=Paper=
{{Paper
|id=Vol-3603/Poster4
|storemode=property
|title=Tracking the Functional Effects of SARS-CoV-2 Genomic Variants: an Ontology-Driven
Approach
|pdfUrl=https://ceur-ws.org/Vol-3603/Poster4.pdf
|volume=Vol-3603
|authors=Madeline Iseminger,Muhammad Zohaib Anwar,Rhiannon Cameron,Damion Dooley,Paul Gordon,Emma Griffiths,Anoosha Sehar,Khushi Vora,William Hsiao
|dblpUrl=https://dblp.org/rec/conf/icbo/Iseminger0CDGGS23
}}
==Tracking the Functional Effects of SARS-CoV-2 Genomic Variants: an Ontology-Driven
Approach ==
https://ceur-ws.org/Vol-3603/Poster4.pdf
Tracking the functional effects of SARS-CoV-2
genomic variants: An ontology-driven approach
Madeline Iseminger1,2,∗ , Muhammad Zohaib Anwar2 , Rhiannon Cameron2 ,
Damion Dooley2 , Paul Gordon3 , Emma Griffiths2 , Anoosha Sehar2 , Khushi Vora3 and
William Hsiao1,2
1
University of British Columbia, Vancouver, BC, Canada
2
Centre for Infectious Disease Genomics and One Health, Faculty of Health Sciences, Simon Fraser University, Burnaby,
BC, Canada
3
Centre for Health Genomics and Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Abstract
Emerging SARS-CoV-2 genomic variants can impact disease transmission, viral antigenicity, infection
severity, and vaccine efficacy. As such, it is critical that new variants and their potential impacts are
tracked in a rapid and globally accessible way. Due to the intensive labor required to manually extract
genomic variant information from the literature, a semi-automated approach is needed. We present
a novel ontological framework for describing SARS-CoV-2 mutations and their purported functional
effects, and contextual data for literature evidence. This framework follows Basic Formal Ontology
guidelines and is interoperable with existing OBOFoundry ontologies. When coupled with genomic
surveillance of circulating pathogens, it will assist with rapid sharing of potential functional impacts
of new variants in a standardized, machine-readable way. In future, the model could be extended to
use cases beyond SARS-CoV-2, such as influenza or antimicrobial resistance. The framework consists
of three linked minimodels: variant calling, host and pathogen phenotypes, and literature evidence.
The variant calling model describes the process from sequencing a viral sample to variant calling, and
linking variant calls to phenotypes. As far as we know, this is the first model in OBOFoundry to describe
mutation-phenotype relations. The mutation names exist on the instance level to avoid proliferation
of new classes, and they are correlated with punned instances of phenotypes. Sequence Ontology[1]
terms for mutation types were not used to remain compatible with BFO standards. The phenotype
model contains terms for functional impacts that are correlated with SARS-CoV-2 mutations, spanning
levels of granularity from molecular impacts to impacts on disease transmission. The terms are based
on terms from Pokay[2], a hand-curated repository of SARS-CoV-2 mutations and their functional
effects, with links to related research articles. The phenotype terms are housed in the Pathogen Host
Interaction Phenotype Ontology (PHIPO)[3], while non-phenotype terms (relating to vaccines, treatment,
diagnostics, and associations with pre-existing conditions or homoplasy) are reused from the Vaccine
Ontology (VO)[4], Coronavirus Infectious Disease Ontology (CIDO)[5] wherever possible. Phenotype
terms begin with “altered”, matching PHIPO[3], as a description of the change taking place. The literature
evidence mini model links mutation calls and phenotypes to their literature evidence sources. Short free
text descriptions of the research findings are included here. We are in the process of developing a text
mining module utilizing the minimodels to explore semi-automatic retrieval of relevant literature.
Keywords
SARS-CoV-2, application ontology, mutations and phenotypes, literature retrieval
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References
[1] K. Eilbeck, S. E. Lewis, C. J. Mungall, M. Yandell, L. Stein, R. Durbin, M. Ashburner,
The sequence ontology: a tool for the unification of genome annotations, Genome
Biol. 6 (2005) R44.
[2] P. Gordon, Pokay, https://github.com/nodrogluap/pokay, ???? Accessed: 2023-6-7.
[3] M. Urban, A. Cuzick, J. Seager, V. Wood, K. Rutherford, S. Y. Venkatesh, J. Sahu,
S. V. Iyer, L. Khamari, N. De Silva, M. C. Martinez, H. Pedro, A. D. Yates,
K. E. Hammond-Kosack, PHI-base in 2022: a multi-species phenotype database for
Pathogen-Host interactions, Nucleic Acids Res. 50 (2022) D837–D847.
[4] Y. Lin, Y. He, Ontology representation and analysis of vaccine formulation and
administration and their effects on vaccine immune responses, J. Biomed. Semantics
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[5] Y. He, H. Yu, E. Ong, Y. Wang, Y. Liu, A. Huffman, H.-H. Huang, J. Beverley, J. Hur,
X. Yang, L. Chen, G. S. Omenn, B. Athey, B. Smith, CIDO, a community-based
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Proceedings of the International Conference on Biomedical Ontologies 2023, August 28th-September 1st,
2023, Brasilia, Brazil
∗
Corresponding author.
Envelope-Open m.iseminger@alumni.ubc.ca (M. Iseminger)
Orcid 0000-0002-0548-891X (M. Iseminger); 0000-0001-8236-485X (M. Z. Anwar); 0000-0002-9578-0788
(R. Cameron); 0000-0002-8844-9165 (D. Dooley); 0000-0002-1107-9135 (E. Griffiths);
0000-0001-5275-8866 (A. Sehar); 0000-0002-1342-4043 (W. Hsiao)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 Inter-
national (CC BY 4.0).
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