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BMC Bioinformatics
Open Access
Database
BNDB – The Biochemical Network Database
Jan Küntzer*
1
, Christina Backes
1
, Torsten Blum
2
, Andreas Gerasch
2
,
Michael Kaufmann
2
, Oliver Kohlbacher
2
and Hans-Peter Lenhof
1
Address:
1
Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany and
2
Center for Bioinformatics/Wilhelm Schickard
Institute for Computer Science, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
Email: Jan Küntzer* - kuentzer@bioinf.uni-sb.de; Christina Backes - cbackes@bioinf.uni-sb.de; Torsten Blum - blum@informatik.uni-
tuebingen.de; Andreas Gerasch - gerasch@informatik.uni-tuebingen.de; Michael Kaufmann - mk@informatik.uni-tuebingen.de;
Oliver Kohlbacher - oliver.kohlbacher@uni-tuebingen.de; Hans-Peter Lenhof - len@bioinf.uni-sb.de
* Corresponding author
Abstract
Background: Technological advances in high-throughput techniques and efficient data acquisition
methods have resulted in a massive amount of life science data. The data is stored in numerous
databases that have been established over the last decades and are essential resources for scientists
nowadays. However, the diversity of the databases and the underlying data models make it difficult
to combine this information for solving complex problems in systems biology. Currently,
researchers typically have to browse several, often highly focused, databases to obtain the required
information. Hence, there is a pressing need for more efficient systems for integrating, analyzing,
and interpreting these data. The standardization and virtual consolidation of the databases is a
major challenge resulting in a unified access to a variety of data sources.
Description: We present the Biochemical Network Database (BNDB), a powerful relational
database platform, allowing a complete semantic integration of an extensive collection of external
databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which
is powerful enough to model most known biochemical processes and at the same time easily
extensible to be adapted to new biological concepts. Besides a web interface for the search and
curation of the data, a Java-based viewer (BiNA) provides a powerful platform-independent
visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an
interactive visualization and navigation of BNDB.
Conclusion: BNDB allows a simple, unified access to a variety of external data sources. Its tight
integration with the biochemical network library BN++ offers the possibility for import,
integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org.
Background
The development of high-throughput technologies has
generated an extensive quantity of -omics data over the
last decades. Despite the technological progress, improve-
ments in the application area, e.g. in drug discovery, have
failed to keep pace with increased research and develop-
ment spending, as demonstrated by Nightingale et al. [1].
One of the main reasons for this discrepancy is the
increasing number of highly focused databases differing
in both the data models and the interfaces [2]. The data-
Published: 2 October 2007
BMC Bioinformatics 2007, 8:367 doi:10.1186/1471-2105-8-367
Received: 2 July 2007
Accepted: 2 October 2007
This article is available from: http://www.biomedcentral.com/1471-2105/8/367
© 2007 Küntzer 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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bases are often independently developed, have a substan-
tial overlap and are not well standardized. The absence of
a standardization limits the usability of these databases
and leads to a demand for a unified access to the data [3].
Hence, a large number of systems addressing this problem
with diffierent approaches have been developed. These
approaches can be classified by their architecture into
three main categories [4]: navigators, mediators, and ware-
houses. The first category, navigators, is based on the idea
of a navigational or link-based integration of several data
sources. Such a portal normally does not integrate the
data itself, but provides the user with pages navigating to
external data sources. Well-established examples of portal
systems are SRS [5], BioNavigator [6], and Entrez [7]. A
mediator gives access to distributed data by reformulating
the queries of the user at runtime into queries on external
data sources. However, availability and efficiency are
major drawbacks of such solutions. Examples for this cat-
egory are Discovery Link [8], TAMBIS [9], and BioMedia-
tor [10]. Systems of the third category, warehouses,
require a complete semantic integration of the data from
various external data sources into a single local database
via an integrative data model. Such approaches allow for
an efficient execution of queries since they avoid typical
problems of the other methods such as network bottle-
necks, short-time unavailability of the external data
sources, and changes in the external data sources. How-
ever, data warehouses usually require complex data mod-
els and regular updates of the integrated data sources, in
order to avoid the possibility of returning outdated query
results. BNDB is a representative of this category, as are
other systems like GUS [11], ONDEX [12], cPath [13], and
Biozon [14].
Construction and content
Based on an object-oriented data model, called BioCore,
we developed and implemented BNDB, an SQL data
warehouse system that integrates data sets from external
and internal data sources via importers. The BioCore
model allows not only for modelling of nearly all cur-
rently known biochemical processes, but also for includ-
ing new biological concepts with little effort [15,16]. The
architecture of the system is presented in Fig. 1.
The BNDB is implemented as a relational database using
MySQL [17]. We decided to chose a relational database
management system over an object-oriented system, since
relational DBMS are well-established and the current de-
facto standard. This guarantees a high portability of the
biochemical network database allowing a user to create a
local version of the BNDB on a wide range of platforms.
Therefore, we created an object-relational mapping of the
BioCore model onto a relational database management
system, using only SQL2 [18] compatible statements. This
restriction allows the usage of any relational or object-
relational database management system like DB2, Oracle,
or PostgreSQL. The database consists of more than 240
tables representing all BioCore classes [see Additional file
1]. Additionally, BNDB includes tables for the user and
rights management, as well as for the reconstruction of the
object-oriented structure of the database. The schema for
BNDB (Fig. 2) is available on the website.
In the current state, BNDB represents a comprehensive
collection of biological data integrated from the following
data sources:
• Sequence databases: SwissProt [19], RefSeq [20]
• Pathway databases: KEGG [21], BioCyc [22], TransPath
[23]
• Protein interaction databases: DIP [24], MINT [25],
IntAct [26], HPRD [27]
• Transcription factor databases: TransFac [28]
For the horizontal data integration [29,30] of these data
we implemented comprehensive merging heuristics. The
key concept behind these methods is the integration of
complementary data sources and the elimination of
redundancy in the data. We use two fundamental
approaches for the merging of the data:
(1) object matching based on unambiguous external iden-
tifiers and (2) structural matching based on identical
object relations.
The first approach relies on the existence and correctness
of selected standardized IDs in the imported databases
(see Fig. 3). Each object in the database is linked with a
variety of different external data source identifiers, like
RefSeq, GeneId, SwissProt, Unigene, InterPro, etc. We
only use those identifiers, that unambiguously identify
the corresponding biochemical objects. For the merging
we collect all unambiguous database identifiers in BNDB.
For each of these IDs we check if they are connected to
more than one object instance of the same type. If this is
the case, we merge these instances into one single
instance. All attributes of these instances are merged and
multiple occurrences of these attributes are removed.
External database IDs not describing unique objects, but
rather clusters of objects (e.g. Unigene, InterPro, etc.) are
not considered in the merging process. For objects with-
out external identifiers, like biochemical events (e.g. met-
abolic reactions), we use the second approach based on
structural matching of object relations. We define two
events to be equal if they are of the same event type and
contain the same participants occurring in the same role,
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whereas events, participants, and role are the major build-
ing blocks of the BioCore schema [15].
The merging process itself consists of several steps: In an
initial step, we merge most of the database objects by their
identifiers and remove redundancy in their attributes
through the first approach. Then, in the second step we
collect and merge all equivalent events in BNDB through
the second structural approach.
A simplified example for merging genes using the first
approach, is presented in Fig. 4. Four instances of the
human BAD gene with different external database identi-
fiers and names are merged by unambiguous identifiers.
Two instances are connected with the same NCBI GI-ID
and therefore identified to be equal. These instances are
merged into one single instance connected with the
merged attributes. The remaining instances are all associ-
ated with the same NCBI-GeneID. Thus, our algorithm
merges these three instances into one single gene instance,
which is linked with the merged information of all four
former instances. All merging heuristics were imple-
mented using the Biochemical Network Library BN++
[16] and the source code is available on our website.
Utility and discussion
For accessing BNDB we offer three different ways: a web
interface, a network visualizer, and a programming inter-
face.
Web interface
An intuitive web client browser enables querying and
browsing BNDB. The user can search by name, descrip-
tion, or publication for participants, events and pathways.
The user query is converted internally into an SQL query.
For the standard search the user does not need to know
any information about the internal structure of BNDB or
its underlying data model BioCore. In addition, for more
advanced users the web interface gives the possibility to
perform direct SQL queries. The retrieved results are pre-
sented text- and link-based in a user-friendly way. Hyper-
links to external data sources are provided for additional
information whenever external database identifiers are
connected with the object (for an example see Fig. 5).
Depending on the rights of the user, the system allows for
a curation of the database by editing the displayed results.
Furthermore, we included a functionality for adding new
information in a convenient way, such that the user does
not need to know the internal structure of the database.
ArchitectureFigure 1
Architecture. Architecture of the BNDB data warehouse.
BioCore
implements
implements
C++
library
BGL
yFiles
Java
library
us
e
s
u
s
e
s
us
e
s
us
e
s
BN++ DB
(MySQL)
BN++
Framework
BiNA
SQL
SQL
Plugin Plugin Plugin
MINT
MINT
T
T
ra
T
T
n
sP
P
a
th
th
T
T
ra
T
T
nsF
F
a
c
BioCyc
RefSeq
KEGG
I
n
tA
c
t
ItAt
HPRD
DIP
implements
contains
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The BNDB interface guides the user through the adding
process and warns if necessary information is missing.
Network visualization
We provide a stand-alone Java application called BiNA for
querying and analyzing the data contained in BNDB and
for visualizing biological networks. The tool allows for
visualizing metabolic and regulatory networks with
sophisticated graph layout algorithms. Besides the direct
visualization, BiNA also provides a mapping engine to
analyze arbitrary data sets in the context of networks. This
allows to map numerical biological data, e.g. mRNA
expression data, onto graph attributes like node/edge
color or size. The visualization of two data sets at the same
time makes it easy to compare different data sets and iden-
tify correlations. The color scheme and the edge thickness
used for the drawing can be freely defined by the user and
is shown as a legend in the visualization view. Addition-
ally, the mapped data values can be changed easily to
interactively explore time-series expression data. In the
metabolic view, the edges labeled with the catalyzing
enzymes can be colored by the expression values of the
enzyme-coding genes. In the regulatory view, the map-
ping plugin allows for coloring the nodes representing
proteins, genes or protein families, whereas the protein
families are colored by the values of all contained mem-
bers. Fig. 6 gives an example for a metabolic view in BiNA
with mapped expression data.
The graph and visualization capabilities of our applica-
tion are comparable to that of visualization systems such
as Cytoscape [31], PathSys [32], VisANT [33], or commer-
cial tools such as MetaDrug [34] or PathwayStudio [35].
Additionally, BiNA offers a multifunctional workbench,
which is easily extensible. The viewer itself can be
regarded as a collection of modules that depend on each
other. The hierarchical plugin system automatically
resolves dependencies between plugins through a well-
defined and very powerful interface. The plugin structure
of BiNA allows for an easy integration of own analysis
routines. Currently, several plugins exists, e.g. for map-
ping gene expression data onto the network, pathway
search algorithms, or exporting pathways into SBML and
BioPAX.
Programming interface
BNDB is fully integrated with the Biochemical Network
Library BN++ [15,16] providing a sophisticated program-
Simplified DDL Diagram of BNDBFigure 2
Simplified DDL Diagram of BNDB. The simplified structure of the database schema.
<<index>> + id_index(id)
<<PK>> - id: int
- reverse: tinyint = NULL
Event
<<index>> + id_index(id)
<<index>> + event_index(event)
<<index>> + participant_index(participant)
<<PK>> - id: int
- event: int
<<not null>> - participant: int
Role
<<index>> + id_index(id)
<<PK>> - id: int
- classname_id: int
- accession_nr: int
- timestamp: timestamp
Thing
<<index>> + id_index(id)
<<PK>> - id: int
Participant
{columns=id}
{columns=event}
{columns=id}
{columns=participant}
<<key>>
<<key>>
{columns=id}
<<key>>
<<key>>
{columns=id}
<<key>>
<<key>>
<<key>>
<<key>>
<<key>>
<<key>>
{columns=id}
{columns=id}
{columns=id}
{columns=id}
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ming interface. Hence, arbitrary data like a complete path-
way can be serialized and deserialized from C++ by a
single line of code. This speeds up the development proc-
ess of analysis routines, since a programmer can concen-
trate on the implementation of the algorithm. In addition,
the BN++ software framework offers a comprehensive col-
lection of implemented analysis routines.
The C++ programming interface provides a convenient,
but very flexible way to merge the data. With a few lines of
code it is possible to construct a customized local meta-
database containing only that data the user requires.
Conclusion
With BNDB we present a data warehouse system integrat-
ing a large number of different biological databases.
Access to these data is provided through a generic web
interface allowing for adding, editing, and searching the
data in BNDB. In addition, we have developed BiNA, a
powerful and extensible tool for visualizing biochemical
networks directly from BNDB. Through the BN++ soft-
ware framework BNDB is easily accessible for software
developers and can be integrated into tailor-made appli-
cations and customized to user needs. All tools and meth-
ods described herein, BNDB, BiNA, the source code, the
web interface to BNDB, and the underlying data model
are freely available from our website.
Database UniverseFigure 3
Database Universe. The nodes represent external databases labeled by their name. An edge is draw from A to B meaning
that database A knows the ids of database B. In addition, the database are grouped by the contained data: the protein interac-
tion dbs are yellow, enzyme dbs are green, the protein and sequence dbs are blue, pathway dbs are olive, and the orange nodes
are domain dbs.
MINT BioGrid
IntAct
DIP
KEGG
PfamProDomProsite
Transpath
HPRD
Ensembl
Transfac
BRENDA
InterPro
ENZYME
MetaCyc
GenBank
PubMed
PDB
HUGO
OMIM
PIR
UniGene
UniProt
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A major advantage of BNDB is its underlying data model
BioCore. This comprehensive and extensible object model
can represent most currently known biochemical entities
and processes. Therefore, BNDB is able to store a huge
variety of different biochemical data. Researchers can eas-
ily adapt it to their own needs and build customized data-
bases. Another benefit is the full integration of BNDB into
the visualizer BiNA. Other systems often present only a
database with an analysis tool (e.g. Biozon), or a database
with a web interface (e.g. Entrez). For the graphical repre-
sentation of the networks, many of these systems use
standard visualizer (e.g. Cytoscape). However, we think
that the full integration of an own visualization tool facil-
itates the visualization and presentation of the stored
data.
We have developed several applications based on BNDB
that show the usefulness of the approach, e.g. an efficient
gene set analysis tool, GeneTrail [36], which enables the
user to identify enriched functional categories in protein
or gene sets. GeneTrail has been successfully applied to
detect a molecular target of the antimicrobial metabolite
kendomycin [37].
In summary, BNDB is a comprehensive database system,
which makes it not only possible to retrieve the combined
information of integrated data sources in an easy way, but
can also be customized and extended to meet the needs of
different users.
Availability and requirements
Project name: BNDB;
Project home page: http://www.bndb.org
;
Operating system(s): Platform independent;
Programming language: Java; Other requirements: Java
1.6.0 or higher;
Object matching based mergingFigure 4
Object matching based merging. Simplified example for merging genes using the object matching based approach. In this
case we have four instances of the human BAD gene, which we merge using the GI identifier and the GeneID. The resulting
gene contains all merged names and identifiers.
GeneID: 572
Unigene: Hs.370254
GI: 10835069
Gene
(BAD)
KEGG: hsa:572
GeneID: 572
Gene
(BAD)
OMIM: 603167
GeneID: 572
Gene
(BCL2-antagonist of cell death)
RefSeqID: NM_004322.2
GI: 10835069
Gene
GeneID: 572
Unigene: Hs.370254
GI: 10835069
RefSeqID: NM_004322.2
Gene
(BAD)
OMIM: 603167
GeneID: 572
Unigene: Hs.370254
GI: 10835069
RefSeqID: NM_004322.2
KEGG: hsa:572
Gene
(BAD)
(BCL2-antagonist of cell death)
merge
merge
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Searching using the web interfaceFigure 5
Searching using the web interface. Search for glycolysis in the web interface.
Visualization using BiNAFigure 6
Visualization using BiNA. Visualization of the glycolysis using the metabolic graph layout. The blue boxes represent meta-
bolic compounds. If there is an enzymatic reaction occurring between compounds, a directed edge labeled with the enzyme
class catalyzing the reaction is drawn. The edge labels are colored by the expression value of the enzyme-coding genes. In this
example we use expression values for the normal control of the GDS820 data set from the GEO database.
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Licence: GNU GPL;
BNDB is freely accessible at http://www.bndb.org
. The
current versions of BN++ and BiNA are distributed under
the GNU GPL license and available from the website
http://www.bnplusplus.org/downloads
.
Abbreviations
BN++ Biochemical Network Library
BiNA Biological Network Analysis
DBMS Database Management System
NCBI National Center for Biotechnology Information
BGL Boost Graph Library
SQL Standard Query Language
SBML Systems Biology Markup Language
BioPAX Biological Pathways Exchange
GEO Gene Expression Omnibus database
Authors' contributions
AG programmed the network visualization tool. MK pro-
vided specialist knowledge on network visualization. JK,
CB, and TB were involved in implementing one or more
importers. JK, OK, and HPL contributed to the system
design of BN++ and to the design of its data model. MK,
OK and HPL supervised the project. All authors read and
approved the final manuscript.
Additional material
Acknowledgements
The project was funded by the Deutsche Forschungsgemeinschaft (BIZ4:1-
4) and the Klaus Tschira Foundation.
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Additional file 1
DDL Diagram of BNDB. The general structure of the database schema.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-8-367-S1.jpeg]
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