PECA: A Novel Statistical Tool for Deconvoluting Time-Dependent Gene Expression Regulation

• DOI: 10.1021/pr400855q
• Publication Date: 2013-11-14T11:51:36Z
• Authors: Guoshou Teo; Christine Vogel; Debashis Ghosh; Sinae Kim; Hyungwon Choi
• Keywords: Protein Degradation; False Discovery Rate

Protein expression varies as a result of intricate regulation synthesis and degradation messenger RNAs (mRNA) proteins. Studies dynamic typically rely on time-course data sets mRNA protein expression, yet there are no statistical methods that integrate these multiomics deconvolute individual regulatory processes gene control underlying the observed concentration changes. To address this challenge, we developed Expression Control Analysis (PECA), method to quantitatively dissect variation into contributions synthesis/degradation synthesis/degradation, termed RNA-level protein-level respectively. PECA computes rate ratios versus summary during given time interval at each molecular level probability ratio changed between adjacent intervals, indicating change point. Along with associated false-discovery rates, gives complete description control, is, which proteins were up- or down-regulated Using PECA, analyzed two yeast monitoring cellular response hyperosmotic oxidative stress. The profiles reported by highlighted large magnitude up-regulation stress genes in early concordant delay. However, RNA- their temporal patterns different sets. We also several cases where counterbalanced transcriptomic changes maintain stability concentrations, suggesting proteostasis is proteome-wide phenomenon mediated post-transcriptional regulation.