Gene Expression Analysis and Profiling of Microarrays Data and RNA-Sequencing Data

Abstract Current genome-wide studies of gene expression are achieved using two major omic technologies: high-density oligonucleotide microarrays and deep RNA sequencing. These high-throughput experimental techniques allow the detection of most known genes and are providing global gene expression profiles and gene signatures for normal and pathological states of multiple biological systems, including many human samples and cell types. At present, microarrays technology is still better established and more widely used than RNA sequencing and has provided the most gene expression data. Most analyses of the human transcriptome focus on the identification and characterization of protein-coding genes; however, the complexity of the human transcriptomic system has been found to be much more than expected, and we still do not have a clear genome-wide compendium of the genes that are active in each human tissue and cell type. Development and application of adequate bioinformatic methods is the only way to achieve a proper use of the omic-wide gene expression datasets. Thorough analysis and integration of omic studies is essential to achieve an unbiased global characterization of the active human transcriptome. In this chapter we present and describe several important concepts in modern transcriptomics and bioinformatic methods to analyze genome-wide data mainly derived from microarrays technology but also from deep-sequencing technology, in both cases applied to gene expression measurements.