# Resource Project

> **NIH NIH U41** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $1,347,701

## Abstract

PROJECT SUMMARY
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. It has become nearly indispensible 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 as way to maximally promote interoperability and
comparability of data sets. It represents the gene functions and biological processes that are perturbed in
human disease, e.g. via the links from Human Phenotype Ontology (HPO) class abnormality of lipid
metabolism, defined in relation to the GO class lipid metabolic process (GO_0006629), researchers or
clinicians can find the set of genes that are known to be involved in this process.
GO is a knowledge resource that can be statistically mined, either standalone or in combination with data from
other knowledge resources, which enables experts 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. The proposed GO knowledge
environment will enable a wider community of scientists to contribute to, and to utilize, a common, computable
representation of biology.
To ensure the knowledge environment meets the requirements of biomedical researchers, we will: a) deliver a
comprehensive, detailed, computable knowledgebase of gene function, encoded in the Gene Ontology and
annotations (computer-readable statements about the how specific genes function), focusing on human
biology; b) provide a “hub” for a broad community of scientists to collaboratively extend, correct and improve
the knowledgebase; c) ensure the GO knowledge resource is of the highest quality with regards to depth,
breadth and accuracy; d) facilitate the transfer of insights obtained from studies of non-human organisms, such
as the mouse and zebrafish, to human biology; and e) enable the scientific community to use the
knowledgebase in analyses of large-scale genetic and -omics data. Our aims reflect the essential requirements
for realizing the overarching objectives for a biomedical data resource: 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:** 9930124
- **Project number:** 5U41HG002273-20
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Paul D. Thomas
- **Activity code:** U41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,347,701
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9930124

## Citation

> US National Institutes of Health, RePORTER application 9930124, Resource Project (5U41HG002273-20). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9930124. Licensed CC0.

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