Nucleic Acids Research 2005 33(Web Server Issue):W324-W325; doi:10.1093/nar/gki401
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BIOVERSE: enhancements to the framework for structural, functional and contextual modeling of proteins and proteomes
Jason McDermott,
Michal Guerquin,
Zach Frazier,
Aaron N. Chang and
Ram Samudrala*
Department of Microbiology, University of Washington Seattle, WA, USA
*To whom correspondence should be addressed. Tel: +1 206 732 6122; Fax: +1 206 732 6055; Email: ram{at}compbio.washington.edu
Received February 14, 2005. Revised March 16, 2005. Accepted March 16, 2005.
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ABSTRACT
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We have made a number of enhancements to the previously described
Bioverse web server and computational biology framework (
http://bioverse.compbio.washington.edu).
In this update, we provide an overview of the new features available
that include: (i) expansion of the number of organisms represented
in the Bioverse and addition of new data sources and novel prediction
techniques not available elsewhere, including network-based
annotation; (ii) reengineering the database backend and supporting
code resulting in significant speed, search and ease-of use
improvements; and (iii) creation of a stateful and dynamic web
application frontend to improve interface speed and usability.
Integrated Java-based applications also allow dynamic visualization
of real and predicted protein interaction networks.
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INTRODUCTION
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We described the web-based interface to the Bioverse framework
previously (
1), which provides objected-oriented representations
of biological components and relationships between them, along
with associated confidence values, at the single molecule as
well as the genomic/proteomic levels. Since then, a number of
improvements, detailed below, have been made to the Bioverse
database and web interface to increase its utility to the life
sciences community.
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DATA IMPROVEMENTS
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The number of organisms represented in the Bioverse has grown
to >50, including >400 000 protein sequences. Network-based
functional annotation has been performed for all genomes, providing
novel annotations for
4000 proteins without existing annotations.
This method is based on the integration of functions from neighboring
proteins in real or predicted protein interaction networks and
has been previously shown to provide accurate predictions (
2–
5).
Other new features include Superfamily (
6) and CATH (
7) sequence
to structural classification and evolutionary information content
for all proteins. Confidence values for all predictions are
dynamic and are constantly being refined against experimental
data. Detailed explanations of the derivation of confidence
values for each type of prediction are provided on the web server.
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FRAMEWORK IMPROVEMENTS
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A relational database backend implemented in MySQL and an object-relational
mapping layer with an XMLRPC interface have been implemented
to facilitate data interchange internally and with other databases.
These modifications result in better speed, stability and accessibility
compared with the previous implementation.
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WEB INTERFACE IMPROVEMENTS
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The web server component of the Bioverse is now a stateful and
dynamic web application, which provides a more intuitive interface.
Web server operations, such as performing a search, dynamically
update information in the current browser page using client-directed
server requests and content updates. This decreases the time
required to render complicated data representations and allows
emulation of familiar behaviors of desktop applications. Users
can now customize the behavior of the interface using an options
page and compile and annotate lists of proteins with a user
history manager. The range of search options has also been significantly
enhanced and more detailed information about each matched protein
is given. A much broader range of protein characteristics is
searchable and searches for proteins with particular relationships,
e.g. evolutionary similarity and predicted functional interactions,
are now possible.
To allow dynamic visualization of predicted and experimental protein interaction networks, we developed a Java-based interaction viewer (8) that was capable of only handling networks of limited size. We have developed a second version of this viewer, called the Integrator, that communicates with the Bioverse object layer and enables exploration of arbitrarily large networks (A. N. Chang, Z. Frazier, M. Guerquin, J. McDermott and R. Samudrala, manuscript submitted). In addition, the Integrator can be used to upload user-supplied data, such as gene expression data, and with our predicted networks, visually search for interacting clusters of proteins corresponding to differentially expressed genes.
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CONCLUSION
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The Bioverse has been used by biologists to annotate and analyze
large-scale genome sequencing projects (
9,
10). The new features
described here enhance the value of the resource by providing
a rich feature set, intuitive interface and tight integration
with visual and algorithmic tools for exploring single molecules
and interactomes.
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ACKNOWLEDGEMENTS
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This work was supported in part by a Searle Scholar Award, NIH
Grant GM068152 and NSF Grant DBI-0217241 (to R.S.), and the
University of Washington's Advanced Technology Initiative in
Infectious Diseases. Funding to pay the Open Access publication
charges for this article was provided by Searle Scholar Award
(to R.S.).
Conflict of interest statement. None declared.
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REFERENCES
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