BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons.

Abstract

Visualisation of genome comparisons is invaluable for helping to determine genotypic differences between closely related prokaryotes. New visualisation and abstraction methods are required in order to improve the validation, interpretation and communication of genome sequence information; especially with the increasing amount of data arising from next-generation sequencing projects. Visualising a prokaryote genome as a circular image has become a powerful means of displaying informative comparisons of one genome to a number of others. Several programs, imaging libraries and internet resources already exist for this purpose, however, most are either limited in the number of comparisons they can show, are unable to adequately utilise draft genome sequence data, or require a knowledge of command-line scripting for implementation. Currently, there is no freely available desktop application that enables users to rapidly visualise comparisons between hundreds of draft or complete genomes in a single image.
BLAST Ring Image Generator (BRIG) can generate images that show multiple prokaryote genome comparisons, without an arbitrary limit on the number of genomes compared. The output image shows similarity between a central reference sequence and other sequences as a set of concentric rings, where BLAST matches are coloured on a sliding scale indicating a defined percentage identity. Images can also include draft genome assembly information to show read coverage, assembly breakpoints and collapsed repeats. In addition, BRIG supports the mapping of unassembled sequencing reads against one or more central reference sequences. Many types of custom data and annotations can be shown using BRIG, making it a versatile approach for visualising a range of genomic comparison data. BRIG is readily accessible to any user, as it assumes no specialist computational knowledge and will perform all required file parsing and BLAST comparisons automatically.
There is a clear need for a user-friendly program that can produce genome comparisons for a large number of prokaryote genomes with an emphasis on rapidly utilising unfinished or unassembled genome data. Here we present BRIG, a cross-platform application that enables the interactive generation of comparative genomic images via a simple graphical-user interface. BRIG is freely available for all operating systems at http://sourceforge.net/projects/brig/.

Full text

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SOFTWARE Open Access
BLAST Ring Image Generator (BRIG): simple
prokaryote genome comparisons
Nabil-Fareed Alikhan, Nicola K Petty, Nouri L Ben Zakour and Scott A Beatson*
Abstract
Background: Visualisation of genome comparisons is invaluable for helping to determine genotypic differences
between closely related prokaryotes. New visualisation and abstraction methods are required in order to improve
the validation, interpretation and communication of genome sequence information; especially with the increasing
amount of data arising from next-generation sequencing projects. Visualising a prokaryote genome as a circular
image has become a powerful means of displaying informative comparisons of one genome to a number of
others. Several programs, imaging libraries and internet resources already exist for this purpose, however, most are
either limited in the number of comparisons they can show, are unable to adequately utilise draft genome
sequence data, or require a knowledge of command-line scripting for implementation. Currently, there is no freely
available desktop application that enables users to rapidly visualise comparisons between hundreds of draft or
complete genomes in a single image.
Results: BLAST Ring Image Generator (BRIG) can generate images that show multiple prokaryote genome
comparisons, without an arbitrary limit on the number of genomes compared. The output image shows similarity
between a central reference sequence and other sequences as a set of concentric rings, where BLAST matches are
coloured on a sliding scale indicating a defined percentage identity. Images can also include draft genome
assembly information to show read coverage, assembly breakpoints and collapsed repeats. In addition, BRIG
supports the mapping of unassembled sequencing reads against one or more central reference sequences. Many
types of custom data and annotations can be shown using BRIG, making it a versatile approach for visualising a
range of genomic comparison data. BRIG is readily accessible to any user, as it assumes no specialist computational
knowledge and will perform all required file parsing and BLAST comparisons automatically.
Conclusions: There is a clear need for a user-friendly program that can produce genome comparisons for a large
number of prokaryote genomes with an emphasis on rapidly utilising unfinished or unassembled genome data.
Here we present BRIG, a cross-platform application that enables the interactive generation of comparative genomic
images via a simple graphical-user interface. BRIG is freely available for all operating systems at http://sourceforge.
net/projects/brig/.
Background
With the dramatic improvement of next-generation
sequencing technologies over the last five years, there
has been a corresponding increase in the amount of
publicly available genomic data. As of February 2011,
Entrez Genome Projects [1] catalogued 6,071 bacterial
and archaeal genome projects. Of these, 1,444 had com-
plete genome sequences, 42 percent of which were
released within the last three years. In addition, 3,872
on-going genome projects were registered with the data-
base; 1,734 of which had a draft sequence publicly avail-
able. These projects do not include the ten terabase-
pairs of sequence data across more than 6,500 entries
currently available in the Short Read Archive, the public
repository specifically for raw data from next-generation
sequencing [2]. Current genome visualisation and data
analysis methods are struggling to keep up as it becomes
a routine requirement for biologists to compare a new
genome to scores, if not hundreds, of other genomes at
once.
* Correspondence: s.beatson@uq.edu.au
Australian Infectious Diseases Research Centre, School of Chemistry and
Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072,
Australia
Alikhan et al. BMC Genomics 2011, 12:402
http://www.biomedcentral.com/1471-2164/12/402
© 2011 Alikhan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

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Genome visualisation methods use linear or circular
representations. Linear representations, like those that
can be generated using Artemis Comparison Tool
(ACT) [3], Genome2D [4], Combo [5], VISTA [6],
Mauve [7], BugView [8] and Genomorama [9], have
advantages in showing insertions and deletions between
genomic sequences and certain programs, like Mauve
and ACT, can show genome rearrangements. However,
it is difficult to summarise large datasets using these
tools. Programs that generate circular figures, like
Microbial Genome Viewer [10] and Genome Projector
[11], are designed to annotate a single chromosome and
have no support for whole genome comparative data.
These programs are restricted to published genomes
and do not let users analyse their own genomic
sequences. DNAPlotter [12] allows the user to input
their own genome sequences and can show genome
comparisons, but only by generating this information
separately and loading it in as custom annotation tracks.
There are comparative circular genome visualisation
alternatives available online, such as CGView Server [13]
and GeneWiz browser [14], which allow users to upload
their own sequences and provide a similar service,
although GeneWiz browser can display mapped read
data, whereas CGView Server cannot. However, both of
these tools are only available as internet resources and
limit the number of genome comparisons that can be
shown on a single image. Command-line based alterna-
tives and imaging libraries also exist, which require
users to prepare all data and customisation through text
files, such as Circos [15], CGView [16], Genome Dia-
gram [17] and BLASTAtlas [18]. While these programs
are very powerful, they require command-line manipula-
tion and scripting to use, putting them out of reach of
many biologist end-users.
To address these issues, we present the BLAST Ring
Image Generator (BRIG); an easy-to-use, cross-platform
desktop application that enables rapid visualisation of
BLAST comparisons to one or more central reference
sequences using complete, draft or unassembled genome
data.
Implementation
The BLAST Ring Image Generator (BRIG) is a cross-
platform desktop application written in Java 1.6. It uses
CGView [16] for image rendering and BLAST [19] for
genome comparisons. It has a graphical user interface,
programmed on the Swing framework, which takes the
user step-by-step through the generation of a circular
image. The settings used to generate a particular image
can be saved for re-use with different genome data, or
the entire session can be bundled and saved for later.
The image can be generated in JPEG, PNG, SVG or
SVGZ format. An example of BRIG’s output can be
seen in Figure 1. A user guide describing step-by-step
tutorials for several visualisation tasks and accompany-
ing example files are provided at http://sourceforge.net/
projects/brig/files/.
Results
Whole genome comparisons
BRIG is capable of generating circular comparison
images for prokaryote genomes, showing multiple gen-
ome comparisons in a single image, and displaying simi-
larity between a reference genome in the centre against
other query sequences as a set of concentric rings
coloured according to BLAST identity. An example
image (Figure 1) produced by BRIG shows a comparison
of a draft Escherichia coli genome with 13 other E. coli
and 14 Salmonella genomes (Table 1). The varying col-
our gradient of rings 5-16 in Figure 1 indicates a
BLAST match of a particular percentage identity, as
shown in the key. BLAST matches can be filtered
according to a minimum percentage identity or E-value
cut-off (or indeed any available BLAST option). These
matches are calculated from the perspective of the refer-
ence sequence; consequently, regions that are absent
from the reference genome but present in one or more
of the query sequences will not be displayed. Data from
different genomes can be collated into a single lane,
which enables visualisation of a large number of gen-
omes and allows users to compare genomes as a group
against the central reference sequence. This is shown in
Figure 1 where the comparison results from 3 E. coli
strains, MG1655, HS and W3310, have been grouped
together to represent regions of the reference genome
that are found in non-pathogenic E. coli.
Users can highlight regions of the reference genome
with custom annotations by specifying the label text,
colour, shape, and position of features either manually,
or by uploading this information as a tab-delimited file.
Alternatively, selected annotations can be uploaded
from a GenBank or EMBL file; for instance, the annota-
tions shown in the outermost ring in Figure 1 have been
read from the GenBank file of E. coli O157:H7 str. Sakai
[20] by selecting ‘misc_features’ that contain the text
‘Sp’ or ‘SpLE’, which correspond to annotated prophage
regions [20].
Generating comparisons of a large number of gen-
omes raises the issue of memory usage. To produce Fig-
ure 1, with its comparison against 27 genomes each of
approximately 5 Megabase-pairs in size, one Gigabyte of
RAM was required on a standard desktop computer.
The memory requirement can be reduced by filtering
the BLAST results according to E-value and percentage
identity cut-offs within BRIG. Alternatively, the amount
of memory allocated to BRIG can be altered from within
the program.
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A high level of customisation through a user-friendly
graphical user interface
A variety of genomic data sources can be used to produce
an image, including BLAST comparisons of protein or
nucleotide sequences from GenBank, EMBL and FASTA
files. BRIG will internally handle all genome comparisons
by converting GenBank or EMBL files into FASTA format,
creating any necessary BLAST databases, running BLAST
and converting the results into a format that CGView ren-
ders as the circular image. Users do not have to interact
directly with BLAST or CGView, and nor is any knowl-
edge of using command-line programs assumed. By
default the central reference sequence is treated as the
subject BLAST database with the rings representing
matches to individual query sequences.
Users are taken step-by-step through the process to
create a circular comparison image via a graphical user
interface (Figure 2). In the first screen (Figure 2A), users
specify data they would like to compare to a central
reference sequence. In the second screen (Figure 2B),
users are able to configure the individual concentric
rings; choosing which data they would like to show in
each lane and make aesthetic choices including colour
or ring size. Lastly, the settings can be reviewed and
submitted for BLAST [19] alignment and image drawing
using CGView [16] (Figure 2C). Image rendering
Figure 1 BRIG output image of a simulated draft E. coli O157:H7 str. Sakai genome. Figure 1 shows a draft E. coli genome compared
against 27 other prokaryote genomes (the full list of genomes is described in Table 1). The reference genome is an ordered set of contigs,
assembled using GS De Novo Assembler (454 Life Sciences/Roche) version 2.3, from simulated sequencing reads generated by MetaSim [21]
based on the E. coli O157:H7 str. Sakai genome [GenBank:BA000007]. After assembly contigs were ordered against the complete E. coli O157:H7
Sakai genome using Mauve [7]. The innermost rings show GC skew (purple/green) and GC content (black). The third innermost ring shows
genome coverage (brown); genome regions with coverage more than one standard deviation (~ 41) from the mean coverage (~ 94) are
represented as blue spikes. Contig boundaries are shown outside this ring as alternating red and blue bars. The remaining rings show BLAST
comparisons of 27 other complete E. coli and Salmonella genomes against the simulated draft genome assembly (in several cases, multiple
genome comparisons are collapsed into a single ring, Table 1). The outermost ring highlights the Sakai prophage, and prophage-like (Sp & SpLE)
regions as described by Hayashi et al. [20], shown in navy blue and fuchsia, respectively. SpLE 4, containing the locus of enterocyte effacement,
is shown in green.
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settings and genome comparison configurations for a
particular BRIG image can be saved and reused as an
XML profile file. Alternatively, a number of sample tem-
plates are available to users, in order to quickly generate
an image with optimised size and colour settings.
Users can add their own annotations to a BRIG image
through the ‘add custom features’ dialog, to produce
complex yet informative images. Users can alter every
aspect of visualisation, including: image size, label visibi-
lity, texts and fonts, colours of ring lanes, the gradient
reflecting percentage identity, and custom labels inside
or outside of a ring.
Data such as transcriptome and microarray expression
values can be graphed and displayed as a ring in the cir-
cular image. These custom graphs can be produced
from user-defined data in a space or tab-delimited file
that either includes; the start and stop positions and the
value for that region; or a single value for every base
pair, with one value per line. To ensure that a useful
visualisation is produced regardless of the data source,
the default graphing function is to display skew from
the mean value, similar to the coverage graph in Figure
1. Users can choose to override this behaviour and scale
the graph between zero and a user-defined value.
Visualisation of information from a user-defined set of
sequences
In many instances users may only be interested in the
presence, absence or variation of a certain set of
sequences amongst a number of different genomes.
BRIG can visualise this kind of comparison if provided
with a multi-FASTA sequence file (of genes, proteins or
sequence regions) that will be concatenated to form the
central reference ring. An example of such analysis can
be seen in Figure 3A, where the translated nucleotide
sequences from genes encoded by the Locus of Entero-
cyte Effacement (LEE) pathogenicity island in the Enter-
ohaemorrhagic E. coli strain O157:H7 Sakai genome
were compared to the translated nucleotide sequence of
whole genomes of other published Enterohaemorrhagic
Table 1 Genome sequences included in Figure 1
Ring number1
Genome AccessionReference
Ring 5Enterohaemorrhagic E. coli O157:H7 str. EDL933AE005174 [25]
Ring 6Enterohaemorrhagic E. coli O157:H7 str. EC4115CP001164 J. Craig Venter Institute
Ring 7Non-pathogenic E. coli str. K12 substr. MG1655U00096[26]
Non-pathogenic E. coli str. K12 substr. W3110AP009048[27]
Non-pathogenic E. coli HS CP000802[28]
Ring 8Non-pathogenic E. coli IAI1CU928160[29]
Ring 9Non-pathogenic E. coli B str. REL606 CP000819[30]
Ring 10Environmental E. coli SMS-3-5CP000970[31]
Ring 11Uropathogenic E. coli CFT073AE014075[32]
Ring 12Uropathogenic E. coli UTI89 CP000243[33]
Ring 13 Extraintestinal E. coli IAI39 CU928164[29]
Ring 14Extraintestinal E. coli UMN026CU928163[29]
Ring 15Extraintestinal E. coli S88 CU928161 [29]
Ring 16
Salmonella enterica subsp. enterica serovar Heidelberg str. SL476 CP001120J. Craig Venter Institute
Salmonella enterica subsp. enterica serovar Dublin str. CT_02021853CP001144J. Craig Venter Institute
Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91AM933173 [34]
Salmonella enterica subsp. enterica serovar Paratyphi B str. SPB7CP000886W.U. Genome Sequencing Center
Salmonella enterica subsp. enterica serovar Enteritidis str. P125109AM933172 [34]
Salmonella enterica subsp. enterica serovar cholerasuis str. SC-B67 AE017220[35]
Salmonella enterica subsp. enterica serovar Newport str. SL254 CP001113J. Craig Venter Institute
Salmonella enterica subsp. enterica serovar Typhi str. CT18AL513382[36]
Salmonella enterica subsp. enterica serovar Typhi str. Ty2FN424405[37]
Salmonella enterica subsp. enterica serovar Agona str. SL483CP001138J. Craig Venter Institute
Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150CP000026[38]
Salmonella enterica subsp. enterica serovar Typhimurium str. LT2AE006468[39]
Salmonella enterica subsp. enterica serovar Paratyphi C str. RKS4594CP000857[40]
1Genomes are listed as they appear on Figure 1, from innermost to outermost. Rings 1 to 4 correspond to GC-skew, GC-content, coverage and contig
boundaries, respectively.
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E. coli; two Enteropathogenic E. coli and one Citrobacter
rodentium (related bacterial pathogens that also carry
the LEE); and E. coli K-12 MG1655 (a non-pathogenic
strain that does not contain the LEE) Table 2.
Figure 2 Screenshots of BRIG’s graphical user interfaces.
Screenshots of BRIG’s three main graphical user interfaces: A. The
“select input data” window where users are able to specify the
reference sequences, query sequences, and output folder. B. The
“customise ring” window where one or more query sequence files,
that were loaded in the previous window, are chosen for each
concentric ring. Image drawing configurations, including ring colour,
size, identity thresholds and legend text can also be specified.
Custom annotations, graphs, or a ring showing contig boundary
information can be added at this point. C. In the “confirmation”
window settings are confirmed and submitted to BRIG to perform
the genome comparisons and image rendering. Progress is written
to a console box. From any window prior to job submission,
configurations for BLAST or CGview can be altered via the
preferences pull-down menu.
Figure 3 Using BRIG to compare a multi-sequence reference
against complete genomes or unassembled sequence reads.
BRIG image showing the presence, absence and variation of
individual genes from the E. coli O157:H7 str. Sakai Locus of
Enterocyte Effacement (LEE) in related pathogens and E. coli K12, a
non-pathogenic strain of E. coli known to lack the LEE region.
Images show a multi-sequence reference consisting of the
translated nucleotide sequences of the 41 LEE protein-coding
genes, in order, retrieved from the E. coli O157:H7 str. Sakai genome
[GenBank: BA000007]. Labels around the outside of each circular
image correspond to LEE gene names. In both panels the rings
display BLAST× comparisons of 10 bacterial genomes with the
translated nucleotide sequences of the LEE genes: A. Comparison
with complete genome sequences (Table 2). B. Comparison with
unassembled, simulated 100 base-pair Illumina reads based on the
complete genome sequences used in Figure 3A. The image is
scaled to the nucleotide length of the genes. Long tick marks on
the outer and inner circumference of the ring indicate increments
of 1 kilobase-pairs and short tick marks indicate 200 base-pairs.
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Comparing translated nucleotide sequences through
protein alignment offers better sensitivity for divergent
sequences than comparing nucleotide sequences only.
BRIG is capable of using raw sequencing reads as
query sequences to provide rapid preliminary insights
into unassembled draft genome or meta-genome data.
To illustrate this feature, we have simulated unas-
sembled Illumina data using MetaSim [21] by randomly
sampling one million 100 base pair sequences from the
complete genome sequences shown in Figure 3A and
applying an Illumina error model. Reads were translated
into peptides and used as query sequences in BRIG
(using BLASTx) to search against the same central refer-
ence sequence in Figure 3A, producing the image shown
in Figure 3B. Despite being based only on raw sequen-
cing reads, the representation of sequence presence,
absence and variation in Figure 3B is highly similar to
that found when using whole genome sequences in Fig-
ure 3A.
Figure 3 represents the presence of protein encoding
genes within each query genome as a full and vividly
coloured bar (e.g. see the E. coli O157:H7 strains for the
translated espD gene). Gene absence can be observed as
a blank/white region, like any of the results for E. coli
K12 MG1665, whose genome does not carry the LEE.
Variation in the translated sequences will have a lower
sequence identity compared to the reference genome
and appear with a fully coloured but slightly faded bar,
as seen in Figure 3 for E. coli O103:H2 and C. roden-
tium when searching for EspZ, or where the bar is not
fully coloured, such as for E. coli O111:H- and O127:H6
when searching for EspH. As with any BRIG image, per-
centage identity cut-off values can be customised to
alter the dynamic range of colour shown in each ring.
The annotations in Figure 3 illustrate a feature of BRIG
where users can opt to load the FASTA headings from a
multi-FASTA reference sequence and use these headers
to annotate their image.
Visualisation of information from draft genome
assemblies
BRIG is a valuable tool for analysing draft genome
sequences. A draft genome that has been assembled into
a set of contiguous sequences (contigs) or scaffolds
(ordered contigs separated by gaps denoted by N’s) in
multi-FASTA format can be used as a reference
sequence. Contig or scaffold boundaries can be shown
as alternating blue or red segments as a custom ring. In
addition, by uploading standard genome assembly files
(e.g. ACE or SAM), the underlying sequencing reads can
be included as a custom graph to show genome cover-
age. This procedure can help to highlight misassemblies,
areas of low coverage and repeat regions that warrant
further attention. For instance, the read coverage and
contig boundaries in Figure 1 were generated from the
ACE file produced by GS De Novo Assembler (454 Life
Sciences/Roche). ACE files produced by Consed/Phrap
[22] are also acceptable. The reordering of contigs in a
draft genome is often carried out after assembly without
reordering the corresponding assembly files. To address
this, users can use BRIG’s graph conversation module to
reposition the coverage information from the original
ace file to be consistent with the modified draft genome
sequence based on a BLASTn comparison.
The genome coverage feature of BRIG can also show
read mapping information. This can be a useful
approach for determining differences amongst multiple
unassembled genome datasets relative to the central
reference sequence(s). As described previously, BRIG
supports read or contig mapping by using BLAST (e.g.
Figure 3B). Alternatively, read mapping can be per-
formed externally and read into BRIG as an ACE or
SAM file and shown as a coverage graph. ACE files can
be produced by the 454 Life Sciences/Roche GS Refer-
ence Mapper application, which maps 454 reads to a
reference sequence, and there are a number of tools
that use the SAM format as the standard file format for
Table 2 Table of genomes included in Figure 3 and Figure 4
Ring numberGenomeAccessionReference
Ring 1Enterohaemorrhagic E. coli O157:H7 str. SakaiBA000007[20]
Ring 2Enterohaemorrhagic E. coli O157:H7 str. EDL933AE005174[25]
Ring 3Enterohaemorrhagic E. coli O157:H7 str. EC4115CP001164J. Craig Venter Institute
Ring 4Enterohaemorrhagic E. coli O157:H7 str. TW14359CP001368[41]
Ring 5Enterohaemorrhagic E. coli O103:H2 str. 12009AP010958[42]
Ring 6Enterohaemorrhagic E. coli O111:H- str. 11128AP010960[42]
Ring 7Enteropathogenic E. coli O127:H6 str. E2348/69FM180568[43]
Ring 8Enteropathogenic E. coli O55:H7 str. CB9615CP001846[44]
Ring 9
Citrobacter rodentium ICC168FN543502[45]
Ring 10 Non-pathogenic E. coli str. K12 substr. MG1655U00096[26]
1Genomes are listed as they appear on Figures 3 & 4, from innermost to outermost.
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mapping short reads to a reference sequence. To illus-
trate this feature, the simulated reads from Figure 3B
were mapped to the E. coli O157:H7 Sakai genome
[GenBank:BA000007] using BWA and the genome cov-
erage from resulting SAM files was calculated and visua-
lised by BRIG. The resulting image is shown in Figure
4A, where the complete E. coli O157:H7 Sakai genome
is used as the central reference sequence with the read
mapping graphs as rings. These results are broadly com-
parable to a standard BLAST comparison between
O157:H7 Sakai and the original complete genome
sequences (Figure 4B). Notably, as a member of a differ-
ent genus, the genome of Citrobacter rodentium is more
divergent from the O157:H7 Sakai genome than the
other E. coli genomes shown and could not be mapped
accurately (Figure 4A). This illustrates that the read
mapping utility should be restricted to the analysis of
strains from the same species, with BLAST being the
preferable option for more distant comparisons.
Discussion
There are already a number of resources that produce
circular representations of prokaryote genomes; each
with their own unique features and advantages. Table 3
shows a comparison between the major features of
BRIG and other GUI or internet based applications that
produce circular images for prokaryote genomes. Of
these resources, CGView Server [13], GeneWiz Browser
[14] and DNAPlotter [12] bear the most resemblance to
BRIG.
BRIG presents a solution to visualising prokaryote
genome comparisons for a large number of genomes.
Unlike DNAPlotter, BRIG does not show a preview of
the image as the user edits it and only produces an
image after the user has specified all of their settings.
This is a common drawback of other genome compari-
son applications, including Circos [15], GeneWiz Brow-
ser and CGView Server. To address this, image
templates are available in BRIG to help first time users
to gauge appropriate settings for image aesthetics and
scaling. Furthermore, the ability to save template files at
any point during a BRIG session enables users to return
to previous versions and modify images as needed.
Unlike BRIG, similar tools generally limit the number
of genome comparisons that can be shown on a single
image and they do not offer the option to collate multi-
ple sequences into a single lane (Table 2). These draw-
backs prevent the use of these resources in large-scale
genome comparisons that are increasingly necessary as
the number of publicly available genome sequences
increase. BRIG has been designed with the task of draft
genome analysis in mind. GeneWiz Browser, like BRIG,
supports mapping and visualising short read sequences
Figure 4 Using BRIG to map unassembled sequence reads
against a complete genome reference. BRIG images showing
genomic regions shared by E. coli O157:H7 str. Sakai and related
bacteria. The reference sequence is E. coli O157:H7 str. Sakai
[GenBank: BA000007] with individual rings representing 10 genomes
(Table 3). A. Rings show depth of coverage from unassembled,
simulated 100 base-pair Illumina reads mapped onto the E. coli
O157:H7 str. Sakai genome using the BWA [24] read-mapping
application. Graph height in each ring is proportional to the
number of reads mapping at each nucleotide position in the
reference genome from 0 to 30× coverage. Regions with a genome
coverage greater than 30× are shown as solid blue bands. B. For
comparison, rings show BLASTn comparisons between the same
genome sequences used in panel A (Table 3) against the E. coli
O157:H7 str. Sakai genome. Long tick marks on the outer and inner
circumference of the ring indicate increments of 500 kilobase-pairs
and short tick marks indicate 100 kilobase-pairs. E. coli O157:H7 str.
Sakai prophage and prophage-like (Sp & SpLE) regions are
annotated in black and blue, respectively, using co-ordinates taken
from Hayashi et al. [20].
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onto a reference genome; however, it does not explicitly
support easy visualisation of contig boundaries within a
reference sequence.
Standard BRIG comparisons rely on BLAST, so an
understanding of BLAST parameters and behaviours is
required in order to produce informative images. A
common pitfall for first time users is the low-complexity
filters, which is active by default in BLAST. These filters
mask repetitive and low complexity sequences that
could cause spurious low-scoring matches when search-
ing large datasets. In BRIG, filtering often results in
short (~30 base pairs long) blank regions spanning all
query sequences, which may be misinterpreted as
unique regions in the reference genome. Filtering can be
turned off in the BLAST options field in BRIG. In addi-
tion, BLAST comparisons will often produce overlap-
ping hits, which are difficult to visualise on a static flat
image. To address this, BRIG was implemented to sort
BLAST results so that the highest scoring hits are
drawn last by CGView and displayed on top of other
lower-scoring matches. As a result, high scoring
matches are prominent over low scoring ones.
BRIG is actively maintained with a manual that
includes step-by-step tutorials and sample data provid-
ing walk-throughs of all the major features. In future we
plan to develop support for genome comparisons gener-
ated by programs like MUMmer [23] and for BRIG to
calculate the co-ordinates of major regions of difference
between genomes ‘on-the-fly’ for use in downstream
analyses.
Conclusions
Here we report the development of the BLAST Ring
Image Generator (BRIG), a user-friendly desktop
application for comparing and visualising prokaryote
genomes using BLAST. BRIG is highly versatile; it can
visualise information derived from draft genome data,
including contig boundaries, read coverage or read map-
ping data; it can display the presence, absence or varia-
tion of a user-defined set of reference sequences in
multiple datasets simultaneously, including unassembled
next-generation sequencing reads; and it can display
several types of custom graphs and annotations. All
facets of the program are customisable through an easy-
to-use graphical user interface bringing comparative
genome visualisation well within the reach of any user.
Availability and requirements
Project name: BLAST Ring Image Generator (BRIG)
Project home page: http://sourceforge.net/projects/
brig/
Operating system(s): Platform independent
Programming language: Java
Other requirements: Java 1.6 or greater
Licence: GNU GPLv3
Any restrictions to use by non-academics: None.
Acknowledgements and Funding
The authors would like to Kirstin Hanks-Thomson, Nathan Bachmann,
Makrina Totsika and Mark Schembri for their feedback in testing and
development. This work was supported by a grant from the Australian
National Health and Medical Research Council (511224). SAB is the recipient
of an Australian Research Council Australian Research Fellowship
(DP0881347).
Authors’ contributions
NFA developed and implemented the BRIG application, and helped to draft
the manuscript. NKP and NLBZ participated in the design and coordination
of the study, and helped to draft the manuscript. SAB conceived the study,
participated in its design and coordination, and helped to draft the
manuscript. All authors read and approved the final manuscript.
Table 3 GUI and internet-based applications that produce circular comparison images for prokaryote genomes
BRIG DNAPlotter
[12]
CGView
Server[13]
GeneWiz
Browser[14]
Microbial Genome
Viewer[10]
Genome
Projector[11]
Supports custom annotationsXXXXX
Search and load annotations from existing files (e.
g. Genbank, EMBL)
XX
Allows users to use their own genome dataXXXXX
Use Multi-FASTA as reference sequencesXX
Internally handles multiple genome comparisons XX1
X1
Provides percentage identity and e-value filtering
for BLAST
XXX
Supports read mapping and visualisationXXX
Natively supports contig and scaffold visualisation X
Visualises Clusters of Orthologous Genes (COGS)
functional grouping.
XXXXX
Can produce linear imagesXXX
Shows an interactive imageXXX
1CGView Server and GeneWiz Browser only support three and seven genome comparisons on a single image, respectively.
Alikhan et al. BMC Genomics 2011, 12:402
http://www.biomedcentral.com/1471-2164/12/402
Page 8 of 10

Page 9

Received: 10 April 2011 Accepted: 8 August 2011
Published: 8 August 2011
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doi:10.1186/1471-2164-12-402
Cite this article as: Alikhan et al.: BLAST Ring Image Generator (BRIG):
simple prokaryote genome comparisons. BMC Genomics 2011 12:402.
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