ArticleMolecular genetics and genomics : MGG2014
Co-expression network analysis identifies transcriptional modules in the mouse liver.
Article in Molecular genetics and genomics : MGG, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 17 citations in OpenAlex.
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- Evolution of Epistatic Networks and the Genetic Basis of Innate Behaviors.Trends in genetics : TIG · 2020Review
- Mining conditions specific hub genes from RNA-Seq gene-expression data via biclustering and their application to drug discovery.IET systems biology · 2019Article
- COSCEB: Comprehensive search for column-coherent evolution biclusters and its application to hub gene identification.Journal of biosciences · 2019Article
- Sex-Biased lncRNAs Inversely Correlate With Sex-Opposite Gene Coexpression Networks in Diversity Outbred Mouse Liver.Endocrinology · 2019Article
- Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis.Oncology letters · 2018Article
- FromBiomedical reports · 2017Article
- Whole-transcriptome analysis delineates the human placenta gene network and its associations with fetal growth.BMC genomics · 2017Article
- Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice.Genetics · 2017Article
- Identification of Transcriptional Modules and Key Genes in Chickens Infected withBioMed research international · 2017Article
- Gene Co-Expression Network Analysis Reveals the Correlation Patterns Among Genes in Euryhaline Adaptation of Crassostrea gigas.Marine biotechnology (New York, N.Y.) · 2016Article
- TMEM120A and B: Nuclear Envelope Transmembrane Proteins Important for Adipocyte Differentiation.PloS one · 2015Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mouse liver transcriptome has been extensively studied but little is known about the global hepatic gene network of the mouse under normal physiological conditions. Understanding this will help reveal the transcriptional organization of the liver and elucidate its functional complexity. Here, weighted gene co-expression network analysis (WGCNA) was carried out to explore gene co-expression networks using large-scale microarray data from normal mouse livers. A total of 7,203 genes were parsed into 16 gene modules associated with protein catabolism, RNA processing, muscle contraction, transcriptional regulation, oxidation reduction, sterol biosynthesis, translation, fatty acid metabolism, immune response and others. The modules were organized into higher order co-expression groups. Hub genes in each module were found to be critical for module function. In sum, the analyses revealed the gene modular map of the mouse liver under normal physiological condition. These results provide a systems-level framework to help understand the complexity of the mouse liver at the molecular level, and should be beneficial in annotating uncharacterized genes.
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