A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide
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Motivation: Approximately 9334 (37%) of Human genes have no publications documenting their function and, for those that are published, the number of publications per gene is highly skewed. Furthermore, for reasons not clear, the entry of new gene names into the literature has slowed in recent years. If we are to better understand human/mammalian biology and complete the catalog of human gene function, it is important to finish predicting putative functions for these genes based upon existing experimental evidence. Results: A global meta-analysis (GMA) of all publicly available GEO two-channel human microarray datasets (3551 experiments total) was conducted to identify genes with recurrent, reproducible patterns of co-regulation across different conditions. Patterns of co-expression were divided into parallel (i.e. genes are up and down-regulated together) and anti-parallel. Several ranking methods to predict a gene's function based on its top 20 co-expressed gene pairs were compared. In the best method, 34% of predicted Gene Ontology (GO) categories matched exactly with the known GO categories for ∼5000 genes analyzed versus only 3% for random gene sets. Only 2.4% of co-expressed gene pairs were found as co-occurring gene pairs in MEDLINE. Conclusions: Via a GO enrichment analysis, genes co-expressed in parallel with the query gene were frequently associated with the same GO categories, whereas anti-parallel genes were not. Combining parallel and anti-parallel genes for analysis resulted in fewer significant GO categories, suggesting they are best analyzed separately. Expression databases contain much unexpected genetic knowledge that has not yet been reported in the literature. A total of 1642 Human genes with unknown function were differentially expressed in at least 30 experiments. Availability: Data matrix available upon request. Contact: jdwren@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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Meta-analysis across many datasets often improves performance dramatically.
“However, meta-analysis across many datasets often improves performance dramatically (Gillis and Pavlidis, 2011a;Lee et al., 2004;Wren, 2009).”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Wren, 2009
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Guidance for RNA-seq co-expression network construction and analysis: safety in numbers
Ballouz, S., Verleyen, W., & Gillis, J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv118 · Cited by 215 · see citing works
- Authors
- Wren, J. D.
- Affiliations
- Oklahoma Medical Research Foundation (US)
- Corresponding author
- Wren, Jonathan D.
- ORCID
- Wren, Jonathan D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-05-15
- Volume
- 25
- Issue
- 13
- Pages
- 1694-1701
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2732319
- OpenAlex
- W2150429594
- Cited by
- 95
- References
- 1
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 4 · 2020: 4 · 2019: 7 · 2018: 4 · 2017: 5 · 2016: 7 · 2015: 10 · 2014: 8 · 2013: 7 · 2012: 6
- FWCI
- 0.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (P30AR053483)
- National Center for Research Resources (P20RR020143)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene function prediction, gene co-expression, gene co-regulation, gene ontology enrichment, differential gene expression
- MeSH terms
- Genes, Humans, Publications, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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