Time-Series Analysis of RNA-Seq Gene Expression Data

Time-series analysis of gene expression data from the animal infection time-course experiment was performed using the Short Time-series Expression Miner (STEM) software package (59). The computational procedure for selecting model profiles that are representative and distinct is described by Ernst et al. (60). The software package implements a method for clustering short time-series expression data that can differentiate between real and random patterns of temporal gene expression changes and assigns each gene to the model profile that most closely matches the temporal gene expression profile for that gene as determined by the correlation coefficient. A permutation test is then used to determine which model profiles have a statistically significant number of genes assigned compared to random expectations from the mean number assigned to each profile based on the permuted data (59). STEM also incorporates GO enrichment functionality for biological interpretation of time-series gene expression data.