gmoviz: seamless visualisation of complex genomic variations in GMOs and edited cell lines – An overview
2023-10-24
Contents
1 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
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
gmovizTo install it, type in the R console:
BiocManager::install("BiocGenerics")- GenomeInfoDb and GenomicAlignments are used to read in the coverage data from .bam files. To install them, type in the R console:
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
#000000and named R colours likeredshape The shape that will be used to represent the insert:
rectangleis a rectangle (the default)forward_arrowis a forwards-facing arrowreverse_arrowis 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.
8000will take that many bp either side of the insertion sitea 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
getCoverageDataor 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
#000000and named R colours likeredshape The shape that will be used to represent the feature:
rectangleis a rectangle (the default)forward_arrowis a forwards-facing arrowreverse_arrowis a backwards (reverse) facing arrowupwards_triangleis a triangle pointing up (out of the circle)downwards_triangleis a triangle pointing down (into the circle) It is recommended to useforward_arrowandreverse_arrowfor features on the + and - strands, respectively.rectangleis the default, and recommended for features that are not stranded.
track The index of the track on which to plot the feature
0represents the outermost track, where the ideogram rectangles are plotted1is the default track: one track in from the ideogram2and3and 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 aboutideogram_dataincluding how to specify it using a data frame rather than a GRanges can be found herethe
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 thegetFeatureswhen 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
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#> [89] SparseArray_1.2.0 rjson_0.2.21
#> [91] memoise_2.0.1 htmltools_0.5.6.1
#> [93] lifecycle_1.0.3 httr_1.4.7
#> [95] GlobalOptions_0.1.2 bit64_4.0.5References
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