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Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data
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 bacteri...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3462729/ https://www.ncbi.nlm.nih.gov/pubmed/22873695 http://dx.doi.org/10.1186/1471-2105-13-193 |
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author | Tintle, Nathan L Sitarik, Alexandra Boerema, Benjamin Young, Kylie Best, Aaron A DeJongh, Matthew |
author_facet | Tintle, Nathan L Sitarik, Alexandra Boerema, Benjamin Young, Kylie Best, Aaron A DeJongh, Matthew |
author_sort | Tintle, Nathan L |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3462729 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34627292012-10-03 Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data Tintle, Nathan L Sitarik, Alexandra Boerema, Benjamin Young, Kylie Best, Aaron A DeJongh, Matthew BMC Bioinformatics Research Article 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. BioMed Central 2012-08-08 /pmc/articles/PMC3462729/ /pubmed/22873695 http://dx.doi.org/10.1186/1471-2105-13-193 Text en Copyright ©2012 Tintle et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Tintle, Nathan L Sitarik, Alexandra Boerema, Benjamin Young, Kylie Best, Aaron A DeJongh, Matthew Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data |
title | Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data |
title_full | Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data |
title_fullStr | Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data |
title_full_unstemmed | Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data |
title_short | Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data |
title_sort | evaluating the consistency of gene sets used in the analysis of bacterial gene expression data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3462729/ https://www.ncbi.nlm.nih.gov/pubmed/22873695 http://dx.doi.org/10.1186/1471-2105-13-193 |
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