Gene expression rate comparison for multiple high-throughput datasets

• Published in: IET systems biology 2013-10, Vol.7 (5), p.135-142
• Main Authors: Chen, Chien-Ming, Shih, Tsan-Huang, Pai, Tun-Wen, Liu, Zhen-Long, Chang, Margaret Dah-Tsyr, Hu, Chin-Hwa
• Format: Article
• Language: English
• Subjects: Algorithms; Annotations; Biological; Clouds; colour shades; Databases, Genetic; Estimating; gene cluster; Gene expression; gene expression levels; Gene Expression Profiling; gene expression rate; Gene Expression Regulation; Gene Expression Regulation, Fungal; gene ontology associations; Genes; genetics; Genome; genome annotation; genome biological pathways; Genomes; genome‐wide transcript profiles; genomics; High-Throughput Nucleotide Sequencing - methods; high‐throughput techniques; housekeeping gene selection; lab‐on‐a‐chip; microarray datasets; microarray raw images; molecular biophysics; molecular configurations; Multigene Family; multiple high‐throughput datasets; multiple RNA‐seq datasets; next generation sequencing; Oligonucleotide Array Sequence Analysis - methods; Pathways; quantitative measurement; representative GO terms; RNA; RNA, Fungal; Saccharomyces cerevisiae; Sequence Analysis, RNA; Signal Transduction; Special Issue: Selected papers from the 6th IEEE International Conference on Systems Biology; Systems Biology; tag cloud representation; temporal pathways; Time Factors; transcript discovery
• ISSN: 1751-8849; 1751-8857; 1751-8857
• DOI: 10.1049/iet-syb.2012.0060

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.