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Updated: Jun 23, 2026

Bacterial Gene Expression Analysis Using Microarrays

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

Text-based over-representation analysis of microarray gene lists with annotation bias.

Hui Sun Leong¹, David Kipling

¹Department of Pathology, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK.

Nucleic Acids Research

|May 12, 2009

Summary

Over-representation analysis (ORA) for gene lists faces bias from research patterns. New methods adapt ORA for free-text mining, revealing insights beyond predefined ontologies like Gene Ontology (GO).

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Functional interpretation of gene lists from microarray data is challenging.
Over-representation analysis (ORA) using hypergeometric tests is common but limited to predefined terminologies like Gene Ontology (GO) and KEGG.
Experimentally derived gene lists exhibit bias due to differential research activity, problematic for standard ORA.

Purpose of the Study:

To explore applying ORA to free-text mining beyond predefined ontologies.
To develop and demonstrate novel approaches to overcome bias in ORA for gene list interpretation.
To compare free-text mining insights with GO-based analysis.

Main Methods:

Developed three novel approaches to address bias in ORA.
Applied and validated these methods on diverse published datasets across species.
Compared results with existing GO term-based analysis tools.

Main Results:

The developed methods successfully overcome the bias in experimentally derived gene lists.
Free-text mining of PubMed abstracts revealed biological insights not captured by GO alone.
Demonstrated usability across various species and datasets.

Conclusions:

ORA can be effectively extended to mine free-text, offering broader biological interpretation.
Novel bias-correction methods enhance ORA's applicability to complex gene lists.
Mining PubMed abstracts provides complementary and potentially novel biological insights compared to ontology-based approaches.