ArticleBMC bioinformatics2012
Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- IMPROVED PERFORMANCE OF GENE SET ANALYSIS ON GENOME-WIDE TRANSCRIPTOMICS DATA WHEN USING GENE ACTIVITY STATE ESTIMATES.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- A Bayesian Framework for the Classification of Microbial Gene Activity States.Frontiers in microbiology · 2016Article
- Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation.Frontiers in microbiology · 2016Article
- Novel gene sets improve set-level classification of prokaryotic gene expression data.BMC bioinformatics · 2015Article
- Cautions about the reliability of pairwise gene correlations based on expression data.Frontiers in microbiology · 2015Article
- The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST).Nucleic acids research · 2014Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStatistical 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.
resultsWe 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.
conclusionsDespite 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.
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