IET Syst Biol. 2013 Oct;7(5):135-42. 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.
微阵列提供全基因组转录谱，而 RNA-seq 是一种用于转录本发现和基因组注释的替代方法。这两种高通量技术都显示了基因表达的定量测量。为了探索差异基因表达率并了解生物学功能，作者设计了一个系统，该系统利用京都基因与基因组百科全书 (KEGG) 生物途径和基因本体论 (GO) 关联的注释来整合多个 RNA-seq 或微阵列数据集。该系统通过将下一代测序短读序列映射到参考基因组上来估计基因表达水平，或者从微阵列原始图像中进行强度分析来启动。通过不同的方法（包括每百万映射读取的每千碱基读取数（RPKM）和管家基因选择）来评估和比较表达水平的归一化过程。以不同的颜色阴影显示这些基因表达水平，并以设计的时间途径图形显示。为了增强聚类基因功能关系的重要性，与差异表达基因簇相关的代表性 GO 术语以标记云表示的形式直观地显示出来。
