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.
Chien-Ming
CM
Chen
Tsan-Huang
TH
Shih
Tun-Wen
TW
Pai
Zhen-Long
ZL
Liu
Margaret Dah-Tsyr
MD
Chang
Chin-Hwa
CH
Hu
D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015966:Gene Expression Regulation, Fungal; D016678:Genome; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012331:RNA, Fungal; D012441:Saccharomyces cerevisiae; D017423:Sequence Analysis, RNA; D015398:Signal Transduction; D049490:Systems Biology; D013997:Time Factors
D012331:RNA, Fungal