=Paper=
{{Paper
|id=Vol-1747/IP18_ICBO2016
|storemode=property
|title=The ImmPort Antibody Ontology
|pdfUrl=https://ceur-ws.org/Vol-1747/IP18_ICBO2016.pdf
|volume=Vol-1747
|authors=William Duncan,Travis Allen,Jonathan Bona,Olivia Helfer,Barry Smith,Alan Ruttenberg,Alexander D. Diehl
|dblpUrl=https://dblp.org/rec/conf/icbo/DuncanABHSRD16
}}
==The ImmPort Antibody Ontology ==
https://ceur-ws.org/Vol-1747/IP18_ICBO2016.pdf
The ImmPort Antibody Ontology
William Duncan1, Travis Allen1,2, Jonathan Bona3, Olivia Helfer1, Barry Smith1,2,3,
Alan Ruttenberg4, Alexander D. Diehl1,3,5
1
NYS Center of Excellence in Bioinformatics and Life Sciences, 2Department of Philosophy,
3
Department of Biomedical Informatics, 4Oral Diagnostics Sciences, 5Department of Neurology
University at Buffalo
Buffalo, NY, USA
addiehl@buffalo.edu
The resulting antibody registry was transformed into the
I. INTRODUCTION AnitO ontology using the Reagent Ontology (ReO) as a
Monoclonal antibodies are essential biomedical research paradigm for the representation of monoclonal antibody
and clinical reagents that are produced by companies and reagents [3]. Monoclonal antibodies are classified via isotype
research laboratories. The NIAID Immunology Database and and species of origin and are formally related to their protein
Analysis Portal (ImmPort) is a sustainable data warehouse for targets via the recognizes relation. For example, monoclonal
data generated by NIAID, DAIT and DMID funded studies antibody clone HI100 recognizes some ‘receptor-type tyrosine-
designed to allow long-term archiving and re-use of protein phosphatase C isoform CD45RA’. We supplemented
immunological data [1]. A variety of immunological data in the information in AntiO by creating classes for entries in the
ImmPort is generated using techniques that rely upon NIF Antibody Registry [4] that represent products that contain
monoclonal antibody reagents, including flow cytometry, particular monoclonal antibody clones. These classes are types
immunofluorescence, and ELISA. In order to facilitate of ‘monoclonal antibody offering’ in our ontology and are
querying, integration, and reuse of these data, standardized linked to clone name classes via has_part relations. We have
terminology for describing monoclonal antibody reagents and also mined and standardized additional information from the
their targets needs to be used for annotating data submitted to NIF Antibody Registry that is associated with particular
ImmPort. monoclonal antibody offering classes, including information
about product vendors, catalog numbers, conjugations
A major problem with monoclonal antibody-associated data (fluorchromes, biotin, etc.) of antibody products, antibody
is that data producers typically report antibody clones or target species specificity, and experimental usage.
markers using non-standardized terminology:
AntiO is built in an automated fashion using scripts that
• CD3 vs. CD3e (protein names) combine information about monoclonal antibodies and their
• HIT3e vs. UCHT1 (antibody clones for CD3e) targets found in curated spreadsheets with information text-
mined from relevant NIF Antibody Registry entries to create a
• 550367 vs. 300401 (catalog numbers for anti-CD3e base set of OWL2 modular ontologies that are imported into
antibody reagents) the AntiO ontology (see Figure 1) along with import files for
In order to address this problem, we have created the ReO and Protein Ontology terms. Additional terms from the
ImmPort Antibody Ontology (AntiO) to provide a source of Ontology for Biomedical Investigations [5], the BioAssay
standardized names for monoclonal antibodies and their protein Ontology [6], the Molecular Interactions Ontology [7], and the
targets for use by ImmPort investigators and the scientific NCBI Taxonomy [8] are included as MIREOT’ed terms as
community in general, and to provide robust querying for well [9]. The resulting combined ontology is viewable and
monoclonal antibody reagents via a variety of criteria. queryable in Protégé 5 [10], and is loaded into a publicly
available RDF triple store for SPARQL queries.
II. METHODS
III. RESULTS
We curated monoclonal antibody-protein target
relationships by identifying names and information about AntiO contains 941 monoclonal antibodies of common use
monoclonal antibodies based on published papers, data in immunology experiments, and represents about 30,000
submissions to ImmPort, and commercial monoclonal products monoclonal antibody products from 80 vendors based on
for immunology research such as the BD Lyoplate products. information derived from the NIF Antibody Registry. We have
We selected standardized monoclonal antibody names (clone included the NIF ‘AB_XXXXXX’ identifiers as part of our
names) and curated information about the protein targets of the monoclonal antibody offering labels
antibodies using Protein Ontology and UniProt identifiers [2]. The AntiO triple store is based on OWLIM [11], is pre-
For both the monoclonal antibody clone names, and the protein reasoned, and contains over a million RDF triples. A variety of
targets of the monoclonal antibodies, we have included many queries using AntiO are possible. One can for instance search
additional synonyms to facilitate querying. for all monoclonal antibodies that have a particular protein
target (Figure 2). Or, similarly, all monoclonal antibody
Supported by NIGMS 2R01GM080646 (Protein Ontology), NIAID
HHSN272201200028C (ImmPort), NIAID HHSN272201200028C (HIPC).
offerings (products) from a given vendor that have a particular
target. More indirect querying is possible; for instance, one can
search for the protein targets of monoclonal antibodies using
only the catalog number of the products used. There are
additional ways to search as well; one can limit searches to
antibodies that work only in particular types of experiments,
for instance. We have created a Bitbucket repository and wiki
to provide information about the ontology, as well as example
SPARQL queries (see Table 1 for URLs).
TABLE I
Important URLs
AntiO http://protein.ctde.net:8080/openrdf- Fig. 1. AntiO Ontology ImmPort Schema
Triple Store workbench/repositories/antio/query
enable better reuse and integration of scientific data while
AntiO Wiki https://bitbucket.org/wdduncan/antio/wiki/Home adding value to the NIF Antibody Registry data through our
careful curation and standardization steps.
IV. DISCUSSION
Through careful curation and data extraction using ACKNOWLEDGMENT
computer programs, we have developed an ontology of We thank Sanchita Bhattacharya, Patrick Dunn, Atul Butte,
monoclonal antibodies used in immunological research with a Matthew Brush, Melissa Haendel, and Anita Bandrowski for
focus on ImmPort clinical studies and other recently published helpful comments and support.
papers in immunology. Our effort developing AntiO is
complementary to existing antibody registries. While such
resources let researchers find useful antibodies and the REFERENCES
companies that produce them, they do not provide standardized [1] Bhattacharya S, et al., “ImmPort: disseminating data to the public for the
terms for clone names, targets of the antibodies, conjugations, future of immunology,” Immunol Res. 2014, 58:234-9.
etc. and so are difficult to use computationally. In collaboration [2] Natale DA, et al., “Protein Ontology: a controlled structured network of
with the NIH-funded NIF Antibody Registry, we have protein entities,” Nucleic Acids Res. 2014, 42:D415-21.
developed a framework that will allow researchers to more [3] Brush MH, et al., “Developing a Reagent Application Ontology within
the OBO Foundry,” 2011, http://ceur-ws.org/Vol-833/paper32.pdf.
easily query for monoclonal antibodies, the vendors that sell
[4] Bandrowski A, et al., “The Resource Identification Initiative: A cultural
them, and their protein targets and experimental usage, and shift in publishing,” F1000Res. 2015, 4:134.
provides standardized terminology for all these data types and [5] Bandrowski A, et al., “The Ontology for Biomedical Investigations,”
more. Our long-term goal is to develop web interfaces that will PLoS One. 2016, 11:e0154556.
enable submitters of data not only to query for monoclonal [6] Visser U, et al., “BioAssay Ontology (BAO): a semantic description of
antibodies and their targets, but also facilitate the finding of bioassays and high-throughput screening results,” BMC Bioinformatics.
experimental results, such as clinical studies within the 2011, 12:257.
ImmPort system, in which particular monoclonal antibodies [7] Orchard S, Kerrien S, “Molecular interactions and data standardisation,”
were used. Methods Mol Biol. 2010, 604:309-18.
[8] Sayers EW, et al., “Database resources of the National Center for
Of further note is our reuse within AntiO of the compiled Biotechnology Information,” Nucleic Acids Res. 2009, 37:D5-15.
NIF Antibody Registry data on antibody products, which is [9] Courtot M, et al. “MIREOT: The minimum information to reference an
part of the Research Resource Identification Project [4]. By external ontology term,” Applied Ontology. 2011, 6:23-33.
associating the monoclonal antibody offerings in AntiO with [10] http://protege.stanford.edu
the RRIDs provided by NIF Antibody Registry, we ensure [11] Kiryakov A, Ognyanov D, Manov D, “OWLIM–a pragmatic semantic
AntiO contributes to the goals of the Research Resource repository for OWL.” 2005, In Web Information Systems Engineering–
Identification Project by linking to this common resource to WISE 2005 Workshops, Springer Berlin Heidelberg.
SELECT distinct ?offering ?vendor ?clone ?target
WHERE {
?offeringt rdfs:subClassOf offering: . ?vendori rdf:type vendor: .
?clonet rdfs:subClassOf mAB: . ?targett rdfs:subClassOf protein: .
?r1 owl:onProperty has_part: . ?r1 owl:someValuesFrom ?clonet .
?offeringt rdfs:subClassOf ?r1 .
?r2 owl:onProperty is_sold_by: . ?r2 owl:hasValue ?vendori .
?offeringt rdfs:subClassOf ?r2 .
?r3 owl:onProperty recognizes: . ?r3 owl:someValuesFrom ?targett .
?clonet rdfs:subClassOf ?r3 .
?offeringt rdfs:label ?offering . ?clonet rdfs:label ?clone .
?vendori rdfs:label ?vendor . ?targett rdfs:label ?target .
filter(?clonet != mAB:)
filter(?vendor = "Abcam")
filter (?target = "E-selectin") }
Fig. 2. Example SPARQL query and Results (see wiki for complete query listing).