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Related Experiment Video

Updated: Jun 4, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

An ontology-neutral framework for enrichment analysis.

Rob Tirrell¹, Uday Evani, Ari E Berman

¹Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 25, 2011

Summary

This summary is machine-generated.

This study introduces RANSUM, a tool for enrichment analysis using any biomedical ontology. RANSUM enhances the interpretation of high-throughput biological data by enabling broader annotation and analysis beyond the Gene Ontology.

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Last Updated: Jun 4, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput biological assays generate large datasets, often yielding lists of significant genes.
Interpreting these gene lists typically involves enrichment analysis using Gene Ontology (GO) terms to identify over- or under-represented biological concepts.
Current methods are often limited to GO, restricting the scope of analysis.

Purpose of the Study:

To present RANSUM (Rich Annotation Summarizer), a novel tool for performing enrichment analysis.
To enable enrichment analysis using any ontology available through the National Center for Biomedical Ontology (NCBO) BioPortal.
To extend the application of enrichment analysis to diverse high-throughput data types beyond gene expression.

Main Methods:

RANSUM performs enrichment analysis by profiling gene sets against user-selected ontologies.
The tool integrates with the NCBO BioPortal for access to a wide range of biomedical ontologies.
Methodology addresses challenges in enrichment analysis and explores novel analytical approaches.

Main Results:

RANSUM facilitates enrichment analysis using any ontology, not just GO.
The tool supports various high-throughput data modalities including proteomics and metabolomics.
Demonstrates the extensibility of enrichment analysis to broader biological contexts.

Conclusions:

RANSUM significantly broadens the applicability of enrichment analysis in biological research.
The tool allows for more comprehensive data interpretation by leveraging diverse biomedical ontologies.
Enables novel insights from high-throughput data analysis across multiple omics fields.