ArticleBMC bioinformatics2008
Conserved co-expression for candidate disease gene prioritization.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-coexpression conservation analysis of microarray data: a "subset" approach provides insight into brain-derived neurotrophic factor regulation.BMC genomics · 2009Pooled it
- Contribution of model organism phenotypes to the computational identification of human disease genes.Disease models & mechanisms · 2022Article
- Identification of an AP1-ZFP36 Regulatory Network Associated with Breast Cancer Prognosis.Journal of mammary gland biology and neoplasia · 2020Article
- Modeling and analysis of RNA-seq data: a review from a statistical perspective.Quantitative biology (Beijing, China) · 2018Article
- ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index.Plant & cell physiology · 2018Article
- ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression.Plant & cell physiology · 2016Article
- WWOX at the crossroads of cancer, metabolic syndrome related traits and CNS pathologies.Biochimica et biophysica acta · 2014Review
- ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants.Plant & cell physiology · 2014Article
- Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs).BMC bioinformatics · 2013Article
- Genes, behavior and next-generation RNA sequencing.Genes, brain, and behavior · 2013Review
- COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals.Nucleic acids research · 2013Article
- Gene networks and haloperidol-induced catalepsy.Genes, brain, and behavior · 2012Article
- An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction.European journal of human genetics : EJHG · 2011Article
- ATTED-II updates: condition-specific gene coexpression to extend coexpression analyses and applications to a broad range of flowering plants.Plant & cell physiology · 2011Article
- COXPRESdb: a database to compare gene coexpression in seven model animals.Nucleic acids research · 2011Article
- Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse.BMC genomics · 2010Article
- Application of genetic/genomic approaches to allergic disorders.The Journal of allergy and clinical immunology · 2010Review
- ORTom: a multi-species approach based on conserved co-expression to identify putative functional relationships among genes in tomato.Plant molecular biology · 2010Article
- Network-based Identification of novel cancer genes.Molecular & cellular proteomics : MCP · 2010Article
- Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network.Genome biology · 2009Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone.
resultsWe use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms.
conclusionWe find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools.
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