OSCR

The Cell Ontology in the age of single-cell omics.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § Methods › Integration with taxon-specific ontologies ↔ src/aurelian/agents/ubergraph/ubergraph_agent.py, lines 11–46 · score 0.64 · oboInOwl, hasDbXref, cross reference, provenance, predicates, Uberon
  2. [2] § Methods › Implementing taxon constraints ↔ src/aurelian/agents/robot_ontology/robot_ontology_agent.py, lines 1–90 · score 0.59 · logical axioms, annotation properties, ROBOT, taxon
  3. [3] § Methods › Integration with taxon-specific ontologies ↔ src/aurelian/agents/robot_ontology/robot_ontology_agent.py, lines 1–90 · score 0.57 · oboInOwl, OBO ontologies, fetch, taxon, metadata, mappings
  4. [4] § Results › Application › Applications for viewing and querying the Cell Ontology ↔ src/aurelian/agents/ubergraph/ubergraph_mcp.py, lines 13–35 · score 0.53 · SPARQL endpoint, SPARQL queries, Ubergraph, asserted, OBO, Uberon
  5. [5] § Results › Application › Applications for viewing and querying the Cell Ontology ↔ src/aurelian/agents/ubergraph/ubergraph_tools.py, lines 62–130 · score 0.53 · SPARQL queries, SPARQL endpoint, precomputes, Ubergraph, OBO, ontologies

Paper

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The authors' code

Python · 137 lines · 5.7 KB · MIT · 2 matches

  1. """
  2. Agent for creating ROBOT templates and compiling to ontologies.
  3. """
  4. from dataclasses import dataclass, field
  5. from typing import List, Dict
  7. from aurelian.agents.filesystem.filesystem_tools import inspect_file, download_url_as_markdown, list_files
  8. from src.aurelian.agents.robot_ontology.robot_ontology_config import RobotDependencies
  9. from aurelian.agents.robot_ontology.robot_tools import write_and_compile_template, fetch_documentation
  10. from aurelian.utils.async_utils import run_sync
  11. from aurelian.utils.search_utils import web_search
  12. from pydantic_ai import Agent, RunContext, Tool
  14. from aurelian.dependencies.workdir import WorkDir, HasWorkdir
  16. SYSTEM = """
  17. Background:
  19. Your job is to iteratively build an ontology via *robot templates*,
  20. These are tabular data (CSV syntax) with a special header that compiles to OWL.
  22. For example, if the request is for an animals ontology, you could start with a CSV with columns Name, ParentTaxon, Eats,
  23. with rows filled out with some example animals.
  25. The main tool you will use is `write_and_compile_template` which writes the the template content to
  26. a local file after compiling to OWL. This also takes a list of ontologies to import, which
  27. should also be on the file system.
  29. Sometimes you may need to work with multiple dependent ontologies. For example, if you have a vehicle class
  30. hierarchy in `vehicles.csv` and parts in `parts.csv`, and vehicles depends on parts, you would first iterate
  31. on `parts.csv` (e.g. calling `write_and_compile_template`, with no imports), then write vehicles using
  32. `write_and_compile_template` with `['parts.csv']` as the dependencies/imports.
  34. ## Robot template CSV structure:
  36. Robot template files have an additional metadata row below the header row. This is called the "template row". It specifies how each column maps to OWL. Typical values will be "ID" for the unique identifier, LABEL for the name, "SC %" for the parent class. Consult the docs for details. Note that this is always beneath the main header row. This can seem a bit duplicative, but that's OK. An example might be:
  38. identifier,name,parent,synonyms
  39. ID,LABEL,SC %,A oboInOwl:hasExactSynonym
  40. ANIMAL:1,chicken,aves,Gallus gallus|chick
  42. The first row is a normal header with human-friendly columns. The 2nd is the robot template row. After that are the usual data rows.
  44. Here "A oboInOwl:hasExactSynonym" in the template row for "synonyms" indicate this column should be interpreted as an owl annotation using that property. Generally the value for annotations is literals/text.
  46. Another common piece of metadata is definitions. For OBO ontologies, IAO must be used here. For non-OBO ontologies people may want to use skos
  48. Some ontologies may need to use other relationships. For part-of parents, use "'part of' some %" (this means that the class indicated by the ID is part-of some X, where X is the value in the part-of column). Use other relationships as appropriate. If you are unclear about the semantics, then consult the docs. You can also work through the docs with the user.
  50. Note that any terms referenced as parents or in logical axioms such as part-of should be in the ontology, so make sure they have rows in the CSV. It's OK to use the label. For example:
  52. identifier,primary_name,parent,madeOf
  53. ID,LABEL,SC %,SC 'made of' some %
  54. VON:1,vehicle,,
  55. VON:2.car,vehicle,wheel|chassis
  56. VON:3,wheel,car part,
  57. VON:4,chassis,car part,
  59. If in doubt, use "A <propertyName>" for metadata and "SC '<relationName>' some %" for logical relationships / graph edges.
  61. If your working dir doesn't contain any object or annotation properties you can make them in a seperate
  62. imported ontology, TYPE is useful for determining the OWL type, for example:
  64. ```
  65. ID,Label,Type,Definition
  66. ID,LABEL,TYPE,A IAO:0000115
  67. IAO:0000115,definition,owl:AnnotationProperty
  68. BFO:0000050,part_of,owl:ObjectProperty
  69. ```
  71. If you need any more detailed documentation, you can fetch it with `fetch_documentation`
  73. You can look at files with `inspect_file`
  75. Use scientific language as far as possible. For IDs, these should be numeric curies unless the user requests otherwise. If the user wants to substitute actual ontology term IDs for these, use lookup_curies_get_lookup_get
  76. """
  79. robot_ontology_agent = Agent(
  80. model="openai:gpt-4o",
  81. deps_type=RobotDependencies,
  82. system_prompt=SYSTEM,
  83. tools=[
  84. Tool(write_and_compile_template, max_retries=2),
  85. Tool(fetch_documentation),
  86. Tool(inspect_file),
  87. Tool(list_files),
  88. Tool(download_url_as_markdown),
  89. ]
  90. )
  93. @robot_ontology_agent.system_prompt
  94. def include_templates_in_prompt(ctx: RunContext[RobotDependencies]) -> str:
  95. if ctx.deps.workdir:
  96. files_names = ctx.deps.workdir.list_file_names()
  97. else:
  98. files_names = []
  99. s = "Working directory files/templates:"
  100. if files_names:
  101. for f in files_names:
  102. s += f"{f}\n---"
  103. s += ctx.deps.workdir.read_file(f)
  104. s += "\n"
  105. return s
  108. @robot_ontology_agent.system_prompt
  109. def include_prefixes_in_prompt(ctx: RunContext[RobotDependencies]) -> str:
  110. pmap = ctx.deps.prefix_map
  111. return f"Prefixes: {pmap}"
  114. def chat(workdir: str, **kwargs):
  115. import gradio as gr
  116. deps = RobotDependencies()
  117. deps.workdir.location = workdir
  119. def get_info(query: str, history: List[str]) -> str:
  120. print(f"QUERY: {query}")
  121. print(f"HISTORY: {history}")
  122. if history:
  123. query += "## History"
  124. for h in history:
  125. query += f"\n{h}"
  126. result = run_sync(lambda: robot_ontology_agent.run_sync(
  127. query, deps=deps, **kwargs))
  128. return result.data
  130. return gr.ChatInterface(
  131. fn=get_info,
  132. type="messages",
  133. title="robot AI Assistant",
  134. examples=[
  135. ["Create an ontology of snacks"],
  136. ]
  137. )

robot_ontology_agent.py at commit d9464b5, under MIT · at the source

Overview

Authors: Shawn Zheng Kai Tan1,2, Aleix Puig-Barbe2, Damien Goutte-Gattat3,4, Caroline Eastwood5, Brian Aevermann6, Alida Avola5, James P Balhoff7, Ismail Ugur Bayindir5, Jasmine Belfiore5, Anita Reane Caron2, David S Fischer8, Nancy George9, Benjamin M Gyori10, Melissa A Haendel11, Charles Tapley Hoyt12, Huseyin Kir5, Tiago Lubiana13, Nicolas Matentzoglu14, James A Overton15, Beverly Peng16
and 15 other authorsBjoern Peters17, Ellen M Quardokus18, Patrick L Ray19, Paola Roncaglia2, Andrea D Rivera5, Ray Stefancsik2, Wei Kheng Teh2, Sabrina Toro11, Nicole Vasilevsky20, Chuan Xu21, Yun Zhang22, Richard H Scheuermann22, Christopher J Mungall23, Alexander D Diehl24, David Osumi-Sutherland5
ORCID iDs: Aleix Puig-Barbe, Damien Goutte-Gattat, James P Balhoff, Anita Reane Caron, Melissa A Haendel, Charles Tapley Hoyt, Nicolas Matentzoglu, Bjoern Peters, Ellen M Quardokus, Patrick L Ray, Paola Roncaglia, Ray Stefancsik, Sabrina Toro, Christopher J Mungall, David Osumi-Sutherland
24 affiliations
  1. SignaMind, Singapore, Singapore
  2. European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1 SD UK
  3. Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY UK
  4. German BioImaging GMB e.V., c/o University of Konstanz, Box 604, 78454 Konstanz, Germany
  5. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1RQ UK
  6. Chan Zuckerberg Initiative, Redwood City, USA
  7. Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC USA
  8. Medical University of Vienna, Institute of Artificial Intelligence, Center for Medical Data Science, Vienna, Austria
  9. Syngenta, Jealott’s Hill, Warfield, Bracknell UK
  10. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA USA
  11. University of North Carolina at Chapel Hill, Chapel Hill, NC USA
  12. RWTH Aachen University, Institute of Inorganic Chemistry, Landoltweg 1a, 52074 Aachen, Germany
  13. University of São Paulo, São Paulo, Brazil
  14. Semanticly, Athens, Greece
  15. Knocean Inc., Toronto, Ontario Canada
  16. Department of Informatics, J. Craig Venter Institute, La Jolla, CA USA
  17. La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037 USA
  18. Indiana University: Bloomington, Indiana, US
  19. Allen Institute for Brain Science, Seattle, WA USA
  20. Critical Path Institute, Tucson, AZ USA
  21. Cambridge Stem Cell Institute and Department of Medicine, University of Cambridge, Trinity Ln, Cambridge UK
  22. Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD USA
  23. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA 94720 USA
  24. Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203 USA
Institutions: European Bioinformatics Institute (United Kingdom); Novo Nordisk (Denmark) (Denmark); University of Konstanz (Germany); University of Cambridge (United Kingdom); German BioImaging – Gesellschaft für Mikroskopie und Bildanalyse; Wellcome Sanger Institute (United Kingdom); Chan Zuckerberg Initiative (United States) (United States); University of North Carolina at Chapel Hill (United States); Renaissance Computing Institute (United States); Medical University of Vienna (Austria); Syngenta (United Kingdom) (United Kingdom); Harvard University (United States); RWTH Aachen University (Germany); Universidade de São Paulo (Brazil); J. Craig Venter Institute (United States); La Jolla Institute for Immunology (United States); Indiana University Bloomington (United States); Indiana University (United States); Allen Institute for Brain Science (United States); Critical Path Institute (United States); Wellcome/MRC Cambridge Stem Cell Institute (United Kingdom); National Institutes of Health (United States); United States National Library of Medicine (United States); Lawrence Berkeley National Laboratory (United States); University at Buffalo, State University of New York (United States)
Journal: Scientific data, volume 13, issue 1, article 946
Dates: received 26 June 2025; accepted 30 March 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07173-8 · PMID 42031777 · PMCID PMC13315338 · OpenAlex W4412785154
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Keywords: Standards, Gene ontology
MeSH: Biological Ontologies*, Single-Cell Analysis*, Humans, Large Language Models, Transcriptome (* major topic)
Topic: Gene Regulatory Network Analysis (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) (# 5RM1 HG010860, HG010859, HG010860, HG012212); U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD) (#5R24OD011883); NIH HHS (R24 OD011883); NHGRI NIH HHS (RM1 HG010860); Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID) (4U19AI118610); U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) (4U19AI118610); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (OT2OD026671, 1UM1MH130981-0, OT2OD033756); ODCDC CDC HHS (R24 OD011883); Wellcome Trust (220540/Z.20/A); Biotechnology and Biological Sciences Research Council (BB/T014008)
Citations: cited by 8 papers (Europe PMC); 141 references in the paper

Abstract

Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets demand robust frameworks for data integration and annotation. The Cell Ontology (CL) has emerged as a pivotal resource for achieving FAIR (Findable, Accessible, Interoperable, and Reusable) data principles by providing standardized, species-agnostic terms for canonical cell types, forming a core component of a wide range of platforms and tools. In this paper, we describe the wide variety of uses of CL in these platforms and tools and detail ongoing work to improve and extend CL content including the addition of transcriptomic types, working closely with major atlasing efforts including the Human Cell Atlas and the Brain Initiative Cell Atlas Network to support their needs. We cover the challenges and future plans for harmonising classical and transcriptomic cell type definitions, integrating markers and using Large Language Models (LLMs) to improve content and efficiency of CL workflows.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

monarch-initiative/aurelian

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d9464b58787cbe40285799208c31640b600fce7e, 16 November 2025
Languages: Python (263), Jupyter (8)
Size: 404 files, 271 scripts
Software Heritage: not archived
Found in: the text, “Integration of large language models”
Holds: README, license file, environment (poetry.lock, pyproject.toml, uv.lock), tests, continuous integration, documentation, 8 notebooks
Not found: CITATION.cff
Tools: RDKit (2 files), pandas (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
273 files
At the source: github.com/monarch-initiative/aurelian

obophenotype/cell-ontology

License: CC-BY-4.0
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Languages: Shell (8), Python (6), Perl (5), JavaScript (2)
Size: 332 files, 21 scripts
Software Heritage: archived
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Holds: README, license file, environment (requirements.txt, src/ontology/Dockerfile), continuous integration, documentation
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Tools: pandas (3 files)
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23 files
At the source: github.com/obophenotype/cell-ontology

shawntanzk/cl-manuscript

License: none: the authors keep all their rights
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Evidence: files inventoried
Commit: ef7d00d684f4e721061d3758606b859619882e0c, 7 January 2026
Languages: Jupyter (2)
Size: 12 files, 2 scripts
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Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), pandas (2 files), NumPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/shawntanzk/cl-manuscript

Cellular-Semantics/CL_KG

License: Apache-2.0
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Evidence: files inventoried
Commit: eb2effcce2d36d38e06d3bb5aa9edf195818e343, 18 May 2026
Languages: Python (47)
Size: 160 files, 47 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (anndata2rdf/docker-compose.yml, anndata2rdf/Dockerfile, anndata2rdf/requirements.txt, bitmap_query_service/Dockerfile, bitmap_query_service/requirements.txt, cl_kb_pipeline/docker-compose.yml, graph_query_service/Dockerfile, graph_query_service/requirements.txt, translator_api_mapper/Dockerfile, translator_api_mapper/requirements.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: pandas (2 files)
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49 files
At the source: github.com/Cellular-Semantics/CL_KG

Zenodo 17543831

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Holds: 1 notebook
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1 file
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Zenodo 4641309

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2 files
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Zenodo 17966975

License: CC-BY-4.0
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Zenodo 10518990

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9 files
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Zenodo 15319198

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2 files
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obasktools/obask

License: Apache-2.0
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Commit: 18f3c6f8e49ecdac742e3074322249d5597c5904, 1 April 2026
Languages: Python (4), Shell (3)
Size: 80 files, 7 scripts
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9 files
At the source: github.com/OBASKTools/obask/tree/v0.0.1

incatools/ubergraph

License: BSD-3-Clause
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Commit: 7b1d8899724f4c932c9240fd675fc5e89085ced1, 18 July 2026
Languages: Shell (1), C++ (1)
Size: 36 files, 2 scripts
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4 files
At the source: github.com/INCATools/ubergraph/tree/v2021-03-26

Code availability

The Cell Ontology is developed, documented and released via a GitHub repository (https://github.com/obophenotype/cell-ontology) implemented using the Ontology Development Kit84. This includes all code and ontology files used in the development of the ontology, available under an open license, as well as a tracker for community requests. Code and data used for stats in this paper can be found at https://github.com/shawntanzk/cl-manuscript.

All code used to build the knowledge-base can be found at https://github.com/Cellular-Semantics/CL_KG.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 11 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 380 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

CL86 can be accessed via a variety of routes. It can be browsed on the Ontology Lookup Service65,66 and Bioportal64,137. A stripped down version of CL with links to markers can also be browsed on CZ CELLxGENE CellGuide (see Fig. 3).

CL is available for download from a set of standard persistent URLs in a variety of forms (see below) and formats (OWL, OBO and OBOgraphs JSON). The main CL release is available from:

http://purl.obolibrary.org/obo/cl.{obo/owl/json}

All other releases are available via

http://purl.obolibrary.org/obo/cl/cl-{form}.{obo/owl/json}

Each download also includes a version URL—a permanently resolvable link to the version that can be used to reference versions used in analysis and annotation.

e.g.

http://purl.obolibrary.org/obo/cl/releases/2025-12-17/cl.owl

Downloads available by content (form):

• cl - The Cell Ontology plus merged imports, including from Uberon and the Gene Ontology

• cl-plus - combines the full Cell Ontology with the provisional cell ontology

• cl-base - The Cell Ontology with links to external ontologies recorded as bare IRIs. This is designed to be combined with other base ontologies to make integrated products and for use in generating import modules from CL.

• cl-simple - only CL terms and their relationships. No imports

• cl-basic - only CL terms and their relationships. Additionally the graph is guaranteed to lack cycles (these are perfectly legal in OWL but removing them makes it easier for some software to operate on it).

Each release also comes with a detailed, automated report of changes since the last release.

CL is also available as part of ‘composite-metazoan’—an ontology that combines, Uberon, CL and species-specific ontologies: Drosophila Anatomy Ontology (FBbt); C. elegans Gross Anatomy Ontology (WBbt); Zebrafish Anatomy and Development Ontology (ZFA); Xenopus Anatomy Ontology (XAO); Mouse Adult Gross Anatomy (MA) and Mouse Developmental Anatomy Ontology (EMAPA); Human Developmental Anatomy (EHDAA2); and the ontologies derived from the Allen Institute's brain atlases. The composite ontology is available at http://purl.obolibrary.org/obo/uberon/composite-metazoan.owl.

Programmatic access is available via the Ontology Lookup Service API (https://www.ebi.ac.uk/ols4/api/ontologies/cl/), the Bioportal API (see http://data.bioontology.org/documentation), via the UberGraph68 SPARQL endpoint (https://ubergraph.apps.renci.org/sparql), and via the Ontology Access Kit (OAK). UberGraph supports queries of all asserted and inferred relationships between terms in CL and terms in a broad range of OBO-Foundry ontologies that reference CL, including the Gene Ontology, Uberon, Human Phenotype Ontology and MONDO. A REST API is also available, featuring pre-rolled template queries of Ubergraph, for example allowing users to query for all cell types in a specific anatomical structure (https://grlc.io/api-git/INCATools/ubergraph/subdir/sparql).

The Cell Ontology is developed, documented and released via a GitHub repository (https://github.com/obophenotype/cell-ontology) implemented using the Ontology Development Kit84. This includes all code and ontology files used in the development of the ontology, available under an open license, as well as a tracker for community requests. Code and data used for stats in this paper can be found at https://github.com/shawntanzk/cl-manuscript.

All code used to build the knowledge-base can be found at https://github.com/Cellular-Semantics/CL_KG.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 35 authors, 2 keywords, 5 MeSH terms, 10 funders, 129 references.

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This paper

Tan, S. Z. K., Puig-Barbe, A., Goutte-Gattat, D., Eastwood, C., Aevermann, B., Avola, A., Balhoff, J. P., Bayindir, I. U., Belfiore, J., Caron, A. R., Fischer, D. S., George, N., Gyori, B. M., Haendel, M. A., Hoyt, C. T., Kir, H., Lubiana, T., Matentzoglu, N., Overton, J. A., . . . Osumi-Sutherland, D. (2026). The Cell Ontology in the age of single-cell omics. Scientific data, 13(1), 946. https://doi.org/10.1038/s41597-026-07173-8

BibTeX

@article{tan2026cell,
author = {Tan, Shawn Zheng Kai and Puig-Barbe, Aleix and Goutte-Gattat, Damien and Eastwood, Caroline and Aevermann, Brian and Avola, Alida and Balhoff, James P and Bayindir, Ismail Ugur and Belfiore, Jasmine and Caron, Anita Reane and Fischer, David S and George, Nancy and Gyori, Benjamin M and Haendel, Melissa A and Hoyt, Charles Tapley and Kir, Huseyin and Lubiana, Tiago and Matentzoglu, Nicolas and Overton, James A and Peng, Beverly and Peters, Bjoern and Quardokus, Ellen M and Ray, Patrick L and Roncaglia, Paola and Rivera, Andrea D and Stefancsik, Ray and Teh, Wei Kheng and Toro, Sabrina and Vasilevsky, Nicole and Xu, Chuan and Zhang, Yun and Scheuermann, Richard H and Mungall, Christopher J and Diehl, Alexander D and Osumi-Sutherland, David},
title = {{The Cell Ontology in the age of single-cell omics}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {946},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07173-8},
url = {https://doi.org/10.1038/s41597-026-07173-8},
pmid = {42031777},
pmcid = {PMC13315338}
}

RIS

TY - JOUR
AU - Tan, Shawn Zheng Kai
AU - Puig-Barbe, Aleix
AU - Goutte-Gattat, Damien
AU - Eastwood, Caroline
AU - Aevermann, Brian
AU - Avola, Alida
AU - Balhoff, James P
AU - Bayindir, Ismail Ugur
AU - Belfiore, Jasmine
AU - Caron, Anita Reane
AU - Fischer, David S
AU - George, Nancy
AU - Gyori, Benjamin M
AU - Haendel, Melissa A
AU - Hoyt, Charles Tapley
AU - Kir, Huseyin
AU - Lubiana, Tiago
AU - Matentzoglu, Nicolas
AU - Overton, James A
AU - Peng, Beverly
AU - Peters, Bjoern
AU - Quardokus, Ellen M
AU - Ray, Patrick L
AU - Roncaglia, Paola
AU - Rivera, Andrea D
AU - Stefancsik, Ray
AU - Teh, Wei Kheng
AU - Toro, Sabrina
AU - Vasilevsky, Nicole
AU - Xu, Chuan
AU - Zhang, Yun
AU - Scheuermann, Richard H
AU - Mungall, Christopher J
AU - Diehl, Alexander D
AU - Osumi-Sutherland, David
TI - The Cell Ontology in the age of single-cell omics
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/24
VL - 13
IS - 1
SP - 946
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07173-8
UR - https://doi.org/10.1038/s41597-026-07173-8
LA - en
ER -

CSL-JSON

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"issued": {
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}
}

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