A Module-Based Approach for Evaluating Differential Genome-Wide Expression Profiles

Abstract

Transcription is the process of making an RNA copy of a gene sequence. Next, this copy (mRNA) is then translated into proteins. Proteins dictates the expected behavior inside the cells and are required for the structure, function, and regulation of the body's tissues and organs. Together, transcription and translation are known as gene expression. Transcriptograms are basically defined as "images" of gene expression data of genomes, by generating expression profiles for transcriptomes. They allow to assess cell metabolism, being capable of discriminating the stage the cell is going through at a given instant, as well as pointing metabolic changes in altered cellular states as compared to a control state, independently of the transcriptome profilling protocol. Though, they cannot highlight differential expression profiles. We present a new possibility of RNA-Seq data analysis using Transcriptograms for discovering module-based differential expression profiles. We demonstrate its practical application while obtaining more specific gene signatures as well as functional annotations, closely related to biomedical context. Moreover, these signatures are also enriched by survival cancer analysis.