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


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.

SUBMITTER: Teo G 

PROVIDER: S-EPMC5736550 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Teo Guoshou G   Bin Zhang Yun Y   Vogel Christine C   Choi Hyungwon H  

NPJ systems biology and applications 20171219


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 po  ...[more]

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