Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans' Gene Expression Data. Issue 10 (1st October 2020)

• Record Type: Journal Article
• Authors: Cary, Michael; Podshivalova, Katie; Kenyon, Cynthia
• Is Part Of: G3. Volume 10:Issue 10(2020)
• Page Start: 3623
• Page End: 3638
• Publication Date: 2020-10-01
• Subjects: gene expression data analysis -- microarray -- functional annotation of genes -- gene co-expression -- independent component analysis -- aging -- mitochondrial unfolded protein response -- respiration -- hif-1 -- atfs-1; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.120.401270
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans . These modules represent responses to changes in the environment ( e.g., starvation, exposure to xenobiotics), genes regulated by transcriptions factors ( e.g., ATFS-1, DAF-16 ), genes specific to tissues ( e.g., neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation ( e.g., GO term). Analysis of "module-weighted annotations" improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource with tutorials at http://genemodules.org/, in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own gene expression data (generated using any platform) or gene sets of interest.