CyanoFactory Knowledge Base & Synthetic Biology
A Plea for Human Curated Bio-databases
Gabriel Kind, Eric Zuchantke and R
¨
obbe W
¨
unschiers
University of Applied Sciences Mittweida, Technikumplatz 17, 09648 Mittweida, Germany
Keywords:
Synthetic Biology, Big Data, Omics, Warehouses, Databases, Knowledge-base, Wisdom of the Crowd.
Abstract:
Nowadays, life science research is dominated by two conditions: interdisciplinarity and high-throughput.
The former leads to highly diverse datasets from a data type point of view while high-throughput yields
massive amounts of data. Both aspects are reflected by the byte-growth of public bio-databases and the sheer
number of specialised databases or databases of databases (i.e. data warehouses). We provide an insight to the
development of a biodata knowledge base (dubbed CyanoFactory KB) targeted to bio-engineers in the field of
synthetic biology and exemplify the need for data type specific data curation and cross-linking. CyanoFactory
KB is unique in incorporating experimental data from a broad range of scientific methods that are based on one
strain of Synechocystis sp. PCC 6803. The knowledge base can be accessed upon request via cyanofactory.hs-
mittweida.de.
1 INTRODUCTION
Nowadays, life science research is dominated by two
conditions: interdisciplinarity and high-throughput.
The former leads to highly diverse datasets from a
content point of view while high-throughput yields
massive amounts of data. Both aspects are reflected
by the byte-growth of public bio-databases and the di-
versity of specialised databases (see, e.g. the database
issues of the NAR journal). However, quite often
more data leads to less understanding. Driven by
the methodology of systems biology, a holistic view
of genetic and metabolic regulatory processes is de-
manded. One important goal is the application of
these systemic data for in silico modelling of bio-
logical processes or, ultimately, biological systems,
i.e. cells, tissues, organisms. One step towards this
goal was the successful prediction of the phenotype
from the genotype in Mycoplasma genitalium (Karr
et al., 2012). The basis to solve this challenge was a
database named WholeCell Knowledge Base (Whole-
Cell KB) (Karr et al., 2013). It contains experimental
results from over 900 publications and includes more
than 1,900 experimentally observed parameters. Im-
portantly, all data has been validated and curated by
scientists.
Another important manually curated database is
Brenda, which contains almost 1.5 million manu-
ally curated enzyme parameters (as of July 2014,
brenda-enzymes.org). In contrast, GenBank con-
tains 174,108,750 individual and 189,080,419 whole
genome shotgun sequences (as of August 2014,
ncbi.nlm.nih.gov) that are partially manually up-
loaded but not curated. In the field of cyanobacte-
ria research, CyanoBase is a well-known manually
curated genome database, including over 5200 refer-
ences (Fujisawa et al., 2014).
With the rising amount of biological data and the
increasing capabilities of computer hardware, many
attempts have been undertaken to automatically har-
vest, store, cross-link and provide biological data in
databases and databases of databases (i.e. data ware-
houses) (Triplet and Butler, 2011). We argue that au-
tomatically generated data collections are of limited
value (the common garbage-in garbage-out problem),
especially in the context of large scale biological en-
gineering as envisioned by the field of synthetic biol-
ogy. In this field, computer modelling of biological
processes builds the base to targeted (instead of trial-
and-error) genetic engineering.
With this paper we provide an insight in the devel-
opment of a biodata-warehouse (CyanoFactory KB)
targeted to bio-engineers in the field of synthetic biol-
ogy. We are in the extraordinary situation to work in
a research consortium that consists of partners from
different scientific fields (interdisciplinary) and seven
different countries (multiregional) with the unifying
goal to tinker the cyanobacterium Synechocystis sp.
237
Kind G., Zuchantke E. and Wünschiers R..
CyanoFactory Knowledge Base & Synthetic Biology - A Plea for Human Curated Bio-databases.
DOI: 10.5220/0005285802370242
In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2015), pages 237-242
ISBN: 978-989-758-070-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
PCC 6803 to produce photohydrogen. We are par-
ticularly working at the interface between experimen-
tal and computational biology, which implies differ-
ent understanding of data (Figure 1). CyanoFactory
KB, which is a massive expansion of the WholeCell
KB, shall provide a central data hub for members of
the consortium and for disseminating project data to
the research community. Thus, it shall provide ways
for an improved collaboration between all partners
within the CyanoFactory consortium. All partners
are experts in different fields from microbial biotech-
nology or metabolic modelling up to synthetic biol-
ogy. The goal of the knowledge base is to bridge
the gap between bio-engineers and bioinformaticians
by providing user friendly functionalities for working
with experimental data and for visualising and con-
textualising it in different ways. Besides experimen-
tal data, further data is obtained from other biological
databases.
Figure 1: Different ways of thinking about data.
CyanoFactory KB is continuously improving and
adjusted to new requirements of the research partners.
The warehouse is still under heavy development and
gains new features every month. It is not available
to the general public yet, this step is planned when
the codebase is more matured and more experimental
data of the partners has been integrated.
2 MATERIALS AND METHODS
CyanoFactory KB is a massive expansion of Whole-
Cell KB (Karr et al., 2013) and was further adopted
to the needs of CyanoFactory. WholeCell KB turned
out to be the best suited and matured bio-warehouse
system after intensive scouting and testing of various
known open-source systems (Table 1).
Due to different responsibilities, different tech-
nologies and data types are provided and demanded
by individual partners, e.g. mass spectrometry data,
DNA-microarray- or RNASeq-based transcription
analyses, metabolic analyses, mathematical mod-
ells, sequence based data from genetic engineer-
Table 1: Intensively tested open-source biodata-
warehouses.
Warehouse Citation
BioDWH (T
¨
opel et al., 2008)
BioMart Central Portal (Guberman et al., 2011)
BioWarehouse (Lee et al., 2006)
BioXRT (Zhang et al., 2004)
BN++ (K
¨
untzer et al., 2007)
CoryneRegNet (Baumbach, 2007)
DAWIS-M.D. (Hippe et al., 2010)
InterMine (Lyne et al., 2007)
ONDEX (Taubert et al., 2014)
Open Genome Resource (Klein et al., 2009)
PROFESS (Triplet et al., 2010)
PiPa (Arzt et al., 2011)
RAMEDIS (T
¨
opel et al., 2010)
WholeCell KB (Karr et al., 2013)
ing or climate and experimental data from outdoor
photo-bioreactor experiments. All experiments are
performed using the model organism Synechocys-
tis sp. PCC 6803 as provided from the University
of Uppsala/Sweden (Uppsala subtype). This sub-
type has been resequenced, compared to the orig-
inal sequence on GenBank and analysed for ge-
netic variations. Additional data was obtained from
other biological databases: Organism related infor-
mation from GenBank, pathway data from KEGG
(Kanehisa et al., 2014) and Boehringer Biochemical
Pathways Maps (Michal and Schomburg, 2012) and
protein-protein interactions from STRING (Frances-
chini et al., 2013) and STITCH (Kuhn et al., 2014).
Furthermore CyanoDesign provides a unique inter-
face for metabolic modelling and analyses of the effi-
ciency of enzymatic reactions via flux balance analy-
sis.
3 RESULTS
CyanoFactory KB is a productive knowledge base,
which handles all the information from our partners.
The advantage of our solution is that, besides holding
information, it provides different visualisation tech-
niques and cross-links to other data sources.
Uploading of experimental data is supported in
different formats. The warehouse provides import
functionality for FASTA, GenBank and System Biol-
ogy Markup Language (SBML). The import runs as a
background job and is automatically merged into the
current dataset upon completion. All modifications to
the knowledge base are stored as revisions, therefore
changes to all items can be retrieved and rolled back if
BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms
238
necessary. Exporting is possible in the import formats
and furthermore in machine readable XML or JSON
formats. The access to individual resources can be
restricted using permissions.
Besides the hierarchical view of the warehouse the
user can group selected data in “baskets”. A user can
create different baskets and group relevant items in
them.
The general structure of the organism is visual-
ized by using a chromosome viewer (Figure 2). The
viewer is fully interactive and provides filtering func-
tionalities. Additional metadata is displayed beneath
the genes. In our case these are SNPs obtained from
the Uppsala subtype of Synechocystis sp. PCC 6803.
When selecting a gene or SNP additional metadata for
the corresponding component is displayed.
Figure 2: Chromosome viewer showing SNPs (triangles be-
low the genes) from the Uppsala subtype of Synechocystis
sp. PCC 6803.
Due to possible mutations in Synechocystis sp.
PCC 6803 over time the strand used in experiments
by the Uppsala university was resequenced, analyzed
and the sequence modifications uploaded into the
knowledge base. The newly sequenced genome was
aligned to the reference sequence. Many genes of the
Uppsala strain contain SNPs (Table 2). It is currently
investigated whether these inflict noticeable changes
to the metabolic system of the organism.
Table 2: Mutations in chromosome of Synechocystis sp.
PCC 6803.
Chromosome
Number of SNPs 732
SNPs on Genes 511
Genes 6462
Modified Genes 1028
The metabolic processes of Synechocystis sp.
PCC 6803 and interactions of biochemical compo-
nents are visualized using the Process Description
Language of the Systems Biology Graphical Nota-
tion (SBGN) (Le Novere et al., 2009). SBGN rep-
resents the metabolic model of the organism in a way
detailed enough for biochemists and is machine read-
able, therefore supporting mathematical simulations
inside the model.
It should, however, be noted that SBGN proofed to
be confusing to the human eye. Thus, traditional visu-
alisations such as Boehringer Pathway Maps (Michal
Figure 3: Part of the metabolic model of Synechocystis sp.
PCC 6803 rendered in Systems Biology Graphical Notation
(SBGN).
and Schomburg, 2012) or KEGG maps (Kanehisa
et al., 2014) are to be preferred.
Pathway Visualisation
The enzymes and metabolites of Synechocystis sp.
PCC 6803 are displayed on different pathway maps.
Biochemical Pathways provides a detailed overview
about chemical reactions. A small excerpt with high-
lighted enzymes contained in Synechocystis sp. PCC
6803 is visualised in Figure 4.
All found enzymes and metabolites are high-
lighted on individual KEGG pathway maps (Figure
5). When an item was detected the image is filled in
green for enzymes and red for metabolites. Pathways
are cross-linked to each other.
Custom searches, independent of the organism,
are supported on all pathway maps. All search queries
can be saved for later reuse.
Figure 4: Boehringer Pathway Map.
Metabolic Modelling
Metabolic modelling is provided as part of Cyano-
Design. Flux balance analysis (FBA) is used for the
reconstruction of metabolic networks of organisms
(Figure 6). A metabolic network consists of multi-
ple enzymatic reactions with metabolites contained in
CyanoFactoryKnowledgeBase&SyntheticBiology-APleaforHumanCuratedBio-databases
239
Figure 5: KEGG Pathway Map.
a stoichiometric matrix (positive for production, neg-
ative for degradation). This network is solved us-
ing a linear solving method. The motivation behind
CyanoDesign is allowing the bio-engineer to change
the metabolic network in silico and to get a predic-
tion how the organism will behave. A modelling ap-
proach saves valuable time because it gives hints how
mutants of the organism behave, resulting in a high
amount of saved work in the lab. FBA is done using
the library PyNetMet (Gamermann et al., 2014). For
improved quality of the simulation results the addi-
tion of more advanced algorithms like “Minimization
of Metabolic Adjustment” (MOMA) is planned.
Figure 6: Metabolic model with calculated fluxes. The cal-
culated amount of flux is displayed on top of the arrows and
indicated by there thickness.
Interactions
CyanoInteraction provides visualisation of protein-
protein-interactions and protein-metabolite-interac-
tions of selected proteins and metabolites from Syne-
chocystis sp. PCC 6803 (Figure 7). The dataset used
is based on data from STRING (Franceschini et al.,
2013) and STITCH (Kuhn et al., 2014). The in-
teractions are displayed as undirected graphs. Only
the most significant interaction partners are displayed.
The significance is calculated based on the STRING
and STITCH interaction scores. This score is based
on, among others, homology, coexpression and text
mining. The visualisation is completely interactive
and adjustable to the users needs.
Figure 7: Graph displaying protein and chemical interac-
tions. Shorter edges between nodes mean higher scores.
4 CONCLUSION
With the help of the web-based CyanoFactory knowl-
edge base it is possible to concentrate information
from a single organism and its derived mutants. The
more information and the more cross-links between
these information can be established, the better is
the understanding of the selected organism. The
CyanoFactory KB is still under development. Thus,
later versions of the warehouse will see more func-
tions and experimental data.
From previous interactions between, and from ex-
periences by the partners in the research consortium
it became clear that automatically collected data sets
are too error prone. Thus, the CyanoFactory KB sets
the stage to examine the effort and value of human
curated databases and warehouses.
It is important to note the importance to put
experimental and computational results about one
particular bacterial strain under one umbrella. This
demands management decisions within a research
consortium or even a research community. The re-
search consortium CyanoFactory (cyanofactory.eu)
works with the model organism Synechocystis sp.
PCC 6803. The Kazusa strain of Synechocystis sp.
PCC 6803 was the first photosynthetic prokaryote
whose genome sequence has been determined in 1996
(Kaneko et al., 1996). Besides the sister strains PCC
(Pasteur Culture Collection), ATCC (American Type
Culture Collection) and GT (glucose tolerant), the
Kazusa strain has been derived from one original Cal-
ifornia freshwater isolate from 1971, the Berkeley
strain (Stanier et al., 1971). Recently, it has been
shown that all sister strains can be distinguished by
single nucleotide polymorphisms and indels (Ikeuchi
and Tabata, 2001; Kanesaki et al., 2012; Trautmann
et al., 2012). Furthermore, many sub- or laboratory
strains have been derived from all four strains. This
leads to experimental and computational results based
on different genetic backgrounds. Ultimately, this
BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms
240
may lead to non-comparable results. Thus, when inte-
grating data in a knowledge base, detailed information
about the experimental setup and the genetic back-
ground are necessary. Only then, valid and consistent
metabolic models can be derived.
The CyanoFactory KB takes a first step in the di-
rection of strain specific databases. This, however,
requires a high investment in man-month of skilled
personnel. Another important problem that we are
currently addressing is the way of data integration
from diverse data sources. While the systems biology
markup language (SBML) provides a good founda-
tion for data exchange, strong effort has to be invested
in data-pipeline setup. On the long run, research con-
sortia need special funding solely directed to manual
data(base) curation. In return, a sustainable and co-
herent data source for follow-up research can be es-
tablished.
ACKNOWLEDGEMENT
We want to thank Eric Frenzel for the creation of the
SBGN networks. This project has received funding
from the European Union’s Seventh Programme for
research, technological development and demonstra-
tion under grant agreement No 308518, CyanoFac-
tory.
REFERENCES
Arzt, S., Starlinger, J., Arnold, O., Kr
¨
oger, S., Jaeger, S.,
and Leser, U. (2011). Pipa: Custom integration of pro-
tein interactions and pathways. In Workshop Daten In
den Lebenswissenschaften, Berlin, Germany. Citeseer.
Baumbach, J. (2007). CoryneRegNet 4.0 A refer-
ence database for corynebacterial gene regulatory net-
works. BMC Bioinformatics, 8(1):429.
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M.,
Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork,
P., von Mering, C., and Jensen, L. J. (2013). STRING
v9.1: protein-protein interaction networks, with in-
creased coverage and integration. Nucleic Acids Res.,
41(Database issue):D808–815.
Fujisawa, T., Okamoto, S., Katayama, T., Nakao, M.,
Yoshimura, H., Kajiya-Kanegae, H., Yamamoto, S.,
Yano, C., Yanaka, Y., Maita, H., Kaneko, T., Tabata,
S., and Nakamura, Y. (2014). CyanoBase and Rhi-
zoBase: databases of manually curated annotations
for cyanobacterial and rhizobial genomes. Nucleic
Acids Research, 42(Database issue):D666–70.
Gamermann, D., Montagud, A., Infante, R. A. J., Triana,
J., de Crdoba, P. F., and Urchuegua (2014). PyNet-
Met: Python tools for efficient work with networks
and metabolic models. Computational and Mathemat-
ical Biology, 3(5):1–11.
Guberman, J. M., Ai, J., Arnaiz, O., Baran, J., Blake,
A., Baldock, R., Chelala, C., Croft, D., Cros, A.,
Cutts, R. J., Di G
´
enova, A., Forbes, S., Fujisawa, T.,
Gadaleta, E., Goodstein, D. M., Gundem, G., Hag-
garty, B., Haider, S., Hall, M., Harris, T., Haw, R., Hu,
S., Hubbard, S., Hsu, J., Iyer, V., Jones, P., Katayama,
T., Kinsella, R., Kong, L., Lawson, D., Liang, Y.,
Lopez-Bigas, N., Luo, J., Lush, M., Mason, J.,
Moreews, F., Ndegwa, N., Oakley, D., Perez-Llamas,
C., Primig, M., Rivkin, E., Rosanoff, S., Shepherd,
R., Simon, R., Skarnes, B., Smedley, D., Sperling,
L., Spooner, W., Stevenson, P., Stone, K., Teague, J.,
Wang, J., Wang, J., Whitty, B., Wong, D. T., Wong-
Erasmus, M., Yao, L., Youens-Clark, K., Yung, C.,
Zhang, J., and Kasprzyk, A. (2011). BioMart Central
Portal: an open database network for the biological
community. Database, 2011(0):bar041–bar041.
Hippe, K., Kormeier, B., T
¨
opel, T., and Janowski, S.
(2010). DAWIS-MD-A Data Warehouse System for
Metabolic Data. GI Jahrestagung.
Ikeuchi, M. and Tabata, S. (2001). Synechocystis sp. PCC
6803 - a useful tool in the study of the genetics of
cyanobacteria. Photosynthesis research., 70(1):73–
83.
Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furu-
michi, M., and Tanabe, M. (2014). Data, informa-
tion, knowledge and principle: back to metabolism in
KEGG. Nucleic Acids Res., 42(Database issue):199–
205.
Kaneko, T., Sato, S., Kotani, H., Tanaka, A., Asamizu, E.,
Nakamura, Y., Miyajima, N., Hirosawa, M., Sugiura,
M., Sasamoto, S., Kimura, T., Hosouchi, T., Matsuno,
A., Muraki, A., Nakazaki, N., Naruo, K., Okumura,
S., Shimpo, S., Takeuchi, C., Wada, T., Watanabe,
A., Yamada, M., Yasuda, M., and Tabata, S. (1996).
Sequence analysis of the genome of the unicellular
cyanobacterium Synechocystis sp. strain PCC6803. II.
Sequence determination of the entire genome and as-
signment of potential protein-coding regions. DNA
Research: An International Journal for Rapid Pub-
lication of Reports on Genes and Genomes, 3(3):109–
136.
Kanesaki, Y., Shiwa, Y., Tajima, N., Suzuki, M., Watanabe,
S., Sato, N., Ikeuchi, M., and Yoshikawa, H. (2012).
Identification of substrain-specific mutations by mas-
sively parallel whole-genome resequencing of Syne-
chocystis sp. PCC 6803. DNA Research: An Inter-
national Journal for Rapid Publication of Reports on
Genes and Genomes, 19(1):67–79.
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Arora, A., and
Covert, M. W. (2013). WholeCellKB: model organ-
ism databases for comprehensive whole-cell models.
Nucleic Acids Res., 41(Database issue):D787–792.
Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V.,
Jacobs, J. M., Bolival Jr., B., Assad-Garcia, N., Glass,
J. I., and Covert, M. W. (2012). A Whole-Cell Com-
putational Model Predicts Phenotype from Genotype.
Trends in Genetics, 150(2):389–401.
Klein, J., M
¨
unch, R., Biegler, I., Haddad, I., Retter, I., and
Jahn, D. (2009). Strepto-DB, a database for compara-
tive genomics of group A (GAS) and B (GBS) strepto-
CyanoFactoryKnowledgeBase&SyntheticBiology-APleaforHumanCuratedBio-databases
241
cocci, implemented with the novel database platform
’Open Genome Resource’ (OGeR). Nucleic Acids Re-
search, 37(Database issue):D494–8.
Kuhn, M., Szklarczyk, D., Pletscher-Frankild, S., Blicher,
T. H., von Mering, C., Jensen, L. J., and Bork, P.
(2014). STITCH 4: integration of protein-chemical
interactions with user data. Nucleic Acids Res.,
42(Database issue):D401–407.
K
¨
untzer, J., Backes, C., Blum, T., Gerasch, A., Kaufmann,
M., Kohlbacher, O., and Lenhof, H.-P. (2007). BNDB
- the Biochemical Network Database. BMC Bioinfor-
matics, 8(1):367.
Le Novere, N., Hucka, M., Mi, H., Moodie, S., Schreiber,
F., Sorokin, A., Demir, E., Wegner, K., Aladjem,
M. I., Wimalaratne, S. M., Bergman, F. T., Gauges,
R., Ghazal, P., Kawaji, H., Li, L., Matsuoka, Y., Vil-
leger, A., Boyd, S. E., Calzone, L., Courtot, M., Do-
grusoz, U., Freeman, T. C., Funahashi, A., Ghosh, S.,
Jouraku, A., Kim, S., Kolpakov, F., Luna, A., Sahle,
S., Schmidt, E., Watterson, S., Wu, G., Goryanin, I.,
Kell, D. B., Sander, C., Sauro, H., Snoep, J. L., Kohn,
K., and Kitano, H. (2009). The Systems Biology
Graphical Notation. Nat. Biotechnol., 27(8):735–741.
Lee, T. J., Pouliot, Y., Wagner, V., Gupta, P., Stringer-
Calvert, D. W. J., Tenenbaum, J. D., and Karp, P. D.
(2006). BioWarehouse: a bioinformatics database
warehouse toolkit. BMC Bioinformatics, 7(1):170.
Lyne, R., Smith, R., Rutherford, K., Wakeling, M., Var-
ley, A., Guillier, F., Janssens, H., Ji, W., Mclaren, P.,
North, P., Rana, D., Riley, T., Sullivan, J., Watkins,
X., Woodbridge, M., Lilley, K., Russell, S., Ash-
burner, M., Mizuguchi, K., and Micklem, G. (2007).
FlyMine: an integrated database for Drosophila and
Anopheles genomics. Genome Biology, 8(7):R129.
Michal, G. and Schomburg, D., editors (2012). Biochemical
Pathways. An Atlas of Biochemistry and Molecular
Biology. Wiley.
Stanier, R. Y., Kunisawa, R., Mandel, M., and Cohen-
Bazire, G. (1971). Purification and properties of uni-
cellular blue-green algae (order Chroococcales). Bac-
teriological reviews, 35(2):171–205.
Taubert, J., Hassani-Pak, K., Castells-Brooke, N., and
Rawlings, C. J. (2014). Ondex Web: web-based
visualization and exploration of heterogeneous bio-
logical networks. Bioinformatics (Oxford, England),
30(7):1034–1035.
T
¨
opel, T., Kormeier, B., Klassen, A., and Hofest
¨
adt, R.
(2008). BioDWH: a data warehouse kit for life sci-
ence data integration. Journal of Integrative Bioinfor-
matics, 5(2).
T
¨
opel, T., Scheible, D., Trefz, F., and Hofest
¨
adt, R. (2010).
RAMEDIS: a comprehensive information system
for variations and corresponding phenotypes of rare
metabolic diseases. Human mutation, 31(1):E1081–8.
Trautmann, D., Voss, B., Wilde, A., Al-Babili, S., and Hess,
W. R. (2012). Microevolution in cyanobacteria: re-
sequencing a motile substrain of Synechocystis sp.
PCC 6803. DNA Research: An International Jour-
nal for Rapid Publication of Reports on Genes and
Genomes, 19(6):435–448.
Triplet, T. and Butler, G. (2011). Systems Biology Ware-
housing: Challenges and Strategies toward Effective
Data Integration. DBKDA 2011 : The Third Interna-
tional Conference on Advances in Databases, Knowl-
edge, and Data Applications, pages 34–40.
Triplet, T., Shortridge, M. D., Griep, M. A., Stark, J. L.,
Powers, R., and Revesz, P. (2010). PROFESS: a
PROtein function, evolution, structure and sequence
database. Database, 2010(0):baq011–baq011.
Zhang, J., Duggan, G. E., Khaja, R., and Scherer, S. W.
(2004). Bioxrt: a novel platform for developing online
biological databases based on the cross-referenced ta-
bles model. In 3rd Canadian Working Conference on
Computational Biology, Markham, Canada.
BIOINFORMATICS2015-InternationalConferenceonBioinformaticsModels,MethodsandAlgorithms
242