IslandViewer
the inclusion of other methods that had lower specificity (some
as low as 38% precision), which would result in a large number
of false predictions in IslandViewer. Finally, none of the methods
included in IslandViewer had been previously available as a web
resource; therefore, giving new user-friendly access to three different
GI prediction methods.
2.3 IslandViewer interface
IslandViewer allows the viewing of all GI predictions for the
above predictors through a single integrated interface. Predictions
are precomputed for all published GIs and are updated on a
monthly basis, while users with newly sequenced unpublished
genomes can submit their genome for analysis and receive an email
notification when finished. These user-submitted genomes are not
viewable by other IslandViewer users and are accessible for at least
1 month. IslandPick automatically selects comparison genomes for
use using default distance parameters, but since researchers may
have particular insights into a particular species, they can choose
to run IslandPick with their own manually selected comparison
genomes and have the option of being notified by email when the
results are available.
Once the genome of interest is selected it is presented as a circular
genome image with each predicted GI highlighted (different colours
for different tools in the IslandViewer) and is also available as a high-
resolution image suitable for publication. In addition to the predicted
GIs for each tool, IslandViewer highlights any GIs that have been
predicted by two or more methods. The annotations for genes within
each GI can be quickly viewed by hovering over the GI of interest
within the image. Clicking on an island jumps to the corresponding
row in a table below the genome image and gives information such
as GI coordinates, links to tables showing genes and annotations
within the GI region, links to external genome viewers at NCBI and
joint genome institute (JGI), and links to IslandPath to allow further
examination of GI-related features in the genome of choice. GI
predictions may be downloaded in various formats including Excel,
tab-delimited, comma-delimited, Fasta and Genbank (allowing easy
input into the genome browser and annotation tool Artemis). In
addition, we provide a ‘Resources’ page that links to other GI
prediction methods that are not included in IslandViewer, but may be
useful to users who wish to investigate different prediction methods.
All datasets and source code are available for download under a GNU
GPL license.
3 CONCLUDING COMMENTS
GI identification is becoming a first critical step in the char-
acterization of a bacterial genome, due to the growing appreciation
for the role of GIs in important adaptations of interest. Recent
research has therefore focused on developing new computational
methods for their prediction. However, these methods tend to use
different approaches and identify different features of GIs. The result
is that the most accurate methods each have high precision, but
low recall, leading to slightly different regions being predicted.
Previously, researchers could either pick a single method or try to
manually integrate the results from multiple methods themselves.
In addition, many of these tools did not have their own web interfaces
and often required that the user download and run the program on
their computer. IslandViewer alleviates these concerns by providing
a web interface for three accurate GI prediction methods that were
not previously available through a web interface. By precomputing
GI datasets for all completed genomes and providing a single
submission process for new user genomes, we allow researchers
access to a user-friendly resource that can be used as the first step in
GI analysis of bacterial genomes. We would expect that researchers
would manually inspect any GI predictions shown in IslandViewer to
determine their validity and make more accurate predictions of their
boundaries. IslandViewer helps aid further analysis of GI predictions
by providing data in various formats that can be used in other
bioinformatic tools such as Artemis, and by providing numerous
links to other GI resources. IslandViewer should be a useful resource
for any researcher studying GIs and microbial genomes.
ACKNOWLEDGEMENTS
M.G.I.L. also holds a MSFHR scholarship, while F.S.L.B. is a
MSFHR Senior Scholar and CIHR New Investigator. Infrastructure
support was also provided by Genome Canada/GenomeBC, SFU
CTEF and IBM.
Funding: Canadian Institutes of Health Research and Michael Smith
Foundation for Health Research (for SFU/UBC Bioinformatics
Training Program).
Conflict of Interest: none declared.
REFERENCES
Chiapello,H. et al. (2005) Systematic determination of the mosaic structure of bacterial
genomes: species backbone versus strain-specific loops. BMC Bioinformatics, 6,
171.
Dobrindt,U. et al. (2004) Genomic islands in pathogenic and environmental
microorganisms. Nat. Rev. Microbiol., 2, 414–424.
Hacker,J. and Kaper,J.B. (2000) Pathogenicity islands and the evolution of microbes.
Ann. Rev. Microbiol., 54, 641–679.
Hsiao,W.W. et al. (2005) Evidence of a large novel gene pool associated with prokaryotic
genomic islands. PLoS Genet., 1, e62.
Karlin,S. (2001) Detecting anomalous gene clusters and pathogenicity islands in diverse
bacterial genomes. Trends Microbiol., 9, 335–343.
Langille,M.G.I. et al. (2008) Evaluation of genomic island predictors using a
comparative genomics approach. BMC Bioinformatics, 9, 329.
Mantri,Y. and Williams,K.P. (2004) Islander: a database of integrative islands in
prokaryotic genomes, the associated integrases and their DNA site specificities.
Nucleic Acids Res., 32, D55–D58.
Ou,H.Y. et al. (2006) A novel strategy for the identification of genomic islands by
comparative analysis of the contents and contexts of tRNA sites in closely related
bacteria. Nucleic Acids Res., 34, e3.
Ou,H.Y. et al. (2007) MobilomeFINDER: web-based tools for in silico and experimental
discovery of bacterial genomic islands. Nucleic Acids Res., 35, W97–W104.
Rajan,I. et al. (2007) Identification of compositionally distinct regions in genomes using
the centroid method. Bioinformatics, 23, 2672–2677.
Tu,Q. and Ding,D. (2003) Detecting pathogenicity islands and anomalous gene clusters
by iterative discriminant analysis. FEMS Microbiol. Lett., 221, 269–275.
Vernikos,G.S. and Parkhill,J. (2006) Interpolated variable order motifs for identification
of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.
Bioinformatics, 22, 2196–2203.
Vernikos,G.S. and Parkhill,J. (2008) Resolving the structural features of genomic
islands: a machine learning approach. Genome Res., 18, 331–342.
Waack,S. et al. (2006) Score-based prediction of genomic islands in prokaryotic
genomes using hidden Markov models. BMC Bioinformatics, 7, 142.
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