.2 Multiple insertion sites

Additionally, the insertionDiagram is capable of showing multiple integration sites. To do this, simply add another row to the insertion data:

multi_insertions <- GRanges(seqnames = "chr12",
                       ranges = IRanges(start = 70910000, end = 70920000),
                       name = "plasmid2", length = 10000)
multiple_insertions <- c(single_copy_insertion, multi_insertions)

## plot it
insertionDiagram(multiple_insertions)

Note that adding multiple insertion sites across different chromosomes using insertionDiagram is currently not supported; it simply becomes too messy and crowded on a single circle. If you are interested in showing many insertions throughout the genome, please see the multipleInsertionDiagram function here which uses multiple circles to avoid this problem

3.1.1.3 Adding more information

The insertionDiagram function (and also the featureDiagram function which works in a similar way) is also able to accept two optional inputs: labels (for example to indicate genes, exons or other genomic regions of interest) and coverage data.3 more information on adding labels to plots including colour coding and reading in label data from file can be found here. This section will explain how to add this information to a plot.

Firstly, labels:

nearby_genes <- GRanges(seqnames = c("chr12", "chr12"),
                        ranges = IRanges(start = c(70901000, 70911741),
                                         end = c(70910000, 70919000)),
                        label = c("Gene A", "Gene B"))
insertionDiagram(insertion_data = example_insertion, 
                 label_data = nearby_genes,
                 either_side = nearby_genes, 
                 space_between_sectors = 20,
                 xaxis_spacing = 75, # one x axis label per 75 degrees
                 label_size = 0.8, # smaller labels so they fit
                 track_height = 0.1) # smaller shapes so the labels fit

The labels for genes A and B have now been plotted on the insertion diagram. Importantly, we not only supplied our nearby_genes GRanges to the label_data argument, we also used it for either_side.4 Using a GRanges is only one of the ways to set either_side. Please see here for more information about controlling the amount of sequence shown either side of the integration site. This means that, unlike the previous figure which only plotted the area immediately around the integration site, this figure extends to cover the genes that we want to label.

Finally, we might like to also plot the coverage around the integration site: To do this, we need some coverage_data which can be read in from a .bam file (see here for how to do this), although for now we will just use simulated data.

## simulated coverage
coverage <- GRanges(seqnames = rep("chr12", 400),
                    ranges = IRanges(start = seq(from = 70901000, 
                                                 to = 70918955, by = 45),
                                     end = seq(from = 70901045, 
                                               to = 70919000, by = 45)),
                    coverage = c(runif(210, 10, 15), rep(0, 190)))

## plot with coverage
insertionDiagram(insertion_data = example_insertion, 
                 coverage_data = coverage,
                 coverage_rectangle = "chr12",
                 either_side = nearby_genes, 
                 space_between_sectors = 20,
                 xaxis_spacing = 75) # one x axis label per 75 degrees

Notice how the rectangle representing chr12 has been replaced by a line graph of the coverage. This allows us to see that there has been a deletion of gene B in addition to the insertion of 11 tandem copies of the plasmid.

Note: As well as supplying the coverage data to the coverage_data argument, it is vital that you also provide the sector(s) that you want to plot coverage for to coverage_rectangle.

3.1.1.4 How to set either_side

The either_side argument of insertionDiagram controls how much of the target sequence is shown either side of the insertion site. It can take four sorts of values:

  • "default" takes an extra 15% either side of the insertion site.

  • a single number e.g. 8000 will take that many bp either side of the insertion site

  • a vector of length two will set the start and end points for that sector. This allows you to show the gene(s) or other regions of interest near the insertion site in full

  • a GRanges object can also be used, for example the GRanges used to find the coverage over a region with getCoverageData or to hold gene labels.

For example:

## default
insertionDiagram(example_insertion, start_degree = 45, 
                 space_between_sectors = 20)
## single number
insertionDiagram(example_insertion, either_side = 10000, start_degree = 45,
                 space_between_sectors = 20)

## vector length 2
insertionDiagram(example_insertion, either_side = c(70855503, 71398284),
start_degree = 45, space_between_sectors = 20)

3.1.2 Plotting many insertions throughout the genome

The multipleInsertionDiagram is an extension of the insertionDiagram. It displays many (up to 8) insertions throughout the genome by drawing multiple insertionDiagram figures around a central whole genome circle like so:

Here, two insertions (one in chr1 and one in chr5) are depicted as their own diagrams, connected to the central genome with a ‘link’.

Drawing these diagrams is relatively straightforward (and very similar to the use of insertionDiagram) as only two inputs are necessary:

  • insertion_data: Insertion data in the same format as for insertionDiagram.

  • genome_ideogram_data: A GRanges (or data frame) that contains the name, start and end of each of the chromosomes in the genome (see here for how to import this from file)

For example, to generate the plot shown above:

ideogram_data <- GRanges(
  seqnames = paste0("chr", 1:6), 
  ranges = IRanges(start = rep(0, 6), end = rep(12000, 6)))
insertion_data <- GRanges(
  seqnames = c("chr1", "chr5"),
  ranges = IRanges(start = c(4000, 2000), end = c(4100, 2200)),
  name = c("ins1", "ins5"), length = c(100, 200))
multipleInsertionDiagram(insertion_data = insertion_data,
                         genome_ideogram_data = ideogram_data,
                         colour_set = rich_colours)

One key difference to note about multipleInsertionDiagram (as opposed to the regular insertionDiagram) is that rather than specifying colours for the sectors and links separately, you are only able to provide one ‘set’ (vector) of colours from which the sector and link colours will be assigned. gmoviz provides 5 sets of colours (more information here) or you can supply your own (but note that you must have at least 1 colour per row of genome_ideogram_data). Otherwise, save the figure as a vector image and open it in vector image editing programs to have finer control over the colour of each little bit of the diagram.

3.1.2.1 Customising your multiple insertion diagram

Just like insertionDiagram, multipleInsertionDiagram is able to display more information, like coverage and labels. These work just like for the regular insertion diagram:

## example coverage and labels
example_coverage <- GRanges(
seqnames = c(rep("chr1", 100), rep("chr5", 100)),
ranges = IRanges(start = c(seq(3985, 4114, length.out = 100),
                           seq(1970, 2229, length.out = 100)),
                 end = c(seq(3986, 4115, length.out = 100),
                         seq(1971, 2230, length.out = 100))),
                 coverage = c(runif(100, 0, 25), runif(100, 0, 15)))

 example_labels <- GRanges(seqnames = c("chr1", "chr5"),
                           ranges = IRanges(start = c(4000, 2000),
                           end = c(4120, 2200)),
                           label = c("Gene A", "Gene B"),
                           colour = c("red", "blue"))

## plot with coverage and labels
multipleInsertionDiagram(insertion_data = insertion_data,
                         genome_ideogram_data = ideogram_data,
                         coverage_rectangle = c("chr1", "chr5"),
                         coverage_data = example_coverage,
                         label_data = example_labels, 
                         label_colour = example_labels$colour)

As shown above, it’s perfectly fine to mix the labels and coverage data for different insertion events in the same GRanges/data frame.

For the arguments either_side and style however, this function differs slightly. These can be set overall (by a single value):

multipleInsertionDiagram(insertion_data = insertion_data,
                         genome_ideogram_data = ideogram_data,
                         either_side = 1000, style = 2)

However, it is usually best (especially for either_side) to choose a value that suits the particular event. This can be done with a named vector (for style) or a named list (for either_side):

## it's even possible to use GRanges in this way
either_side_GRange <- GRanges("chr5", IRanges(1000, 3200))
multipleInsertionDiagram(insertion_data = insertion_data,
                         genome_ideogram_data = ideogram_data,
                         either_side = list("ins1" = 1000, 
                                            "ins5" = either_side_GRange),
                         style = c("ins1" = 2, "ins5" = 4))

Warning: When specifying the events for either_side and style you need to use the event names, not the chromosome names (as there can be more than one event per chromosome, but each event must have a unique name)

3.1.3 Feature diagram

The function featureDiagram is more general than insertionDiagram; it is capable of displaying any ‘feature’ (a region of interest, for example a gene, exon or inserted sequence). Each feature is represented as a colour-coded shape, the exact nature of which is specified by the feature_data (see below for details).

Feature data should be a GRanges (or data frame including the chr, start & end columns as previously discussed) with the following columns:

  • label A character string which will be used to label the feature. If possible this should be relatively short due to the limited space within the circle. See here for a detailed discussion of the labelling of features.

  • colour A character string of a colour to use. Supports hex colours like #000000 and named R colours like red

  • shape The shape that will be used to represent the feature:
    • rectangle is a rectangle (the default)
    • forward_arrow is a forwards-facing arrow
    • reverse_arrow is a backwards (reverse) facing arrow
    • upwards_triangle is a triangle pointing up (out of the circle)
    • downwards_triangle is a triangle pointing down (into the circle) It is recommended to use forward_arrow and reverse_arrow for features on the + and - strands, respectively. rectangle is the default, and recommended for features that are not stranded.
  • track The index of the track on which to plot the feature
    • 0 represents the outermost track, where the ideogram rectangles are plotted
    • 1 is the default track: one track in from the ideogram
    • 2 and 3 and so on are further into the centre of the circle Please try to keep the number of tracks below 3 if possible, otherwise there may not be enough space in the circle for all of them.

  • type A character string describing the type of the feature (for example a gene or inserted sequence). This is only used for generating a legend for the features based on colour-coding by type and is thus completely optional.

Note that track, shape, colour and label are also optional: if any of these columns are not supplied the features will still be plotted and default values will be allocated.

The following examples highlight the diverse range of figures that can be plotted with featureDiagram:

3.1.3.1 Plasmid map

The circos plots generated by gmoviz are a great way to plot circular sequences like plasmids. In this example, we will generate a plasmid map. Firstly, we need two key inputs:

  • the ideogram_data (a GRanges describing the name, start and end positions of the plasmid).5 More details about ideogram_data including how to specify it using a data frame rather than a GRanges can be found here

  • the feature_data (a GRanges or describing at least the location of each feature, and optionally specifying a label, shape, colour and track with which to plot). For now we will just use the defaults that are assigned by the getFeatures when reading in features from a .gff file (more details on this here)

## the data
plasmid_ideogram <- GRanges("plasmid", IRanges(start = 0, end = 3000))
plasmid_features <- getFeatures(
  gff_file = system.file("extdata", "plasmid.gff3", package="gmoviz"),
  colour_by_type = FALSE, # colour by name rather than type of feature
  colours = rich_colours) # choose colours from rich_colours (see ?colourSets)

## the plot
## plot plasmid map with coverage
featureDiagram(plasmid_ideogram, plasmid_features, track_height = 0.17,
               label_track_height = 0.11, 
               space_between_sectors = 0, # continuous circle
               xaxis_spacing = 30, # x axis label every 30 degrees
               start_degree = 90) # start from 90 degrees (12 o'clock)

Another great addition to the plasmid map is the coverage data, which can show us which part(s) of the plasmid were inserted into the target genome. As before, we will use simulated coverage data:

## make some simulated coverage data
coverage <- GRanges(seqnames = rep("plasmid", 200),
                    ranges = IRanges(start = seq(from = 0, to = 2985, by = 15),
                                     end = seq(from = 15, to = 3000, by = 15)),
                    coverage = c(runif(110, 10, 15), rep(0, 90)))
## plot plasmid map with coverage
featureDiagram(plasmid_ideogram, plasmid_features, track_height = 0.17,
               coverage_rectangle = "plasmid", # don't forget this!
               coverage_data = coverage, 
               label_track_height = 0.11, space_between_sectors = 0, 
               xaxis_spacing = 30, start_degree = 90)

As we can see, there is relatively uniform coverage on the first half of the plasmid, whilst there is none for the second half. This suggests that our promoter, gene and GFP have been inserted, but the ampR and rest of the plasmid sequence have not.

3.1.3.2 Insertion of a complex construct

While insertionDiagram is great for showing the copy number in tandem insertions, featureDiagram is better for displaying the insertion of complex constructs. In this example, we will draw a complex gene editing design, in which one sector will display the inserted construct and the other sector will be the target sequence.

Firstly, we will read in the necessary data: the ideogram_data, exon_label (a label indicating the exon that will be targeted by the gene editing event) and of course the feature_data.

## the data
complex_ideogram <- GRanges(seqnames = c("Gene X", "Template"),
                            ranges = IRanges(start = c(5000, 0), 
                                             end = c(6305, 3788)))
## exon label
exon_label <- GRanges("Gene X", IRanges(5500, 5960), label = "Exon 2")
  
## features
complex_features <- getFeatures(
  gff_file = system.file("extdata", "complex_insertion.gff3", 
                         package="gmoviz"),
  colours = rich_colours)

Before we plot, we will make a few changes to the complex_features GRanges: by default the getFeatures function will only assign features to ‘track 1’ (the track that is closest to the outside of the circle) and shapes will be either arrows (for genes) or rectangles (for other features).6 Again, this is described in detail in the features section.

In this case, it will look better if our cut sites are drawn as triangles and are set slightly away from the remaining features on the second track:

## make a few edits to the feature_data: 
complex_features$track[c(3,4)] <- 2 # cut sites on the next track in
complex_features$shape[c(3,4)] <- "upwards_triangle" # triangles 

## plot the diagram
featureDiagram(ideogram_data = complex_ideogram, 
               feature_data = complex_features, 
               label_data = exon_label, custom_sector_width = c(0.6, 0.4),
               sector_colours = c("#6fc194", "#84c6d6"),
               sector_border_colours = c("#599c77", "#84c6d6"),
               space_between_sectors = 25, start_degree = 184, 
               label_size = 1.1)

As for insertionDiagram it is possible to add both labels and coverage data to a graphic produced with featureDiagram. However, featureDiagram also accepts one other optional input: the link_data. This can be used to draw a link (like the one used to show the integration event in insertionDiagram) but in this case the link can be positioned wherever you desire.

To make the fact that the template is inserted into gene X a bit clearer, we can use a link. To draw a link, we need to supply a data.frame containing the link information. It should have 2 rows, one for each end of the link.7 Note that this same method can be used to add links to any gmoviz plot using the circlize functions circos.link and circos.genomicLink. For more information on how to use circlize functions to further customise gmoviz plots, please see here. For example, the following link goes from 5600-5706bp on gene X to 0-3788bp on the template.

# link data
link <- data.frame(chr = c("Gene X", "Template"),
                   start = c(5600, 0),
                   end = c(5706, 3788))
featureDiagram(ideogram_data = complex_ideogram, 
               feature_data = complex_features, label_data = exon_label, 
               custom_sector_width = c(0.6, 0.4),
               sector_colours = c("#6fc194", "#84c6d6"),
               sector_border_colours = c("#599c77", "#84c6d6"),
               space_between_sectors = 25, start_degree = 184, 
               label_size = 1.1, link_data = link)

This clearly indicates that connection between the template and gene X sectors.

Session Info

This vignette was rendered in the following environment:

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#> Platform: x86_64-pc-linux-gnu (64-bit)
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#> 
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#> [61] rjson_0.2.20                bitops_1.0-6               
#> [63] rmarkdown_2.3               curl_4.3                   
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#> [67] GenomicAlignments_1.24.0    rtracklayer_1.48.0         
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#> [77] tidyselect_1.1.0            dbplyr_1.4.4               
#> [79] xfun_0.17

References

Gu, Zuguang, Lei Gu, Roland Eils, Matthias Schlesner, and Benedikt Brors. 2014. “Circlize Implements and Enhances Circular Visualization in R.” Bioinformatics 30 (19). Oxford University Press (OUP):2811–2. https://doi.org/10.1093/bioinformatics/btu393.

Krzywinski, M., J. Schein, I. Birol, J. Connors, R. Gascoyne, D. Horsman, S. J. Jones, and M. A. Marra. 2009. “Circos: An Information Aesthetic for Comparative Genomics.” Genome Research 19 (9). Cold Spring Harbor Laboratory:1639–45. https://doi.org/10.1101/gr.092759.109.