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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.

Author information

1
Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY USA.
2
College of Arts and Science, New York University, New York, NY USA.
3
Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
4
Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research, Singapore, Singapore.

Abstract

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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