=Paper=
{{Paper
|id=Vol-1741/jist2016pd_paper1
|storemode=property
|title=RIKEN MetaDatabase: A Database Platform as a Microcosm of Linked Open Data Cloud in the Life Sciences
|pdfUrl=https://ceur-ws.org/Vol-1741/jist2016pd_paper1.pdf
|volume=Vol-1741
|authors=Norio Kobayashi,Kai Lenz,Hiroshi Masuya
|dblpUrl=https://dblp.org/rec/conf/jist/KobayashiLM16a
}}
==RIKEN MetaDatabase: A Database Platform as a Microcosm of Linked Open Data Cloud in the Life Sciences==
https://ceur-ws.org/Vol-1741/jist2016pd_paper1.pdf
RIKEN MetaDatabase: a database platform as a
microcosm of linked open data cloud in the life
sciences
Norio Kobayashi1,2,3 , Kai Lenz1 , and Hiroshi Masuya2,1
1
Advanced Center for Computing and Communication (ACCC), RIKEN,
2-1 Hirosawa, Wako, Saitama, 351-0198 Japan
{norio.kobayashi, kai.lenz}@riken.jp
2
BioResource Center (BRC), RIKEN,
3-1-1, Koyadai,Tsukuba, Ibaraki, 305-0074 Japan
hmasuya@brc.riken.jp
3
RIKEN CLST-JEOL Collaboration Center, RIKEN,
6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
Abstract. Life sciences research produces numerous heterogeneous data-
sets at a rapid rate, and researchers in this field face serious difficulties in
handling and publishing such datasets. These problems occur not only in
the life sciences but also within a research institute such as RIKEN, the
largest Japanese comprehensive science institute. To address this issue,
we developed RIKEN MetaDatabase, which is a database platform built
on our private cloud infrastructure and based on the Resource Descrip-
tion Framework (RDF). The platform was released in April 2015, and
110 databases including mammal, plant, bioresource and image data-
bases with 21 ontologies have been published through this platform as of
July 2016. This presentation focuses on the user interface and biological
application examples of the RIKEN MetaDatabase.
Keywords: Semantic Web, database cloud platform, database integra-
tion, life sciences
1 Introduction
Recent developments in the life sciences produce a large number of heterogeneous
datasets that are specialised for various life sciences subfields. This makes it
difficult for researchers to discover, use and publish such datasets. To address
these difficulties, the following major tasks are required: 1) the realisation of rich
and useful data integration in a sustainable way and 2) the realisation of easy,
flexible and low-cost operation to enable researchers to perform data integration
tasks. These challenges also occur in the data management of RIKEN, Japan’s
largest comprehensive science institute, which generates large-scale life sciences
datasets in various fields and is confronted with issues regarding the promotion
of collaborative research promotion amongst different fields. This situation can
be thought of as a microcosm of the linked open data cloud in the life sciences.
To solve these issues, we developed RIKEN MetaDatabase, which is a data-
base platform based on the Resource Description Framework (RDF) that pro-
vides metadata management at low cost, systematic data integration and global
publication on the Web. This presentation provides overview of the platform,
focusing on the user interface and biological application examples.
2 Data model
Life sciences datasets, including the RIKEN databases, are often represented in
tabular form or hosted by a relational database system. To realise a simple and
user-friendly database platform, we restricted the RDF data handled by RIKEN
MetaDatabase to tabular-type database data and tree-type ontology data.
A tabular-type database data describes the RDF data wherein all RDF re-
sources are associated with an RDF class. A table is generated for each class
of subject instances of RDF triplets and described in a spreadsheet, as shown
in Fig. 1. In this spreadsheet, the second column comprises a list of instances
of class ‘Background strain’, the third column includes a list of literal values of
rdf:langString and the fourth column includes a list of ‘Taxon’ classes as instances
of owl:Class. A spreadsheet can be converted into RDF using our application.
A tree-type ontology data comprises Web Ontology Language (OWL) ontol-
ogy data, which represents the concepts and data classes of the database data
using a conceptual hierarchy.
Fig. 1. Example spreadsheet describing the RDF data of class ‘Background strain’
(http://metadb.riken.jp/db/rikenbrc_mouse/animal_0000004) in tabular form.
3 Implementation
RIKEN MetaDatabase was implemented as a database platform comprising two
components: (a) a web server providing both a graphical user interface (GUI)
and an application programming interface (API) as a SPARQL endpoint and
(b) an RDF triplet store, where each component is lightweight and performed
on a virtual machine in a private cloud known as RIKEN Cloud Service (http:
//cloudinfo.riken.jp).
The distinctive features of the GUI are its table view and card view, which
provide an intuitive RDF data view for a list of instances associated with a class
and an instance, respectively, as shown in Fig. 2.
Fig. 2. Snapshots of (A) the tabular view of class ’Habitat’ of the ‘Japan Collection
of Microorganisms (JCM) resource’ database (http://metadb.riken.jp/metadb/db/
rikenbrc_jcm_microbe) and (B) the card view of instance ‘decreased brain size in
RBRC-GD000047’ of class ’Phenotype Annotation’ of the ‘Metadata of BRC mouse
resources and phenotypes’ database (http://metadb.riken.jp/metadb/db/rikenbrc_
mouse). (i) and (ii) are reverse linked instances of the concerned instances, i.e., instances
of class ‘Habitat’ and instance ‘reference paper’, respectively.
4 Integrated datasets and biological application examples
As of July 2016, 21 public ontologies, including Gene Ontology (GO) and Semantic-
science Integrated Ontology (SIO) were selected and published as mirrors. These
ontologies refer to 110 databases, of which 59 were RIKEN’s original databases.
The remaining 51 databases are external databases that are converted from orig-
inal non-RDF databases and linked from RIKEN’s databases. In total, RIKEN
MetaDatabase comprises 148 million triplets, 797 classes, 2.94 million instances
and 1,352 properties. The original databases pertain to various research fields,
e.g., FANTOM (mammals [1]), FOX Hunting (plants [2]), Heavy-atom Database
System (proteins [3]) and Metadata of the BioResource Center (BRC) resources
(bioresources [4–6]).
More examples of datasets integrated over RIKEN and public datasets are
summarised as follows. In the ENU-induced mutations in RIKEN Mutant Mouse
Library, next-generation sequencing (NGS) metadata are described in a common
RDF scheme, which was developed by a collaboration between DNA Data Bank
of Japan (DDBJ) and RIKEN. The Japan Collection of Microorganisms (JCM)
resources (http://metadb.riken.jp/metadb/db/rikenbrc_jcm_microbe) are
described using a common RDF scheme for strains of microorganisms known
as Microbial Culture Collection Vocabulary (MCCV), which is used in Mi-
crobeDB.jp (http://microbedb.jp/). J-phenome (http://jphenome.info), a
portal of animal phenotypes, employs a unified scheme using common phenotype
ontologies such as Mammalian Phenotype Ontology (MP) and Phenotypic Qual-
ity Ontology (PATO). Furthermore, the International Mouse Phenotyping Con-
sortium (IMPC) RDF data (http://metadb.riken.jp/metadb/db/IMPC_RDF)
are provided, which enable the data integration of these phenotype data with
other datasets. For instance, to retrieve the phenotype that can be expressed
when a specific biological pathway is inactivated, a connection of the IMPC and
Reactome datasets (http://www.reactome.org) is used.
5 Conclusions
We discussed an overview of the RIKEN MetaDatabase platform, which allows
biologists to generate RDF datasets using a spreadsheet and publish them using
various intuitive views, such as table and card views, and as a SPARQL endpoint.
We have successfully realised integrated datasets, such as for microorganisms and
mouse phenotypes, both within RIKEN and external communities. The database
platform is lightweight, and multiple system instances can be launched as virtual
machines in a cloud environment in order to develop individual databases for
each research project. In the future research, we will focus on the implementation
of a practical federation amongst such system instances.
References
1. The FANTOM Consortium and the RIKEN PMI and CLST (DGT): A promoter-
level mammalian expression atlas. Nature 507: 462–470 (2014)
2. Ichikawa, T., Nakazawa, M., Kawashima, M., Iizumi, H., Kuroda, H., Kondou, Y.,
Tsuhara, Y., Suzuki, K., Ishikawa, A., Seki, M., Fujita, M., Motohashi, R., Nagata,
N., Takagi, T., Shinozaki, K., Matsui, M.: The FOX hunting system: an alternative
gain-of-function gene hunting technique. Plant J. 45: 974–985 (2006)
3. Sugahara, M., Asada, Y., Shimada, H., Taka, H., Kunishima, N.: HATODAS II:
heavy-atom database system with potentiality scoring. J. Appl. Crystallogr. 42:
540-544 (2009)
4. Yoshiki, A., Ike, F., Mekada, K., Kitaura, Y., Nakata, H., Hiraiwa, N., Mochida, K.,
Ijuin, M., Kadota, M., Murakami, A., Ogura, A., Abe, K., Moriwaki, K., Obata,
Y.: The mouse resources at the RIKEN BioResource center. Exp. Anim. 58(2):
85–96 (2009)
5. Nakamura, Y.: Bio-resource of human and animal-derived cell materials. Exp.
Anim. 59(1): 1–7 (2010)
6. Yokoyama, K.K., Murata, T., Pan, J., Nakade, K., Kishikawa, S., Ugai, H., Kimura,
M., Kujime, Y., Hirose, M., Masuzaki, S., Yamasaki, T., Kurihara, C., Okubo, M.,
Nakano, Y., Kusa, Y., Yoshikawa, A., Inabe, K., Ueno, K., Obata, Y.: Genetic
materials at the gene engineering division, RIKEN BioResource Center. Exp. Anim.
59(2): 115–124 (2010)