=Paper=
{{Paper
|id=Vol-2180/paper-02
|storemode=property
|title=NGBO: The Introduction of -omics Data to Biobanking Ontology
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-02.pdf
|volume=Vol-2180
|authors=Dalia Al-Ghamdi,Damion Dooley,Emma Griffiths,Gurinder Pal Gosal,William Hsiao
|dblpUrl=https://dblp.org/rec/conf/semweb/AlghamdiDGGH18
}}
==NGBO: The Introduction of -omics Data to Biobanking Ontology==
https://ceur-ws.org/Vol-2180/paper-02.pdf
NGBO: The Introduction of -omics Data to Biobanking Ontology
Dalia Alghamdi1, 4, Damion M. Dooley 1, Gurinder Gosal1, Emma J. Griffiths2,
William W.L. Hsiao1-3
1
University of British Columbia, Vancouver BC V6T 1Z4, Canada.
2
Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
3
BC Centre for Disease Control Public Health Laboratory, Vancouver, BC V5Z 4R4, Canada.
4
King Fahad Medical City, Riyadh 59046, Saudi Arabia.
Dalia.Alghamdi@bccdc.ca
Abstract. A biobank contains a collection of biological samples, along
with associated medical information of sample donors, which can be
used for different types of studies. Given the wealth of information that
can be derived from stored information and biological materials, there
is a pressing need for structuring biobank data for more computer-
amenable analyses. The utility of first generation biobanks was
originally evaluated simply based on the number of samples that they
contained. Currently, the value of biobank data lies in how it can linked
with other molecular and clinical data (“-omics data”), to provide new
insights into health and disease. Linking data has thus far, however,
proven challenging due to unstructured and incompatible data types.
Here, we describe the development of a Next-Generation biobanking
ontology (NGBO) (https://github.com/Dalalghamdi/NGBO) that is
capable of supporting both Biospecimen processing, management,
storage and retrieval infrastructure, and acting as a knowledge hub for
an integrated clinical and translational research ecosystem integrating –
omics data. NGBO harmonizes the instrumentation and procedures used
to prepare and process specimens, and also covers terminology used to
describe computational biology algorithms, analytical tools, electronic-
communication protocols, in vitro assays. Laboratories, investigators,
and other biobanks would also benefit from the knowledge contained in
the ontology, by the means of using NGBO a biobank data catalogue
that can be used to map any existing unstructured data.
Keywords: Ontology, Biobank, Next generation sequencing, data harmonization,
data integration.
A biobank consists of various biological samples linked to the medical information of
sample donors which can be used for translational and biomedical research.
Biological samples can include organs, tissues, cells and body fluids [1]. The stored
specimens enable researchers to save time and resources of collecting and processing
new samples needed in there projects, thereby, they improve research outcomes,
promising for more effective diagnosis and treatments of patients who suffer from
common or rare diseases [2]. The collection of human tissues for the purpose of
biomedical research began centuries ago, but has since undergone dramatic change
due to technology advancements in storage techniques, sample information retrieval,
as well analysis of specimen material. As such, the informatics needs of modern
biobanks are far more complex than past repositories that often captured only the date
or location of a sample collection. Besides sample metadata, modern biobanks cover
the storage and management of more complex data generated from high-throughput
biological studies such as proteomic, genomics and other –omics studies [3].
International and national collaborations can improve the value of biobanks, but this
requires harmonizing the data fields and values across biobanking applications. There
have been several efforts to achieve collaboration and data sharing among various
national biobanks, for instance, the public population project in genomics (P3G)
(http://www.p3g.org) has previously tackled building biobanking resources as well as
data cataloguing and harmonization for data integration [4]. Still, biobanks remain
heterogeneous when it comes to their design, usage, size and types of the samples. It
is possible to link the samples to data records from expansive epidemiological
collections and family histories. However, it is laborious to manually harmonize the
terms across different biobanks. Furthermore, if data harmonization is conducted
individually, inconsistencies often arise. A key development in facilitating data
standardization is the application of ontology, a semantic web technology [5].
Semantic Web is the best practices and sets of standards used to share data and
meanings (semantics) of data over the web. The formal and machine-readable
definitions and axioms made it is possible to come up with automated querying
systems to facilitate faster, easier, and more accurate ways to share and reuse data [6].
Semantic Web OWL ontology is a popular technology choice for representing
terminology and data structure relationships. If one is to use ontology, it becomes
possible to establish vocabularies necessary to model a problem or activity domain. In
the model, there are objects and concepts contained in specific areas and relations
describing how they are related [7]. Ontologies play a role in promoting the
realization of Semantic Web.
Brochhausen et al. proposed the ontology for biobanking (OBIB) in 2016. The OBIB
was created through the merging of two biobank technologies including the Biobank
Ontology (BO) and the Ontologized Minimum Information About Biobank Data
Sharing (OMIABIS) [8]. BO and OMIABIS focus on specimen description and
biobanking administration respectively. The biomedical and biological research has
progressed to a level where the quantity and the types of samples kept are no longer
used in measuring the prowess of biobanks. Instead, measurements are based on the
extent that the samples and metadata are used. Biobanks fall into the category of
infrastructure used in research. Thus, their aim is to support scientific processes.
The integration of more knowledge domains into the biobanking ontology is
necessary for advancing the use of biobanks. We aim to integrate –omics data, with
the creation of Next-Generation Biobanking Ontology (NGBO) to deal with various
scientific research and personalized medicine requirements. To incorporate -omics
knowledge, we model the processes and entities related to diagnostic molecular
pathology procedures, including sample handling, phenotype characterization,
computational biology algorithms and analytical tools, in vitro assays, electronic
communication protocols and data coding. In addition to the technical data
provenance, sample data provenance such as patient phenotypic information, along
with the genetic data will provide the biobank users sufficient data and knowledge to
characterize the functional and pathogenic significance of genetic variants [9].
NGBO is being built based on the Open Biological and Biomedical Ontologies
(OBO) foundry principles. For example, one of the OBO foundry principles is the re-
use of existing ontology to prevent re-inventing the wheel and creating multiple
representations of the same term. OWL (W3C Web Ontology Language) is used to
provide the means for data sharing and reusing between different resources in the
form of semantic application. NGBO will provide standard identifiers for classes and
relations within biobanking domain as well as definitions for all the vocabularies in
NGBO in human and machine-readable formats. Consider the class ‘input data’ as an
example, the definition of input data that can be read by human is “computer file that
has specific format and contains data that serve as input to a device or program”.
However, it could be defined using OWL language as:
'is about' some entity and 'has format' exactly 1'file format'
This is one of the expressions possible for the class ‘input data’, which states that it is
about an entity and has only one file format from file format subclasses. As shown in
figure 1, NGBO re-use many terms from existing ontologies such as Bio-Assay
Ontology (BAO) and the OBO edition of the National Cancer Institute Thesaurus
(NCIT) ontology. NGBO depends primarily on the (is-a) relation between classes and
subclasses, thereby providing a hierarchy of classes that also enables inheritance of
the properties. For example, a concept “planned process” (OBI:0000011) in the
Ontology for Biomedical Investigations (OBI) is defined as “a processual entity that
realizes a plan which is the concretization of a plan specification”. Therefore, all sub-
classes of planned process must inherit the definition of the process. In addition to (is-
a), pre-existing relations such as (is_version_of) is used when needed. Protégé
(version 5.2.0) is used to build NGBO that is compatible with the Basic Formal
Ontology (BFO), a small upper level ontology mostly used to support information
retrieval, analysis and integration in biomedical and biological domains [10]. Figure 1
shows an example of selection of NGBO classes and their sub-classes.
In conclusion, by building NGBO, a next generation biobanking ontology, we are
providing a semantics infrastructure to support externalized biomedical collaborative
research by harmonizing biospecimens with their molecular makeup. It provides a
framework for reusing clear consistent terminology (classes) with their relationships
and the metadata that describe the intended meaning of these classes and
relationships.
Figure 1: The selection of main NGBO classes and their sub-classes. For readability
reasons, the leftmost classes are missing in the figure.
References:
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Research Involving Biobanks: Differing Terms and Norms Present Serious Barriers
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Geary, and Marble Arch International Working Group on Biobanking for Biomedical
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