gmoviz: seamless visualisation of complex genomic variations in GMOs and edited cell lines – An overview
Introduction
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
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:
GitHub
To install the development version of gmoviz from
GitHub, type in the R console:
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:
- 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:
- 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:
- ComplexHeatmap is used to generate legends. To install it, type in the R console:
- Rsamtools is used to read information about the sequence names, lengths and coverage from .bam files. To install it, type in the R console:
- Biostrings is used to read information about the sequence names and lengths from .fasta files. To install it, type in the R console:
- rtracklayer is used to read information on genomic features from .gff files. To install it, type in the R console:
- pracma is used to apply moving-average smoothing to the coverage data. To install it, type in the R console:
- BiocGenerics
is used to support the many Bioconductor data structures and functions
used in
gmovizTo install it, type in the R console:
- GenomeInfoDb and GenomicAlignments are used to read in the coverage data from .bam files. To install them, type in the R console:
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
#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 :
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):
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.
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)
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
#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:
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).5the
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.
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:
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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,startandendto describe the insertion site’s location.↩︎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.↩︎
more information on adding labels to plots including colour coding and reading in label data from file can be found here.↩︎
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.↩︎More details about
ideogram_dataincluding how to specify it using a data frame rather than a GRanges can be found here↩︎Again, this is described in detail in the features section.↩︎
Note that this same method can be used to add links to any
gmovizplot using thecirclizefunctionscircos.linkandcircos.genomicLink. For more information on how to usecirclizefunctions to further customisegmovizplots, please see here.↩︎