# Gene Ontology Consortium and Knowledgebase

> **NIH NIH U24** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $2,603,881

## Abstract

Project Summary/Abstract
Because of the staggering complexity of biological systems, biomedical research is becoming increasingly
dependent on knowledge stored in a computable form. The Gene Ontology (GO) is by far the largest
knowledgebase of how genes function, and has become a critical component of the computational
infrastructure enabling the genomic revolution. The GO knowledgebase encodes a computational model of
biological systems using modern semantic technologies, and this is the key to its broad adoption and
application. It stores vastly more knowledge than one person can know, and therefore enables computational
analyses that would otherwise be impossible. It has become indispensable in the interpretation of large-scale
molecular measurements in biological research. Crucially for human health research, GO is also one of a suite
of complementary ontologies constructed in such a way to maximally promote interoperability and
comparability of data sets. It represents the gene functions and biological processes that can be perturbed in
human disease, helping researchers or clinicians to identify genetic contributions to disease.
GO is a knowledgebase that can be statistically mined, either standalone or in combination with data from
other knowledge resources, which enables researchers to discover connections and form new hypotheses
from the biological networks GO represents. All knowledge in GO is represented using semantic web
technologies and so is amenable to computational integration and consistency checking.
To ensure the knowledge environment meets the requirements of biomedical researchers, we will: 1) Develop
and refine the Gene Ontology to reflect current biological knowledge; 2) Coordinate, integrate, and provide GO
assertions from multiple sources; 3) Enhance usability of the GO resources for multiple research communities.
We will extend the reach of our Consortium of contributors, to efficiently expand the content of the
knowledgebase, and develop test sets and challenges to spur the development of machine learning methods
for knowledge capture. Our aims reflect the essential requirements for realizing the overarching objectives for a
biomedical knowledgebase: efficiently capturing and integrating biological knowledge and adhering to the
highest possible standard for accuracy and detail; constructing and providing a robust, flexible, powerful, and
extensible technological infrastructure available not only for internal use but just as easily by the wider
community; and lastly, leveraging state-of-the-art social media, web services and other technologies to
disseminate the GO resource to the entire biomedical research community.

## Key facts

- **NIH application ID:** 10348001
- **Project number:** 1U24HG012212-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** J. Michael Cherry
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,603,881
- **Award type:** 1
- **Project period:** 2022-06-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10348001

## Citation

> US National Institutes of Health, RePORTER application 10348001, Gene Ontology Consortium and Knowledgebase (1U24HG012212-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10348001. Licensed CC0.

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