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Open Access Research article

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

Nathan L Tintle1*, Alexandra Sitarik2, Benjamin Boerema3, Kylie Young4, Aaron A Best5 and Matthew DeJongh6

Author affiliations

1 Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA, 51250, USA

2 Department of Biostatistics, University of Michigan, Ann Arbor, MI, 43109, USA

3 Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, 48109, USA

4 Department of Mathematics, Hope College, Holland, MI, 49423, USA

5 Department of Biology, Hope College, Holland, MI, 49423, USA

6 Department of Computer Science, Hope College, Holland, MI, 49423, USA

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Citation and License

BMC Bioinformatics 2012, 13:193  doi:10.1186/1471-2105-13-193

Published: 8 August 2012

Abstract

Background

Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed.

Results

We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size.

Conclusions

Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

Keywords:
Gene ontology; KEGG; SEED; Operons; Consistency