# gmoviz: seamless visualisation of complex genomic variations in GMOs and edited cell lines – An overview

# Introduction

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):

- Visualise complex structural variations, particularly relating to tandem insertions

- Generate plots in a single function call, or build them piece by piece for finer customisation

- Integration with existing Bioconductor data structures

Flowchart summarising the usage and key functions of gmoviz

## 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

# Installation

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

## Bioconductor

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

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

## GitHub

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

    BiocManager::install("malhamdoosh/gmoviz")

## 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")

- Seqinfo and GenomicAlignments are used to read in the coverage data from .bam files. To install them, type in the R console:

    BiocManager::install(c("Seqinfo", "GenomicAlignments"))

# 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.

## Higher-level plotting steps

### 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 :

- 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 :

    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.

#### 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.

#### 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

#### 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 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 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`.

#### 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)

### 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.

#### 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)

### 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`:

#### 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

- 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.

#### 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

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 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:

    #> R version 4.6.0 (2026-04-24)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.4 LTS
    #>
    #> Matrix products: default
    #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    #>
    #> locale:
    #>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    #>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    #>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    #>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    #>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    #> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    #>
    #> time zone: Etc/UTC
    #> tzcode source: system (glibc)
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] pasillaBamSubset_0.48.0 knitr_1.51              gmoviz_1.24.0
    #>  [4] GenomicRanges_1.64.0    Seqinfo_1.2.0           IRanges_2.46.0
    #>  [7] S4Vectors_0.50.0        BiocGenerics_0.58.0     generics_0.1.4
    #> [10] circlize_0.4.18         BiocStyle_2.40.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] KEGGREST_1.52.0             SummarizedExperiment_1.42.0
    #>  [3] shape_1.4.6.1               rjson_0.2.23
    #>  [5] xfun_0.57                   bslib_0.10.0
    #>  [7] GlobalOptions_0.1.4         Biobase_2.72.0
    #>  [9] lattice_0.22-9              vctrs_0.7.3
    #> [11] tools_4.6.0                 bitops_1.0-9
    #> [13] curl_7.1.0                  parallel_4.6.0
    #> [15] AnnotationDbi_1.74.0        RSQLite_2.4.6
    #> [17] blob_1.3.0                  cluster_2.1.8.2
    #> [19] Matrix_1.7-5                RColorBrewer_1.1-3
    #> [21] cigarillo_1.2.0             lifecycle_1.0.5
    #> [23] compiler_4.6.0              Rsamtools_2.28.0
    #> [25] Biostrings_2.80.0           codetools_0.2-20
    #> [27] ComplexHeatmap_2.28.0       clue_0.3-68
    #> [29] htmltools_0.5.9             sys_3.4.3
    #> [31] buildtools_1.0.0            sass_0.4.10
    #> [33] RCurl_1.98-1.18             yaml_2.3.12
    #> [35] pracma_2.4.6                crayon_1.5.3
    #> [37] jquerylib_0.1.4             BiocParallel_1.46.0
    #> [39] cachem_1.1.0                DelayedArray_0.38.0
    #> [41] iterators_1.0.14            abind_1.4-8
    #> [43] foreach_1.5.2               digest_0.6.39
    #> [45] restfulr_0.0.16             maketools_1.3.2
    #> [47] fastmap_1.2.0               grid_4.6.0
    #> [49] colorspace_2.1-2            cli_3.6.6
    #> [51] SparseArray_1.12.0          S4Arrays_1.12.0
    #> [53] GenomicFeatures_1.64.0      XML_3.99-0.23
    #> [55] bit64_4.8.0                 httr_1.4.8
    #> [57] rmarkdown_2.31              XVector_0.52.0
    #> [59] matrixStats_1.5.0           bit_4.6.0
    #> [61] png_0.1-9                   GetoptLong_1.1.1
    #> [63] memoise_2.0.1               evaluate_1.0.5
    #> [65] BiocIO_1.22.0               doParallel_1.0.17
    #> [67] rtracklayer_1.72.0          rlang_1.2.0
    #> [69] gridBase_0.4-7              DBI_1.3.0
    #> [71] BiocManager_1.30.27         jsonlite_2.0.0
    #> [73] R6_2.6.1                    MatrixGenerics_1.24.0
    #> [75] GenomicAlignments_1.48.0

# References

Gu, Zuguang, Lei Gu, Roland Eils, Matthias Schlesner, and Benedikt Brors. 2014. “Circlize Implements and Enhances Circular Visualization in r.” Bioinformatics 30 (19): 2811–12. https://doi.org/10.1093/bioinformatics/btu393.
Krzywinski, M., J. Schein, I. Birol, et al. 2009. “Circos: An Information Aesthetic for Comparative Genomics.” Genome Research 19 (9): 1639–45. https://doi.org/10.1101/gr.092759.109.

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.↩︎

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.↩︎

3. more information on adding labels to plots including colour coding and reading in label data from file can be found here.↩︎

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.↩︎

5. More details about `ideogram_data` including how to specify it using a data frame rather than a GRanges can be found here↩︎

6. Again, this is described in detail in the features section.↩︎

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.↩︎