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NPJ Syst Biol Appl. 2017 Dec 19;4:3. doi: 10.1038/s41540-017-0040-1. eCollection 2018.

PECAplus: statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments.

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Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY USA.
College of Arts and Science, New York University, New York, NY USA.
Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research, Singapore, Singapore.


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.

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