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https://doi.org/10.1038/s41540-017-0040-1
Title: | PECAplus: statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments | Authors: | Teo, G Bin Zhang, Y Vogel, C Choi, H |
Issue Date: | 2018 | Citation: | Teo, G, Bin Zhang, Y, Vogel, C, Choi, H (2018). PECAplus: statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments. npj Systems Biology and Applications 4 (1) : 3. ScholarBank@NUS Repository. https://doi.org/10.1038/s41540-017-0040-1 | 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. © 2017, The Author(s). | Source Title: | npj Systems Biology and Applications | URI: | https://scholarbank.nus.edu.sg/handle/10635/175016 | ISSN: | 20567189 | DOI: | 10.1038/s41540-017-0040-1 |
Appears in Collections: | Elements Staff Publications |
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