Expression Analysis to Identify a Set of Genes for the Improvement of RNA-seq Data Normalization
Open AccessCategorizing protein-coding genes and understanding their expression patterns is expected to provide insight into complex regulatory networks. This can result in the identification of genes relevant to specific biological processes, including disease. In this study, a quantitative transcriptomics analysis was applied to RNA-Seq data to identify the genes normally expressed in a representative set of all major human and mouse tissues. Following analysis, Fagerberg's approach was used to classify the genes expressed across all available major human and mouse tissues as analyzed from normal (non-diseased) human and mouse samples, as well as normal adjacent to tumor (paired normal) human samples. Based on the selected threshold for the Fagerberg's method, the consistently and highly expressed genes in the common human and mouse tissues were prioritized for further analysis. Furthermore, differential expression of genes in cancer was retrieved from the BioXpress database to investigate if the expression of these genes changed in cancer. The consistently and highly expressed genes were mapped to Gene Ontology (GO) terms to reveal the biological significance of this gene set. In summary, transcripts were detected for 8420, 6934, and 8789 genes in all normal tissues in the three datasets (normal healthy RNA-seq data for human and mouse and human solid tissue normal RNA-seq data, respectively) used in this study. Moreover, twelve common consistently and highly expressed genes (ATP5F1B, PFN1, CTSD, UBB, RPL8, ACTB, MT-ND1, PSAP, HLA-B, EEF2, RPL13A, B2M) among six tissues (liver, kidney, colon, lung, stomach, and urinary bladder) in human and mouse were identified. Nine of these genes were not significantly differentially expressed in cancer and the remaining three genes were only significantly differentially expressed in some stomach, lung, and urinary bladder cancer. Gene Ontology (GO) enrichment analysis demonstrated that most of these genes are involved in extracellular exosome and membrane. The twelve genes showed consensus with recent studies reporting a list of universally expressed genes across all normal tissues. These findings suggest that the small set of consistently and highly expressed genes may be an appropriate choice for normalizing gene expression data.
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