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Genome Annotation and Assembly

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Related Experiment Video

Updated: Jun 21, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Annotating the human genome with Disease Ontology.

John D Osborne¹, Jared Flatow, Michelle Holko

¹Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA. ozborn@uab.edu

|July 15, 2009

Summary

This study introduces a novel computational approach to annotate the human genome with disease information. Our method significantly enhances disease identification accuracy and coverage compared to existing databases.

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Last Updated: Jun 21, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

The human genome is well-annotated for biological functions but lacks comprehensive computational disease annotations.
Accurate gene-disease relationship identification is crucial for understanding genetic disorders.

Purpose of the Study:

To develop and validate a computational method for discovering and annotating gene-disease relationships.
To improve the coverage and accuracy of human genome disease annotation.

Main Methods:

Utilized the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to extract gene-disease relationships from the GeneRIF database.
Employed a disease-focused subset of UMLS, the Disease Ontology, to filter and interpret MMTx results.
Validated findings against the Homayouni gene collection and compared them with Online Mendelian Inheritance in Man (OMIM) annotations.

Main Results:

Achieved a 91% recall rate and 97% precision rate for disease annotation using GeneRIF.
Demonstrated significantly higher recall (91% vs. 22%) compared to OMIM annotations, while maintaining high precision (97% vs. 98%).
The thesaurus-based approach improved disease identification accuracy through synonym matching.

Conclusions:

Annotating the human genome with Disease Ontology and GeneRIF dramatically increases disease coverage.
The developed method offers a more accurate and comprehensive approach to disease annotation in the human genome.
This approach facilitates comparisons between disease databases and enhances precision in disease identification.