=Paper=
{{Paper
|id=Vol-1709/BMDID_2016_paper_1
|storemode=property
|title=Semantic Research Platform for Model Organisms
|pdfUrl=https://ceur-ws.org/Vol-1709/BMDID_2016_paper_1.pdf
|volume=Vol-1709
|authors=Maxime Déraspe,Kalpana Karra,Gail Binkley,Julie Sullivan,Gos Micklem,Jacques Corbeil,J. Michael Cherry,Michel Dumontier
|dblpUrl=https://dblp.org/rec/conf/semweb/DeraspeKBSMCCD16
}}
==Semantic Research Platform for Model Organisms==
https://ceur-ws.org/Vol-1709/BMDID_2016_paper_1.pdf
Semantic Research Platform for Model
Organism Data
Maxime Déraspe1,5 , Kalpana Karra4 , Gail Binkley4 , Julie Sullivan2,3 , Gos
Micklem2,3 , Jacques Corbeil1 , J. Michael Cherry4 , and Michel Dumontier5
1
Department of Molecular Medicine, Université Laval, Québec, Canada
2
Cambridge Systems Biology Centre, University of Cambridge, Cambridge, United
Kingdom
3
Department of Genetics, University of Cambridge, Cambridge, United Kingdom
4
Department of Genetics, Stanford University, Stanford, United States
5
Stanford Center for Biomedical Informatics Research, Stanford University,
Stanford, United States
Abstract. Model organisms such as budding yeast provide a common
platform to interrogate and understand cellular and physiological pro-
cesses. Knowledge about model organisms, whether generated during the
course of scientific investigations, or extracted from published articles,
are integrated and made available by model organism databases (MODs)
such as the Saccharomyces Genome Database (SGD). SGD uses Inter-
Mine to enable powerful, data-driven bioinformatic analyses and most
of the other MODs also expose their data through InterMine so provid-
ing a standard platform for MOD data exploration and mining. However
bioinformatic analyses also require access to a significantly broader set
of biomedical data, which today can be found in structured form in
the emerging network of Linked Open Data (LOD). The MODs have
expended substantial effort over many years on human curation of the
literature and if these gold-standard data alongside other MOD data
could be provisioned as FAIR (Findable, Accessible, Interoperable, and
Reusable), then scientists could leverage a greater amount of interoper-
able data in knowledge discovery.
Keywords: linked data, semantic web platform, model organisms, biomed-
ical research
1 Introduction
Model organisms are a set of reference species that the research community
use to study basic biology, biodiversity, and help us understand human biol-
ogy. From fundamental to applied sciences, these guinea-pigs have proved their
usefulness in building systems biology, understanding complex phenotypes, un-
covering novel biological mechanisms, discovering new drug targets, testing new
drugs and studying human diseases. Knowledge about model organisms is cap-
tured in Model Organism Databases (MODs) and includes ontologies such as
the Gene Ontology (GO) [1], Sequence Ontology (SO) [2], Human Phenotype
Ontology (HPO) [3] and Disease Ontology (DO) [4]. Through the InterMOD
project [5] the various MODs are working towards standardizing access to their
data through adoption of the InterMine platform [6], a popular system, with
over 25 available endpoints. It covers the most widely studied model organisms,
such as budding yeast, fruit fly, zebrafish, rat, nematode, mouse and Arabidopsis
as well as human. Given that MODs rely considerably on open databases and
that the biological data provider community (EBI, REACTOME, ENSEMBL,
NCBI, DDBJ) has increased its adoption of the Resource Description Frame-
work (RDF), we initiated an effort to provide model organism data as 5-star
linked data6 so as to integrate these into the wider network of Linked Open
Data (LOD). We describe our efforts to develop a novel resource, the Model
Organism Linked Database (MOLD7 ), which uses Semantic Web technologies to
make the knowledge of six model organisms (budding yeast, fruit fly, zebrafish,
rat, mouse, human) available from their respective InterMine endpoints in a
FAIR (Findable, Accessible, Interoperable, and Reusable) [7] manner.
2 From MOD to MOLD
In this section, we present the methodologies used to convert the model organism
data from InterMine data warehouses into RDF and their integration with other
biological LOD.
2.1 RDFization of MOD
InterMine [8] is a model-driven data warehouse system based on PostgreSQL that
provides a client API in five programming languages for access to InterMine data.
The client API is a graph-based query format that inherits some of its semantics
and terminology from SQL. The combination of the API and the object model8
allows the user to fetch the content of an InterMine endpoint. We built a script,
the InterMine-RDFizer9 , to make use of these two components to download and
process the database content of six MODs: YeastMine [9], ZebrafishMine [10],
FlyMine [11], RatMine [12], MouseMine [13] and HumanMine10 . The data flow
of the script is illustrated in Figure 1. As it uses the object model specific to each
InterMine database, InterMine-RDFizer is flexible enough to be used with any
InterMine installation. It can be launched via a command line interface and was
used to convert all of the above six MODs. The first step is to query, download,
and save all the table content into tab-delimited (TSV) files. There are two dif-
ferent types of tables saved by the script: one that contains information about
the resources and another that represents the relations between the tables. The
schema of the PostgreSQL database in InterMine is object oriented and loosely
6
https://www.w3.org/DesignIssues/LinkedData.html
7
http://mo-ld.org
8
The object model can be retrieved in JSON or XML from each InterMine endpoint.
9
https://github.com/mo-ld/intermine-rdfizer
10
http://humanmine.org
Fig. 1. Intermine-RDFizer data flow
coupled. The reported number of tables range from 89 (MouseMine) to 122 (Rat-
Mine) and the number of table relationships from 146 to 223. The script offers the
possibility to maintain the data in its original loosely coupled manner, but the
default option merges the information for the same resource, i.e. an entity with
the same primary key in the SQL database. To maintain flexibility and avoid
the need to manually specify hundreds of predicates, InterMine-RDFizer makes
no assumptions about each database’s vocabulary, and uses the generic prefix
. However, all the object literals are typed
according to their SQL table column name and their database name. Therefore,
the user has the ability to extensively query the endpoint with external ontolo-
gies and aggregate inter-database object types. For example, each InterMine
endpoint has an object type for authors (:yeastmine Author, :flymine Author,
etc.), but one can construct a SPARQL query (as shown in Query 1.1.) for the
aggregation of the six MODs authors while using the Dublin Core11 , FOAF12
and Schema.org13 vocabularies. The statistics of the six MOLD graphs are shown
in Figure 2. Consistent with the fly being one of the most commonly used multi-
cellular invertebrate model organisms, 366m triples were derived from FlyMine.
Following the fly, in triples, are the human (HumanMine 304m) and the mouse
(MouseMine 254m), the two most-studied vertebrate organisms, and in descend-
ing order RatMine (92m), YeastMine (83m) and ZebrafishMine (63m). The data
11
http://dublincore.org/
12
http://xmlns.com/foaf/spec/
13
http://schema.org/
from these six MODs together comprise 1.16B triples, 192m distinct subjects,
192m distinct entities, 188m distinct objects, 56m literals, 1081 types, and 977
properties.
Fig. 2. Basic metrics of the 6 MODs graphs.
PREFIX rdf:
PREFIX rdfs:
PREFIX owl:
PREFIX dc:
PREFIX foaf:
PREFIX mold:
PREFIX mold_voc:
PREFIX schema:
CONSTRUCT {
?s dc:contributor ?author .
?author foaf:name ?name .
?author rdf:type ?author_type, schema:Person
}
WHERE {
?subject mold_voc:hasAuthor ?author .
?author rdfs:label ?name .
?author rdf:type ?author_type
} LIMIT 100
Query 1.1. SPARQL query with external ontologies
2.2 Linking of MODs
The InterMine-RDFizer also allows the user to create cross-references with other
databases and ontologies. It is the only feature of the script that requires an
educated user input. The user needs to provide a CSV file, which is then used
by the script to map the external cross-references of the database with other
linked data endpoints and ontologies. The script also validates each link with the
targeted URI by making a simple ASK query14 over the linked entity. It creates
14
ask { ?predicate ?object}
two different outputs, one RDF/N-TRIPLES file with all the putative links and
a second one with only the validated links. Bio2RDF ([14], [15], [16]) is one of the
broader LOD network for life sciences that can assigns node identifiers for over
2000 datasets. With such a large network of supported databases, it was a natural
choice to start connecting our graph to a larger network of biological data.
We used prefixcommons15 to identify the right prefix for each cross-reference’s
datasource, which was then used to generate a proper Bio2RDF HTTP identifier.
All database cross-references and ontologies contained in an InterMine instance
were collected for further linking. Of the 92 external datasources used in the
MODs, 38 (41%) were also present in Bio2RDF. Thus, linking the MODs with
Bio2RDF added value to the original MODs. It is important to notice that even
the links that are not currently validated were also incorporated into MOLD
datasets. Because of the standardization of Bio2RDF URIs creation, we are
guaranteed that once the targeted database is converted on their side, the links
will bind. Figure 3 shows the connections between the 6 MOLD datasets and the
38 datasets. Only the Gene Ontology and the PantherDB are found in all the six
MODs. Databases focused on genomics (genes, proteins, etc.) show the highest
number of links. PantherDB (2,048,167), RefSeq (936,568), UniGene (399,637)
and NCBI gene (352,585) databases have most links overall. Nonetheless, other
important biological aspects such as phenotypes (HGNC and OMIM databases)
and diseases (human disease ontology (DO)) are present. ”Cumulatively between
the six MODs, 57% (3,382,672 of 5,923,399 links) of all their cross-references
were existing in Bio2RDF. However, the connectivity of the MODs could be
greatly increased if a few additional databases were made available as part of the
network of linked open data. For instance, the entire conversion of PantherDB
would increase the total connectivity of the six MODs by 22%, obtaining a global
coverage of 79%.
3 Semantic platform for MOLD
This section presents the platform of the Model Organism Linked Database. We
also outline our efforts to improve the deployment and reuse of the linked data
platform using Docker.
3.1 MOLD Architecture
The MOLD Web application was built with simplicity in mind aiming to reuse
state-of-the-art Semantic Web software. It comes with all the functionalities a
user would expect to find in a Semantic Web platform: support for querying,
browsing and exploring the data. Figure 4 shows the technologies used in the
MOLD architecture. First, the SPARQL query editor and results viewer, respec-
tively YASQE and YASR, are two components of YASGUI [17], a very user-
friendly and commonly used editor. The editor is also customizable and comes
with interesting features out of the box, such as auto-completion of predicates16
15
http://prefixcommons.org/
16
Auto-completion is based on http://prefix.cc
Fig. 3. MOLD links to other LOD
and multiple options for viewing the results. It is configured to exclusively serve
MOLD, but it still enables federated queries with external endpoints as we will
show below in the use case section. We provide query examples to guide the
user in their first steps with MOLD. For the browsing component of MOLD, we
opted for the Virtuoso17 faceted browser. Virtuoso has proven useful in a great
number of projects, such as Bio2RDF, DBpedia [18] and the EBI-RDF platform
[19]. Moreover, it offers a SPARQL interface for MOLD and provides full text
search capabilities. Another practical tool to explore a graph is Relfinder [20].
The goal of Relfinder is, given resource literals, to find paths in the graph be-
tween them. We integrated Relfinder into MOLD and configured some examples
in the software that works with our graphs. A genomics example could be to
17
https://github.com/openlink/virtuoso-opensource
find a three way relationship between mouse, a specific gene annotation and hu-
man. Relfinder would then find the genes annotated for both of the organisms.
The last piece of the MOLD Web application is the REST API. To adhere to
best practice in API descriptions, we used the OpenAPIs18 specification and the
Swagger-UI19 . Our implementation currently supports five different commands:
search, describe, inlinks, outlinks and sparql, that can be called via HTTP GET.
The describe command is used to describe a resource identified by a URI. Spec-
ified via an option, describe can return either a long (with all the links) or short
(attributes only) description. The two link (in and out) commands can be used
to find other resources that the targeted URI connects with. The sparql instruc-
tion, as its name implies, provides a SPARQL query call that can also be sent via
an HTTP POST, if needs be. Other components of the MOLD interface include
a quick search and an interactive network of database connectivity that can be
found in the about section.
Fig. 4. MOLD Web technologies
3.2 MOLD in the Cloud
To ease deployment of the MOLD infrastructure in the cloud, we built Docker
images for the Virtuoso triple store, the MOLD web application and the MOLD
API. The images are publicly hosted in the docker hub registry20 , along with
documentation to allow users to launch their own MOLD containers. The code is
available on GitHub21 , licensed under the MIT license22 , and includes InterMine-
RDFizer, the Web application, the API, and the different docker configuration
files. A Google group 23 has been created to allow for discussions about MODs
and Linked Data. These resources will provide a de facto place to share common
use cases and best practice.
18
https://openapis.org/specification
19
http://swagger.io/swagger-ui/
20
https://hub.docker.com/u/mold/
21
https://github.com/mo-ld
22
https://opensource.org/licenses/MIT
23
https://groups.google.com/forum/#!forum/mo-ld
4 Pan-Organism Analysis with MOLD
One of the key advantages of linked data is the use of a standardized language
and access protocols to break down data silos and improve interoperability. To
demonstrate the value of our model organism linked data platform, we have
pursued two relevant use cases involving queries across the model organisms.
The first use case focuses on examining the set of orthologous genes between
two or more species. Orthologous genes are genes from different species that
share a common function and whose genetic lineage matches the species tree. To
do this, we constructed a query (Query 1.2) to count the number of orthogolous
genes between human and yeast using PantherDB, a database of evolutionary
relationships. While PantherDB24 is not part of Bio2RDF or the network of
Linked Open data, the graph-like nature of the representation of RDF triples
allow us to find common PantherDB identifiers linked to by the mouse and
human genes. The initial two-species query can be expanded to other species
using a UNION clause. The results for all the organism-organism associations
are reported in Table 1. Surprisingly, the largest number of uniquely identified
orthologous genes were found in the zebrafish. The two organisms that shared
the most entities were zebrafish and human, which we wouldn’t expect to be the
closest one, in evolutionary terms. Notice that the InterMine instances could be
biased due to their representation in PantherDB. Yet this example proved the
ease with which we can build cross-datasets statistics.
PREFIX mine_vocab:
SELECT (COUNT (DISTINCT ?pantherOrtholog) as ?Count)
WHERE {
GRAPH {
?shuman skos:exactMatch ?pantherOrtholog .
?shuman mine_vocab:hasDataSource ?datasource .
?datasource rdfs:label ?dslabel .
FILTER (lcase(str(?dslabel)) = "panther") }
GRAPH {
?syeast skos:exactMatch ?pantherOrtholog . }
}
Query 1.2. SPARQL query for PantherDB orthologous genes between the yeast and
human
Model Organism Yeast Zebrafish Fly Rat Mouse Human
Yeast 2151 1842 785 788 261 1543
Zebrafish 1842 4862 1604 1869 526 3419
Fly 785 1604 2426 721 199 2411
Rat 788 1869 721 2483 26 1509
Mouse 261 526 199 26 796 409
Human 1543 3419 2411 1509 409 5024
Table 1. Shared orthologous genes MOLD from PantherDB
24
http://www.pantherdb.org/
The second use case involves a pan-organism analysis to find genes with a
specific function. Query 1.3 aims to find extrinsic components of a cell membrane
(GO:0019898), a term that is specified in the Gene Ontology. In a nutshell, the
query identifies reactions from the KEGG database for mice, zebrafish, and yeast
genes annotated with the specified GO term. To do so, the federated query asks
the Bio2RDF SPARQL endpoint to find the Enzyme Classification (EC) number
contained in MOLD and fetches the reaction activity from KEGG. An interesting
extension of the query would be to ask for the biological pathways associated with
the resulting enzymes from KEGG, but this addition was omitted for brevity. In
the context of drug development research, this kind of query could be useful to
explore potential drug targets from gene annotations, or to evaluate drug safety
with pathway analysis.
PREFIX mine_vocab:
PREFIX b2f_go:
PREFIX b2f_keyvoc:
PREFIX void:
PREFIX skos:
PREFIX rdfs:
SELECT ?gene_entity ?kegg ?kegg_rx_label
WHERE {
?oTerm skos:exactMatch b2f_go:0019898 .
?oTerm mine_vocab:hasOntologyAnnotation ?gene_annotation .
?gene_annotation mine_vocab:hasBioEntity ?gene_entity .
?gene_entity mine_vocab:hasCrossReference ?xref .
?xref mine_vocab:hasDataSource ?ds .
?xref skos:exactMatch ?bio2rdf_ec .
FILTER (?ds = ||
?ds = ||
?ds = )
SERVICE {
?kegg b2f_keyvoc:x-ec ?bio2rdf_ec .
?kegg b2f_keyvoc:reaction ?kegg_rx .
?kegg_rx rdfs:label ?kegg_rx_label .
}
}
Query 1.3. Federated SPARQL query to gather enzyme reaction for GO annotated
genes
5 Conclusion
Our work creates a new and sustainable avenue by which model organism databases
that use InterMine can be exposed as Linked Data. While our efforts focused
on only 6 of the MODs, many more could also be exposed in a similar fash-
ion. Our analysis of the network of linked data revealed the resources that are
unique and/or shared by the MODs, and we demonstrate the utility of our trans-
formation through pan-MOD queries. We use a common InterMine vocabulary
to increase the interoperability of the data produced, and demonstrate how we
can use SPARQL construct queries to expose these data with other vocabularies
such as schema.org. Structuring model organism data for bioinformatics research
is not new ([16], [21]). However, our approach of simultaneously engaging the
MOD community and using W3C standards to expose data in a manner that
allows others to reproduce and extend our work yields a concrete milestone in
generating Linked Data similar to other institutional efforts ([19], [22]). The soft-
ware and data in this project are open source and available to the community,
thus offering additional support towards the reproducibility of scientific research.
Our current work is not without limitations. First, data available from a MOD
website may differ from that of the InterMine instances, because MODs do not
necessarily rely on InterMine as their primary store. In fact, some MODs, such
as SGD, selectively move data into InterMine from a relational database, thereby
yielding different results. Second, our approach does not attempt to structured
data in a manner that has been promoted by the community. For instance, the
FALDO[23] vocabulary has been put forward as a standard for describing the
location of genomic features. As we continue to develop our approach, we will
strive to include better integration of formal ontologies, including SIO[24], or
the work on genotype-phenotype integration that is ongoing at the the Monarch
Initiative25 . We will also enhance our work by conducting a user experience eval-
uation of the MOLD platform and by collecting more use cases from the MOD
community.
6 Acknowledgments
This work was supported by NIH/NHGRI U41HG001315 (M. Cherry, K Karra,
G Binkley, J Sullivan) and supplement 3U41HG001315-21S1 (M. Dumontier, M
Déraspe), NIH/NHGRI U41HG002659 (supplement subcontract to G.Micklem),
the Wellcome Trust grant 099133 (G.Micklem), and J. Corbeil acknowledges
the Canada Research Chair in Medical Genomics. The content is solely the
responsibility of the authors and does not necessarily represent the official views
of any of the funding bodies.
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