RESUMO
Simultaneous dynamic profiling of mRNA and protein expression is increasingly popular, and there is a critical need for algorithms to identify regulatory layers and time dependency of gene expression. A group of scientists from United States and Singapore present PECAplus, a comprehensive set of statistical analysis tools to address this challenge. Protein expression control analysis (PECA) computes the probability scores for change in mRNA and protein-level regulatory parameters at each time point, deconvoluting gene expression regulation in the presence of measurement noise. PECAplus adapted PECA's mass action model to a variety of proteomic data including pulsed SILAC and generic protein expression data. It also features analysis modules to fit smooth curves on rugged time series observations, and to facilitate time-dependent interpretation of the data for genes and biological functions. They demonstrate the core modules with two time course datasets of mammalian cells responding to unfolded proteins and pathogens.
