Identification of Genes Under Dynamic Post-transcriptional Regulation from Time-series Epigenomic Data

Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. We used time-series profiles of chromatin immunoprecipitation-seq data of histone modifications from differentiation of mesenchymal progenitor cells toward adipocytes and osteoblasts to predict gene expression levels at five time points in both lineages and estimated the deviation of those predictions from the RNA-seq measured expression levels using linear regression. The genes with biggest changes in their estimated stability across the time series are enriched for noncoding RNAs and lineage-specific biological processes. Clustering mRNAs according to their stability dynamics allows identification of post-transcriptionally coregulated mRNAs and their shared regulators through sequence enrichment analysis. We identify miR-204 as an early induced adipogenic microRNA targeting and .