Gene expression rate comparison for multiple high-throughput datasets

Microarray provides genome-wide transcript profiles, whereas RNA-seq is an alternative approach applied for transcript discovery and genome annotation. Both high-throughput techniques show quantitative measurement of gene expression. To explore differential gene expression rates and understand biological functions, the authors designed a system which utilises annotations from Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways and Gene Ontology (GO) associations for integrating multiple RNA-seq or microarray datasets. The developed system is initiated by either estimating gene expression levels from mapping next generation sequencing short reads onto reference genomes or performing intensity analysis from microarray raw images. Normalisation procedures on expression levels are evaluated and compared through different approaches including Reads Per Kilobase per Million mapped reads (RPKM) and housekeeping gene selection. Such gene expression levels are shown in different colour shades and graphically displayed in designed temporal pathways. To enhance importance of functional relationships of clustered genes, representative GO terms associated with differentially expressed gene cluster are visually illustrated in a tag cloud representation.