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Uberon, an integrative multi-species anatomy ontology
Open accessAbstract
We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org.
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![Figure 5 Expression of Distal-less (Dll) and Dll orthologs (Dlx) in 'legs'. (a) Three-day Molgula occidentalis ascidian larva from which an ampulla is extending. (b) Polychaete annelid Chaetopterus variopedatus, ventral view of larva just prior to metamorphosis (anterior to left). Dll expressing cells are visible in parapodial rudiments (arrows), antennae (out of focus on opposite dorsal surface), and in prospective feeding organs (bracket). (c) Metamorphosing Strongylocentrotus droebachiensis sea urchin larvae, aboral view. Cells at the distal tip of the tube feet (arrows) express Dll prior to and during extension from the body wall. (d) Expression in nine-day mouse embryo, lateral view, head top; arrows point to medial border of cells expressing one or more Dlx genes in the presumptive forelimb. (e) The evolutionary appearance of the various appendages for which Dll expression data are indicated in (a-d) are shown on this cladogram (branch lengths are not scaled). Reprinted with modification and permission from [49].](/paper-figures/9a8ed57976584e32bfaa40e23b58636d/uberon-an-integrative-multi-species-anatomy-ontology-figure-5.jpg)
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“When possible, all source tissues have been mapped to standardized Uberon (38) identifiers, enabling us to merge repetitive tissues.”
From TCRD and pharos 2021: mining the human proteome for disease biology (2021) · cites this paper as [38]
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Cites
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096 · cited 1×
“Using the Blipkit framework [67], we took the names and synonyms from each ontology, tokenized them, and performed Porter-stemming [68], removed certain tokens such as ‘of’ and ‘the’, and matched two classes if the set of stemmed tokens from any label was identical.”
MethodThe OBO Foundry: coordinated evolution of ontologies to support biomedical data integration
Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W. J., Ceusters, W. M., Goldberg, L. J., Eilbeck, K. L., Ireland, A., Mungall, C., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1346 · Cited by 2,546 · cited 1×
“We believe that Uberon meets the current need for an integrative cross-species anatomy ontology amongst the OBO Foundry suite [66].”
BackgroundDBpedia - A crystallization point for the Web of Data
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Richard Cyganiak, A., & Hellmann, S. (2009) · Journal of Web Semantics · doi:10.1016/j.websem.2009.07.002 · Cited by 2,144 · cited 6×
“It is important to track the provenance of all information collected in an ontology, and this is particularly important for an ontology such as Uberon, which as a matter of expedience frequently includes ‘tertiary sources’ such as Wikipedia, and other ontologies.”
BackgroundInfluentialA reference ontology for biomedical informatics: the Foundational Model of Anatomy
Rosse, C., & Mejino, J. L. (2003) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2003.11.007 · Cited by 1,167 · cited 19×
“However, the FMA is designed primarily to represent post-embryonic human structures, and would be unsuitable for annotating zebrafish genes expressed in an embryonic fin bud.”
BackgroundInfluentialChEBI: a database and ontology for chemical entities of biological interest
Degtyarenko, K. N., de Matos, P., Ennis, M., Hastings, J., Zbinden, M., McNaught, A., Alcántara, R., Darsow, M., Guedj, M., & Ashburner, M. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm791 · Cited by 1,130 · cited 1×
“We have included relationships and other logical axioms that reference other ontologies in Uberon, such as the GO, the Neuro Behavior Ontology (NBO) [30], the CL [15], the Protein Ontology [31] and CHEBI [32].”
MethodThe human phenotype ontology: a tool for annotating and analyzing human hereditary disease
Robinson, P. N., Köhler, S., Bauer, S., Seelow, D., Horn, D., & Mundlos, S. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.09.017 · Cited by 1,034 · cited 2×
“We used the MPO [74] and the Human Phenotype Ontology [58] in a method analogous to the one described for GO, above.”
MethodRelations in biomedical ontologies
Smith, B., Ceusters, W. M., Klagges, B. R. E., Köhler, J., Kumar, A., Lomax, J., Mungall, C., Neuhaus, F., Rector, A. L., & Rosse, C. (2005) · Genome biology · doi:10.1186/gb-2005-6-5-r46 · Cited by 1,030
FaCT++ Description Logic Reasoner: System Description
Tsarkov, D., & Horrocks, I. (2006) · Lecture notes in computer science · doi:10.1007/11814771_26 · Cited by 805
The Gene Ontology project in 2008
The Gene Ontology Consortium (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm883 · Cited by 776
Systematic determination of patterns of gene expression during Drosophila embryogenesis
Tomančák, P., Beaton, A., Weiszmann, R., Kwan, E., Shu, S., Lewis, S., Richards, S., Ashburner, M., Hartenstein, V., Celniker, S., et al. (2002) · Genome biology · doi:10.1186/gb-2002-3-12-research0088 · Cited by 725 · cited 1×
“4 [80] to encode GCIs and other advanced OWL features within OBO format.”
Background
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Items 501–600 of 750
The use of plant ontologies in comparative and evolutionary studies should be flexible
Sokoloff, D. D., & Remizowa, M. V. (2021) · American Journal of Botany · doi:10.1002/ajb2.1692 · Cited by 4
“Serial homology differs from homology in general in that states of the homologous features co- occur in the same individual organism (Nixon and Carpenter, 2012).”
BackgroundThe Informatics of Developmental Phenotypes
Schofield, P. N., Hoehndorf, R., Smith, C. L., Eppig, J. T., & Gkoutos, G. V. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-800043-4.00025-7 · Cited by 4
Lundberg, J. G., & Luckenbill, K. R. (2015) · Copeia · doi:10.1643/cg-14-112 · Cited by 4
Kibbe, W. A. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802115-6.00003-3 · Cited by 4
Farrell, B., & Bengtson, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223984 · Cited by 5
“The anatomical arm (S1 Fig) made use of the anatomical structure terms imported from the Foundational Model of Anatomy (FMA) [91], and Uberon multi-species anatomy (UBERON) [92, 93] ontologies.”
MethodTaxonomy and the Production of Semantic Phenotypes
Yoder, M. J., Twidale Michael, B., Thomer Andrea, K., Vogt, L., Franz Nico, M., Guo, J., DEANS, A. R., & Balhoff, J. P. (2018) · Studies on the semantic web · doi:10.3233/978-1-61499-854-9-53 · Cited by 4
Semantic Technologies in Drug Discovery
Kanza, S., & Frey, J. G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11520-x · Cited by 4
Toward Phylomics in Entomology: Current Systematic and Evolutionary Morphology
Boudinot, B. E. (2019) · Insect Systematics and Diversity · doi:10.1093/isd/ixz019 · Cited by 4
He, Y. O., Barisoni, L. M. C., Rosenberg, A. Z., Robinson, P. N., Diehl, A. D., Chen, Y., Phuong, J., Hansen, J., Herr, B. W., Börner, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.01.587658 · Cited by 3
“…from 2017, KPMP has organized multiple workshops in which clinical nephrologists, pathologists, and ontologists worked together to discuss and apply a high level framework for annotating, collecting, and proposing new terms or modifications to HPO (7) and underlying ontologies such as Uberon (6).”
InfluentialDisentangling associations between complex traits and cell types with seismicPreprint
Lai, Q., Dannenfelser, R., Roussarie, J.-P., & Yao, V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.04.592534 · Cited by 4
“Cell types in TM and TS were manually annotated to corresponding terms in the Cell Ontology [54, 55], and unresolvable cell types were excluded.”
Public RNA-seq data are not representative of global human diversityPreprint
Romero, I. G., Rodenberg, G., Arner, A. M., Li, L., Beasley, I. J., Rossow, R., Ryan, N., Wang, S., & Lea, A. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.11.617967 · Cited by 4
“For each randomly chosen SRA accession for manual review, the study was excluded from further analyses if it met any of the following criteria: (i) studies only used established, cancerous, or cancer-derived cell lines, (ii) studies that did not measure gene expression, (iii) studies using only…”
MethodUpdates implemented in version 4 of the GlyCosmos Glycoscience Portal
Lee, S., Ono, T., Masaaki, S., Fujita, A., Matsubara, M., Zappa, A., Yamada, I., & Aoki-Kinoshita, K. F. (2024) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-024-05692-0 · Cited by 3
“The GlyCosmos project has made an effort to explicitly explain resources by mapping them to vocabularies provided by domain-specific ontologies, including Disease Ontology (DO) [13], Gene Ontology (GO) [14], Uber-anatomy Ontology (UBERON) [15], and DDBJ Taxonomy.”
Fiala, G., Plass, M., Harb, R., Regitnig, P., Skok, K., Zoughbi, W. A., Zerner, C., Torke, P. R., Kargl, M., Müller, H., et al. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103368 · Cited by 3
The DO-KB knowledgebase 2026 update: expanding programmatic and language access
Baron, J. A., Johnson, C. M. S.-B., Schor, M. A., Olley, D., Nickel, L., Felix, V., Bello, S. M., Greene, C. L., Lichenstein, R., Bisordi, K., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1213 · Cited by 4
“Beginning with diseases associated with a sub-D set of the musculoskeleton, a proof-of-concept web application was developed that links regions of anatomical graphics with Uberon classes that are linked to DO diseases [ 14 ].”
scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository
Youngblut, N. D., Carpenter, C., Nayebnazar, A., Adduri, A., Shah, R., Ricci-Tam, C., Prashar, J., Ilango, R., Teyssier, N., Konermann, S., et al. (2026) · Cell · doi:10.1016/j.cell.2026.08.025 · Cited by 50
A Knowledge Graph Framework for Dementia Research Data
Reina, S. T., Rincón, M., Martínez‐Tomás, R., Kirsebom, B., & Fladby, T. (2023) · Applied Sciences · doi:10.3390/app131810497 · Cited by 4
“In the area of anatomy, we incorporate the Uber-Anatomy Ontology (UBERON) [59,60] and the Foundational Model of Anatomy (FMA) [61].”
MethodNovoa, J. A. R., López-Ibáñez, J., Chagoyen, M., Ranea, J. A. G., & Pazos, F. (2024) · Database · doi:10.1093/database/baae025 · Cited by 3
“For example, co-mentions between cell types (CL) and tissues (UBERON) or between HMDB and MeSH chemical compounds were not evaluated.”
BackgroundInfluentialGöpel, T. (2024) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2024.1343346 · Cited by 3
Building a Global Aquatic Resource Knowledge Base for Fisheries
Marketakis, Y., Tzitzikas, Y., Gentile, A., Ellenbroek, A., & Taconet, M. (2025) · HAICTA · doi:10.3390/proceedings2025117004 · Cited by 3
“In the literature, there are several ontologies for the marine domain, such as MarineTLO [5], SWEET [6], BioTop [7], Uberon [8], etc.”
scMOVIR: a single-cell multi-omics database for human viral infections and immune responses
Zhang, X., Yang, S., Xu, X., Lin, Y., Sun, H., Zhu, B., Zhao, W., Zhou, B., Lou, Y., Wang, X., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1153 · Cited by 3
“All viruses, diseases, tissues, and proteins were standardized using the authoritative databases such as NCBI Tax-onomy [ 36 ], WHO ICD-11 [ 61 ], Uberon [ 62 ], and UniProt [ 63 ].”
What is the abdomen? Rationalising clinical and anatomical perspectives using formal semantics
Brown, P. J. B., Gao, Y., & Clunie, D. (2021) · Journal of Anatomy · doi:10.1111/joa.13384 · Cited by 3
Covid19-obkg: an ontology-based knowledge graph and web service for covid-19
Zheng, X., Xiao, Y., Song, W., Tong, F., Liu, S., & Zhao, D. (2021) · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669535 · Cited by 3
Yan, M. Y., Gustad, L. T., Høvik, L. H., & Øystein Nytrø, A. (2023) · Semantic Web · doi:10.3233/sw-223226 · Cited by 3
An Anatomical Ontology for the Class Collembola (Arthropoda: Hexapoda)
Montaña, L. A. G., Rueda‐Ramírez, D., Serna, F., & Gaigl, A. (2023) · Brazilian Archives of Biology and Technology · doi:10.1590/1678-4324-2023220682 · Cited by 3
“Shared terminology is a measure of interoperability between ontologies [43] and a prerequisite to query all morphological phenotypes applied to an anatomical entity and building multi-species ontologies [15].”
BackgroundMethodBig knowledge visualization of the COVID-19 CIDO ontology evolution
Zheng, L., Perl, Y., & He, Y. O. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02184-6 · Cited by 3
“Originally the concepts in the ‘mesoderm-derived structure’ and ‘tissue’ nodes were imported into CIDO from the Uberon ontology [29] using the tool OntoFox [30].”
MethodCapturing heart valve development with Gene Ontology
Ahmed, S. H., Deng, A. T., Huntley, R. P., Campbell, N. H., & Lovering, R. C. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1251902 · Cited by 3
Mohammadi, M. S., Planty-Bonjour, A., Poupon, F., Uszynski, I., Poupon, C., Destrieux, C., & Andersson, F. (2023) · Brain Structure and Function · doi:10.1007/s00429-023-02726-8 · Cited by 3
“These arbitrary categories were the same as in Lechanoine et al. (2021), and a link to the UBERON ontology (http:// purl. oboli brary. org/ obo/, Mungall et al. 2012) was provided for each entry of https:// fibra tlas. univ-tours. fr/ brain stems/ index. html.”
Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines
Smith, E., Paloots, R., Giagkos, D., Baudis, M., & Stockinger, K. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae045 · Cited by 3
Ontologies related to livestock for the Global Burden of Animal Diseases programme: a review
Bruce, M., Rushton, J., RAYMOND, K., NGUYEN, L., KWOK, S., Larkins, A., Omar, H. A., & GBADs, C. (2024) · Scientific and Technical Review (World Organisation for Animal Health) · doi:10.20506/rst.43.3519 · Cited by 3
“The Uberon Multi-Species Anatomy Ontology (Uberon) [27] is an integrated cross-species anatomy ontology covering animals and bridging multiple species-specific ontologies.”
Differential expression and co-expression reveal cell types relevant to genetic disorder phenotypes
Alías-Segura, S., Pazos, F., & Chagoyen, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae646 · Cited by 2
Carvalho, T. P., & dos Reis, R. E. (2020) · Copeia · doi:10.1643/ci-19-309 · Cited by 3
“In general, anatomical terminology follows the Teleost Anatomical Ontology (TAO; Dahdul et al., 2010), an integral part of the Uberon Ontology covering anatomical structures in animals (Mungall et al., 2012; https://uberon. github.io).”
BackgroundOntology-based support for taxonomic functions
Gerber, A., Morar, N., Meyer, T. A., & Eardley, C. (2017) · Ecological Informatics · doi:10.1016/j.ecoinf.2017.06.003 · Cited by 3
Assessing the IADC Space Debris Mitigation Guidelines: A Case for Ontology-based Data Management
Walls, R., CyVerse, Gaylor, D., Reddy, V., Roberto, Furfaro, & Moriba, J. A. H. (2016) · Advanced Maui Optical and Space Surveillance Technologies Conference · doi:10.64861/xpmv8048 · Cited by 3
“, [14-16]), along with our desire to build an open resource, we adhere to OBO Foundry principles [17] such as openness, versioning, text definitions, and maintenance.”
MethodDruzinsky, R. E., Mungall, C., Haendel, M. A., Lapp, H., & Mabee, P. (2013) · The Anatomical Record · doi:10.1002/ar.22805 · Cited by 3
Biological and Medical Ontologies: Human Phenotype Ontology (HPO)
Bernasconi, A., & Masseroli, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20398-1 · Cited by 3
Börner, K., Quardokus, E. M., Herr, B. W., Cross, L. E., Record, E. G., Ju, Y., Bueckle, A., Sluka, J. P., Silverstein, J. C., Browne, K., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.14474 · Cited by 5
“Next, we identify ASCTs names and unique identifiers in existing ontologies, such as Foundational Model of Anatomy (FMA) [17,18], UBERON [19], and Cell Ontology [20].”
MethodSystematic tissue annotations of –omics samples by modeling unstructured metadataPreprint
Hawkins, N. T., Maldaver, M., Yannakopoulos, A., Guare, L. A., & Krishnan, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.10.443525 · Cited by 3
Surles-Zeigler, M. C., Sincomb, T., Gillespie, T., de Bono, B., Bresnahan, J. C., Mawe, G. M., Grethe, J. S., Tappan, S. J., Heal, M., & Martone, M. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.15.467961 · Cited by 3
“NIFSTD itself is built through imports of major community ontologies and atlases, including the Uber-anatomy ontology (UBERON; RRID:SCR_010668) (Haendel et al., 2009; Mungall et al., 2012), the Allen Mouse Brain Atlas (RRID:SCR_002978; https://mouse.brain-map.org/) and other parcellation schemes.”
BackgroundMethodOntology for Cellular Senescence MechanismsPreprint
Yamagata, Y., Fukuyama, T., Onami, S., & Masuya, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.09.531883 · Cited by 3
Yamagata, Y., Fukuyama, T., Onami, S., & Masuya, H. (2024) · Scientific Data · doi:10.1038/s41597-024-03331-y · Cited by 3
“To permit interoperability of the knowledge, terms widely used in a biomedical ontology in the OBO Foundry 15 , such as GO, the phenotype ontologies Phenotype And Trait Ontology (PATO) 16 and Human Phenotype Ontology (HPO) 17 , the Uber-anatomy ontology (UBERON) 18 , the cell ontology (CL) 19 , the Symptom Ontology 20…”
Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas
Bueckle, A., Herr, B. W., Chen, L., Bolin, D., Qaurooni, D., Ginda, M., Jain, Y., Puig-Barbe, A., Ardlie, K., Wang, F., et al. (2026) · Scientific Data · doi:10.1038/s41597-026-06642-4 · Cited by 4
“As a result, 400 total CTs might be a clear underestimation.”
Homeostasis imbalance process ontology: a study on COVID-19 infectious processes
Yamagata, Y., Kushida, T., Onami, S., & Masuya, H. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02516-0 · Cited by 6
“…the following existing ontologies: processes from Gene Ontology (GO) [6], anatomical entities from Uberon multi-species anatomy ontology (UBERON) [7], Cell Ontology (CL) [8], taxonomy information curated by the National Center for Biotechnology Information (NCBI) [9], compounds curated by…”
Yuan, H., Hicks, P., Ahmadian, M., Johnson, K. A., Valtadoros, L., & Krishnan, A. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae652 · Cited by 2
“…models, we created a gold-standard dataset consisting of 33,849 samples and 3,676 studies from various sources [19, 20, 21, 22, 18, 23, 24] (including in-house curated data; Table S1 and S2 ) labeled with terms in the Uberon-CL tissue/cell ontology [25] and the MONDO disease ontology [26].”
Nematollahi, S., Hamdy, R. C., van Bosse, H. J. P., Li, J., Blanshay‐Goldberg, D., de Vries, J. I. P., Dieterich, K. D., Filges, I., Bedard, T., Haendel, M. A., et al. (2025) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.64067 · Cited by 2
“We used Uberon Ontology (retrieved from: https:// www. ebi. ac. uk/ ols/ ontol ogies/ uberon) (Mungall et al. 2012) to classify the suggestions based on anatomic location, for example, to describe “elbow” vs. “forearm” for “elbow contracture.””
Kushida, T., de Farias, T. M., Sima, A. C., Dessimoz, C., Chiba, H., Bastian, F., & Masuya, H. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03013-8 · Cited by 2
“Ontology graphs translated by KG-OBO include Gene Ontology [30], ChEBI [31], and Uber-anatomy ontology (Uberon) [32].”
Bolognesi, M. M., Dall’Olio, L., Mandelli, G. E., Lorenzi, L., Bosisio, F. M., Haberman, A. M., Bhagat, G., Borghesi, S., Faretta, M., Castellani, G., et al. (2026) · PLOS One · doi:10.1371/journal.pone.0346693 · Cited by 2
“A UBERON terminology (https://evs.nci.nih.gov/ftp1/UBERON/About.html; last consulted on September 4th 2024) [134] was added when suitable (Table 1–7-7, S1 and S14 Tables).”
Fullam, A., K., V., Kuhn, M., Peer Bork, B., & Schmidt, T. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag343 · Cited by 2
“Terms are moreover linked to matching classes in EnvO (Buttigieg et al. 2013, 2016), UBERON (Mungall et al. 2012) and other existing ontologies, as well as to NCBI Taxonomy IDs (Schoch et al. 2020) where applicable.”
van der Horst, E., Kaliyaperumal, R., Tatum, Z., Thompson, M., Schultes, E., Mina, E., Fokkema, I. F. A. C., den Dunnen, J. T., Roos, M., Laros, J. F. J., et al. (2015) · SWAT4LS · Cited by 2
An Embedding-Based Approach to the Ontology Matching Problem
Benarab, A., Yin, G., & Rafique, F. (2023) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml59573.2023.10348247 · Cited by 2
“With 3304 cancer-related concepts, it offers structured representation for basic and clinical research [19].”
BackgroundMRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology
Bartnik, A., Serra, L. M., Smith, M., Duncan, W. D., Wishnie, L., Ruttenberg, A., Dwyer, M. G., & Diehl, A. D. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09664-8 · Cited by 2
“This helps to standardize reporting on analyses using these brain regions and tie the results of neuroimaging studies to existing databases that use Uberon and other OBO Foundry ontologies.”
InfluentialTerán, G., Méndez‐López, A., Benítez, M. F., Serra, W. S., Bogan, S., & Aguilera, G. (2024) · Zoosystematics and Evolution · doi:10.3897/zse.100.121396 · Cited by 2
“Nomenclature of anatomical structures follows that used by the three mentioned authors and Dahdul et al. (2010), included in the Teleost Anatomy Ontology of Uberon Ontology Documentation (Mungall et al. 2012, http://obophenotype.github.io/uberon/).”
MethodOntologies in modelling and analysing of big genetic data
Podkolodnyy, N. L., Podkolodnaya, O. A., Иванисенко, В. А., & Марченко, М. А. (2025) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vjgb-24-101 · Cited by 3
A guide to developing harmonized research workflows in a team science context
Ruiz, O. E., Wagenaar, J., Mehta, B. Y., Ziogas, I., Swanson, L., Worley, K. C., Cruz‐Almeida, Y., Johnson, A. J., Boline, J., Boccanfuso, J. B., et al. (2025) · Experimental Neurology · doi:10.1016/j.expneurol.2025.115333 · Cited by 2
RNA-KG v2.0: an RNA-centered Knowledge Graph with Properties
Cavalleri, E., Perlasca, P., & Mesiti, M. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf194 · Cited by 2
“Eleven standard biomedical ontologies are used to establish common semantics: Human Phenotype Ontology (HPO [51]), Gene Ontology (GO [1]) Monarch Merged Disease Ontology (Mondo [67]), Vaccine Ontology (VO [27]), Chemical Entities of Biological Interest (ChEBI [17]), Uber-anatomy Ontology (Uberon…”
Target discovery from protein databases: challenges for curation
Chichester, C. H., & Gaudet, P. (2015) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2015.01.003 · Cited by 2
“Annotation using ontologies, such as the Gene Ontology [10], the Human Phenotype ontology [11], or the Uberon anatomy ontology [12] enables common concepts for all these datasets and provides the semantics to perform rich queries and data comparisons.”
MethodKiryu, H., Ichikawa, Y., & Kojima, Y. (2019) · Algorithms for Molecular Biology · doi:10.1186/s13015-019-0158-3 · Cited by 2
Bootstrapping Adversarial Learning of Biomedical Ontology Alignments.
Maldonado, R., & Harabagiu, S. M. (2019) · PubMed Central · Cited by 2
Standardising Syndromic Classification in Animal Health Data
Dórea, F. C., Dupuy, C., Vial, F., Revie, C. W., & Lindberg, A. (2015) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v7i1.5789 · Cited by 2
The BioHub Knowledge Base: Ontology and Repository for Sustainable Biosourcing
Read, W., Demetriou, G. C., Nenadić, G., Ruddock, N., Stevens, R., & Winter, J. (2016) · Journal of Biomedical Semantics · doi:10.1186/s13326-016-0071-3 · Cited by 2
“As well as plants, the IKMS will use animal based feedstocks meaning the BioHubO will need to be extended to animal species and the generic animal anatomy Uberon [10] wil be appropriately extended.”
MethodBada, M. A., Hunter, L., Vasilevsky, N., & Haendel, M. A. (2016) · Cited by 2
Species descriptions and digital environments: alternatives for accessibility of morphological data
Silva, T. S. R. (2017) · Revista Brasileira de Entomologia · doi:10.1016/j.rbe.2017.06.005 · Cited by 2
“According to Mungall et al. (2012), although the inference of homology is important from the perspective of connecting gene expression and phenotype across species, statements of homology can be controversial, subject to change and even contradictory, providing a limited set of potentially…”
BackgroundAn Ontology for Dynamic Sensor Selection in Wearable Activity Recognition
Villalonga, C., Baños, O., Pomares, H., & Rojas, I. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16480-9_15 · Cited by 2
“Another candidate was the Uber anatomy ontology (Uberon) [12,3], an anatomy ontology that integrates any type of animal species.”
BackgroundMasci, A. M., White, S., Neely, B., Ardini, M.-A., Hill, C. B., Misra, R. S., Aronow, B. J., Gaddis, N., Yang, L., Wert, S. E., et al. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04008-8 · Cited by 2
Semantic simulations based on object-oriented analysis and modeling
Allen, R. B. (2020) · Library and Information Science Research E-Journal · doi:10.32655/libres.2020.29.2.4 · Cited by 2
Köhler, S., Doelken, S. C., Ruef, B. J., Bauer, S., Washington, N. L., Westerfield, M., Gkoutos, G., Schofield, P. N., Smedley, D. P., Lewis, S., et al. (2013) · F1000Research · doi:10.3410/f1000research.2-30.v1 · Cited by 2
Zheng, L., Liu, H., Perl, Y., Geller, J., Ochs, C., & Case, J. T. (2018) · Europe PMC (PubMed Central) · Cited by 2
Testing the AgreementMaker System in the Anatomy Task of OAEI 2012Preprint
Faria, D., Pesquita, C., dos Santos, E. M., Couto, F. M., Stroe, C., & Cruz, I. F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1212.1625 · Cited by 2
“Furthermore, with just an update to the UBERON ontology used as a mediator, the performance of AgreementMaker would have been only marginally below that of the top system, GOMMA-bk.”
MethodInfluentialDahdul, W., Manda, P., Cui, H., Balhoff, J. P., Dececchi, T. A., Ibrahim, N., Lapp, H., Vision, T., & Mabee, P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/322156 · Cited by 2
Modeling quantitative traits for COVID-19 case reportsPreprint
Queralt-Rosiñach, N., Bello, S. M., Hoehndorf, R., Weiland, C., Rocca‐Serra, P., & Schofield, P. N. (2020) · medRxiv · doi:10.1101/2020.06.18.20135103 · Cited by 2
“For example, the CMO class “heart measurement” (CMO:0000670) matches with the Uberon class “heart” (UBERON:0000948).”
MethodInfluentialCarrio-Cordo, P., & Baudis, M. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-220990 · Cited by 2
Kushida, T., de Farias, T. M., Sima, A. C., Dessimoz, C., Chiba, H., Bastian, F., & Masuya, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.30.555283 · Cited by 2
MegaKG: Toward an explainable knowledge graph for early drug developmentPreprint
Dong, J., Liu, J., Wei, Y., Huang, P., & Wu, Q. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.27.586981 · Cited by 2
“UBERON[42] is a database of ontology of anatomical structures.”
BackgroundFeuermann, M., & Gaudet, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00068-3 · Cited by 2
Knowledge Graph and Machine Learning Help the Research of Drugs Aimed at Neurological DiseasesPreprint
Menestrina, L., & Recanatini, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.29.626076 · Cited by 2
The Ontology of Adverse Events in 2025
Pan, C., Yang, Q., Zhang, X., Luo, S., He, Y., & Xie, J. (2026) · Scientific Data · doi:10.1038/s41597-026-06584-x · Cited by 2
Huffman, A., Yeh, F.-Y., Hur, J., Zheng, J., Masci, A. M., Wu, G., Tao, C., Athey, B. D., & He, Y. (2026) · Scientific Data · doi:10.1038/s41597-026-06558-z · Cited by 2
Serum metabolic signatures of cognitive resilience in a longitudinal aging cohortPreprint
Scheurink, T. A. W., Seo, J. I., David, L. C., Wang, C. X., Solis, D. D., Zemlin, J., Bergstrom, J., Dorrestein, P. C., Mohanty, I., & Molina, A. J. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.29.715122 · Cited by 2
“We used the Uber-anatomy ontology (UBERON) 38 classification in Pan-ReDU to explore the distribution of features with positive or negative loadings on the cognitive resilience-associated component ( Figure 6a ).”
MetaMuse: A Multi-Agent AI System for Biomedical Metadata Curation and HarmonizationPreprint
Mittal, E., Litman, E., Myers, T. D., Agarwal, V., Gopinath, A., & Kassis, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.12.718044 · Cited by 2
Chua, H.-E., Tucker‐Kellogg, L., & Bhowmick, S. S. (2022) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3535508.3545531 · Cited by 1
Yse, D. L., & Torres, D. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-40942-4_8 · Cited by 2
Yargan, D., & Jansen, L. (2025) · Biomimetics · doi:10.3390/biomimetics10010039 · Cited by 1
Comparative anatomical complexity of 5 vertebrates and 6 invertebrates
Root, A. (2025) · Zoomorphology · doi:10.1007/s00435-025-00741-3 · Cited by 1
Navigating open-source waters: the pharmaceutical industry’s role in bioontology development
Tan, S. Z. K., Valdez, J., & Kuriakose, S. V. (2025) · Database · doi:10.1093/database/baaf066 · Cited by 1
“As examples, Uberon [ 6 ] and Cell Ontology [ 7 ][ 8 ] have anywhere between 200 and 300 open issues.”
Database Release: PPSDB , a Linked Open Data Knowledge Base for Protist–Prokaryote Symbioses
Seah, B. K. B. (2025) · Journal of Eukaryotic Microbiology · doi:10.1111/jeu.70049 · Cited by 1
“…in the NCBI Taxonomy The localization of symbionts in the host was mapped to cellular anatomy terms in the Gene Ontology (GO) (Ashburner et al. 2000; Gene Ontology Consortium et al. 2023) or metazoan anatomical terms in Uberon (for protists that are also symbionts of animals) (Mungall et al. 2012).”
SSBD Ontology: A Two-Tier Approach for Interoperable Bioimaging Metadata
Yamagata, Y., Kyoda, K., Itoga, H., Fujisawa, E., & Onami, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09530-5_19 · Cited by 1
Language Models as Ontology Encoders
Yang, H., Chen, J., He, Y., Gao, Y., & Horrocks, I. R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09527-5_24 · Cited by 2
Hak, F., Marchet, C., Gautheret, D., & Gallopin, M. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag166 · Cited by 1
CCCdb: a comprehensive manually curated database for cell–cell communication in human and mouse
Xu, M., Zhang, G., Wang, X., Chen, Y., Feng, C., Guo, J., Fan, X., Liu, L., Wang, Y., Cui, T. R., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1105 · Cited by 1
“All biological terms are standardized us-ing official ontologies Uberon_ID [ 42 ], CL_ID [ 43 ], DOID [ 44 ], and KEGG_ID [ 45 ] to ensure consistency across datasets from diverse sources.”
Popova, E., Jacobs, M. D., Hofmann‐Apitius, M., & Babaiha, N. S. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100816 · Cited by 1
SquiDBase: a community resource of raw nanopore data from microbes
Cuypers, W. L., Ceylan, H., Turcksin, E., Raes, L., de Vrij, N., Míchiels, J., Coppens, S., de Block, T., Jansen, D., Ariën, K. K., et al. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf213 · Cited by 1
“For example, we employ UBERON [ 32 ], ENVO [ 33 ], and FOODON [ 34 ] to specify the sample source and the OBI ontology [ 35 ] to describe the diagnostic method.”
Integrative analysis reveals the autoantibodyome neuroimmune signature of neurodegeneration
Usuda, J. N., Nóbile, A. L., do Vale, F. Y. N., Corrêa, Y. L. G., Adri, A. S., Nava, R. G., Freitas, D. G. D. A., Santos, R. S., Schimke, L. F., Fonseca, D. L. M., et al. (2026) · iScience · doi:10.1016/j.isci.2026.114781 · Cited by 3
Ontology-aware DNA methylation classification with a curated atlas of human tissues and cell types
Kim, M., Dannenfelser, R., Cui, Y., Allen, G. I., & Yao, V. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101328 · Cited by 1
“We further disambiguated tissue and cell type annotations by manually assigning them to the most descriptive tissue or cell term in the UBERON ontology [26] and merging functionally and physiologically similar terms, such as buccal mucosa and oral epithelium (Supplementary Table 6).”
InfluentialDNAviWEB: sequencing-free clinical screening of liquid biopsies
Hess, A., Matani, Y., Seelow, D., & Kretzmer, H. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag446 · Cited by 1
“To enable sustainable and cross-compatible data deposition, DNAviWEB’s metadata section follows the standardized EGA [ 10–12 ] scheme, and hence, access to a wide range of ontology terms, e.g., MONDO [ 13 ], HPO [ 14 ], XCO [ 27 ], CL [ 28 , 29 ], UBERON [ 30 ], and DRON [ 31 ].”
CMOMgen: Complex multi-ontology alignment via pattern-guided in-context learning
Silva, M. C., Faria, D., & Pesquita, C. (2026) · Journal of Web Semantics · doi:10.1016/j.websem.2026.100884 · Cited by 1
Verandani, K. (2026) · International Conference on Emerging and Intelligent Technologies and Systems (EITS) · doi:10.1109/eits70066.2026.11609644 · Cited by 1
“The following resources were integrated, drawing on the OBO Foundry’s interoperability principles [14]: • RxNorm and the Anatomical Therapeutic Chemical Classification System for drug substance and drug class normalization [15], [16].”
Exploring cell-type associations of rare disease phenotypes using non-diseased cell atlases
Novoa, J., Pazos, F., & Chagoyen, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag426 · Cited by 1
“Anatomical entities were retrieved from the Uberon Multi-species Anatomy ontology (UBERON) ( https://www.ebi. ac.uk/ols4/ontologies/uberon; [ 32]; v2024-01-27).”
Reporting and representation of population descriptors in public RNA-seq databases
Romero, I. G., Rodenberg, G., Arner, A. M., Li, L., Beasley, I. J., Rossow, R., Ryan, N., Wang, S., & Lea, A. J. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101326 · Cited by 5
“Cell type and tissue information was further standardized and grouped by searching terms against the Cell (CL) and UBERON ontologies 50,51 ; disease information was standardized and grouped using the Disease Ontology 52 ; in all cases we resolved any ambiguities by manually inspecting the SRA Study…”
A Disaster Document Classification Technique Using Domain Specific Ontologies
Mudassar, Q. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.061217 · Cited by 1
“Ontologies have been used in a diverse range of domains from cultural heritage [4] to 3D modeling [5], ecommerce [6] to health services [7], human anatomy [8] to fraud detection [9] and cyber warfare [10] to agriculture [11].”
BackgroundCOMICS: Cartoon Visualization of Omics Data in Spatial Context Using Anatomical Ontologies
Travin, D. Y., Popov, I., Güler, A. T., Medvedev, D., van der Plas-Duivesteijn, S. J., Varela, M., Kolder, I. C. R. M., Meijer, A. H., Spaink, H. P., & Palmblad, M. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00615 · Cited by 1
Bohár, B., Fazekas, D., Madgwick, M., Csabai, L., Olbei, M. L., Korcsmaros, T. D., & Szalay-Bekő, M. (2022) · F1000Research · doi:10.12688/f1000research.52791.2 · Cited by 1
Metadata
- Authors
- Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A.
- Affiliations
- Oregon Health & Science University (US)
- University of Cambridge (GB)
- Lawrence Berkeley National Laboratory (US)
- Venue
- Genome biology
- Published
- 2012-01-31
- Volume
- 13
- Issue
- 1
- Pages
- R5-R5
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC3334586
- OpenAlex
- W2116034137
- Cited by
- 732
- References
- 67
- Citations by year
- 2026: 60 · 2025: 69 · 2024: 55 · 2023: 66 · 2022: 55 · 2021: 51 · 2020: 46 · 2019: 45 · 2018: 55 · 2017: 46 · 2016: 49 · 2015: 50 · 2014: 34 · 2013: 28 · 2012: 21
- FWCI
- 17.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, DE-AC02-)
- National Institutes of Health (2p41hg002273-07, 5u24rr029825-02, DE-AC02–05CH11231)
- Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02, 05CH11231)
- National Human Genome Research Institute (U41HG002273, R01HG004838, P41HG002273-09S1, P41-HG002273)
- National Center for Research Resources (1U24RR029825-01)
- Basic Energy Sciences (DE-AC02, DE-AC02-05CH11231., AC02–05CH11231)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- UBERON, anatomy ontology, anatomical classification, taxonomic variation, human anatomy
- MeSH terms
- Anatomy, Comparative, Classification, Humans, Phenotype, Software, Species Specificity, Databases, Factual, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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