RNA-seq coverage effects on biological pathways and GO tag clouds

RNA-seq data analysis not only detects novel transcripts, promoters, and single nucleotide polymorphisms in a transcriptome scale, but also shows quantitative measurement of gene expression. In order to perform differential expression analysis for unraveling biological functions, we proposed a workflow which integrated annotations from KEGG biological pathways and Gene Ontology associations for manipulating multiple RNA-seq datasets. The developed system started from mapping short reads onto reference genes, and then performed normalization procedures on read coverage to evaluate and compare expression levels within various gene clusters. Different levels of gene expression were indicated by diverse color shades and graphically shown in designed temporal pathways. Besides, representative GO terms associated with differentially expressed gene cluster were also visually displayed by a GO tag cloud representation. Three different public RNA-seq datasets were applied to demonstrate that the proposed workflow could provide effective and efficient analysis on differential gene expression for either cross-strain comparison or an identical sample sequenced at different time points.