Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome
Year: 2014
Type: article
Abstract: Gene expression profiling of psoriasis has driven research advances and may soon provide the basis for clinical applications. For expression profiling studies, RNA-seq is now a competitive technology, but RNA-seq results may differ from those obtained by microarray. We therefore compared findings obtained by RNA-seq with those from eight microarray studies of psoriasis. RNA-seq and microarray datasets identified similar numbers of differentially expressed genes (DEGs), with certain genes uniquely identified by each technology. Correspondence between platforms and the balance of increased to decreased DEGs was influenced by mRNA abundance, GC content, and gene length. Weakly expressed genes, genes with low GC content, and long genes were all biased toward decreased expression in psoriasis lesions. The strength of these trends differed among array datasets, most likely due to variations in RNA quality. Gene length bias was by far the strongest trend and was evident in all datasets regardless of the expression profiling technology. The effect was due to differences between lesional and uninvolved skin with respect to the genome-wide correlation between gene length and gene expression, which was consistently more negative in psoriasis lesions. These findings demonstrate the complementary nature of RNA-seq and microarray technology and show that integrative analysis of both data types can provide a richer view of the transcriptome than strict reliance on a single method alone. Our results also highlight factors affecting correspondence between technologies, and we have established that gene length is a major determinant of differential expression in psoriasis lesions.
Source: Physiological Genomics
Authors: William R. Swindell, Xianying Xing, John J. Voorhees, James T. Elder, Andrew Johnston, Johann E. Gudjonsson
Institutions: University of Michigan, Michigan Medicine
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G1291235549, Biologic Activities of IL-36 Cytokines in Psoriasis, Identification of Psoriatic Arthritis Genes, G348914924, Linkage Analysis of Familial Psoriasis, Functional Genomics of Psoriasis, G5213153879, Role of the Psoriasis Associated IL23R Risk Variants on Th17 Biology and Function, Combined Analysis of Gene Expression and DNA Variation in Psoriasis
OpenAlex ID: W2331275800