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Related Experiment Video

Updated: Jul 13, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Interpreting microarray results with gene ontology and MeSH.

John D Osborne¹, Lihua Julie Zhu, Simon M Lin

¹Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA.

Methods in Molecular Biology (Clifton, N.J.)

|July 20, 2007

Summary

This study presents a workflow for interpreting gene lists from microarray experiments. It details converting gene identifiers and searching gene ontology (GO) and Medical Subject Headings (MeSH) for biological insights.

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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

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Published on: August 15, 2019

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

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Published on: April 8, 2017

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Microarray experiments generate large gene lists requiring interpretation.
Standardized methods are needed for analyzing and understanding gene expression data.
Ontologies like Gene Ontology (GO) and Medical Subject Headings (MeSH) provide structured vocabularies for biological information.

Purpose of the Study:

To describe a comprehensive workflow for interpreting gene lists derived from microarray experiments.
To demonstrate the conversion of gene identifiers using SOURCE and MatchMiner.
To illustrate the application of GO and MeSH ontologies for biological data mining.

Main Methods:

Gene lists from microarray experiments were processed.
Gene identifiers were converted using SOURCE and MatchMiner.
Converted gene lists were queried against GO and MeSH ontologies.
Tools such as DAVID, EASE, and GOMiner were used for GO enrichment analysis.
High-density array pattern interpreter was employed for MeSH mining.

Main Results:

A practical workflow for gene list interpretation was established.
Successful conversion of gene identifiers facilitated ontology searching.
Enrichment analysis using GO identified biological functions and pathways.
MeSH mining provided insights into disease associations and biological concepts.

Conclusions:

The described workflow enables effective interpretation of microarray gene lists.
Utilizing ontologies like GO and MeSH enhances biological discovery from gene expression data.
This approach provides a robust framework for researchers analyzing genomic data.