title: Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data.
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.
authors.affiliation: Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA 51250, USA. ntintle@dordt.edu
authors.forename: Nathan L
authors.initials: NL
authors.lastname: Tintle
authors.forename: Alexandra
authors.initials: A
authors.lastname: Sitarik
authors.forename: Benjamin
authors.initials: B
authors.lastname: Boerema
authors.forename: Kylie
authors.initials: K
authors.lastname: Young
authors.forename: Aaron A
authors.initials: AA
authors.lastname: Best
authors.forename: Matthew
authors.initials: M
authors.lastname: Dejongh
mesh_terms: D001120:Arginine / Q000096:biosynthesis; D001419:Bacteria / Q000235:genetics / Q000378:metabolism; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D005798:Genes, Bacterial; D016680:Genome, Bacterial; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D059467:Transcriptome
chemical_list: D001120:Arginine