Back

Programmatic access to ICTV virus taxonomy through a public ontology API

Lieutaud, P.; McLaughlin, j.; Hendrickson, R. C.; David, R.; Parkinson, H.; Lefkowitz, E.; Dempsey, D.; Coutard, B.

2026-06-16 bioinformatics
10.64898/2026.06.16.732600 bioRxiv
Show abstract

The International Committee on Taxonomy of Viruses (ICTV) is responsible for developing and maintaining a universal virus taxonomy. As the reference framework for organising the viral world, it is essential for virology and related fields. Despite its widespread use in research and public health, programmatic access to ICTV taxonomy has remained limited, posing challenges for integration, versioning, and interoperability across databases and bioinformatics resources requiring up-to-date virus taxonomy. To address this, we developed a public and sustainable solution leveraging ontology-based APIs. Successive ICTV Master Species List (MSL) releases were transformed into a structured ontology and deployed as a unified representation through the Ontology Lookup Service (OLS). The framework also provides ICTV-NCBI mappings and helper libraries for integration into downstream systems. This enables, for the first time, public programmatic retrieval of current and historical virological taxon names, taxonomic relationships, metadata, and persistent identifiers through stable endpoints. More broadly, this work illustrates a general strategy for transforming structured biological datasets into semantically enriched graph resources exposed through scalable public APIs. These developments enhance interoperability, reduce manual curation, and support FAIR-aligned taxonomic data management in virology and pandemic preparedness. Key pointsO_LIICTV provides the official taxonomy for classifying viruses and naming virus taxa, but lacks standardised programmatic access. C_LIO_LITransforming ICTV data into an ontology enables semantic, machine-actionable access across releases via ontology-based APIs. C_LIO_LIICTV-NCBI mappings support interoperability across bioinformatics resources. C_LIO_LIThe framework enables programmatic resolution of current and historical viral taxa. C_LIO_LIThis approach provides a reusable model for exposing biological datasets through public APIs. C_LI

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
GigaScience
212 papers in training set
Top 0.1%
12.5%
2
Bioinformatics
1204 papers in training set
Top 2%
11.8%
3
PLOS Computational Biology
1863 papers in training set
Top 4%
9.6%
4
PLOS ONE
5266 papers in training set
Top 25%
6.6%
5
Virus Evolution
155 papers in training set
Top 0.4%
5.4%
6
BMC Bioinformatics
457 papers in training set
Top 2%
5.1%
50% of probability mass above
7
Viruses
332 papers in training set
Top 1%
4.8%
8
NAR Genomics and Bioinformatics
242 papers in training set
Top 0.9%
4.8%
9
Nucleic Acids Research
1281 papers in training set
Top 5%
3.5%
10
Database
61 papers in training set
Top 0.2%
3.2%
11
Journal of Molecular Biology
232 papers in training set
Top 1%
2.4%
12
Bioinformatics Advances
203 papers in training set
Top 3%
2.1%
13
Scientific Data
209 papers in training set
Top 1%
1.7%
14
Frontiers in Bioinformatics
49 papers in training set
Top 0.5%
1.7%
15
Nature Communications
5641 papers in training set
Top 45%
1.7%
16
iMeta
10 papers in training set
Top 0.1%
1.3%
17
Computational and Structural Biotechnology Journal
242 papers in training set
Top 4%
1.3%
18
Peer Community Journal
281 papers in training set
Top 4%
1.1%
19
Scientific Reports
3612 papers in training set
Top 71%
1.0%
20
Journal of Bioinformatics and Systems Biology
15 papers in training set
Top 0.3%
0.8%
21
Frontiers in Genetics
230 papers in training set
Top 6%
0.8%
22
Biology
45 papers in training set
Top 1%
0.6%
23
BioData Mining
22 papers in training set
Top 1%
0.6%