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.
Original language | English (US) |
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Article number | 3 |
Journal | npj Systems Biology and Applications |
Volume | 4 |
Issue number | 1 |
DOIs | |
State | Published - Dec 1 2018 |
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ASJC Scopus subject areas
- Computer Science Applications
- Applied Mathematics
- Modeling and Simulation
- Biochemistry, Genetics and Molecular Biology(all)
- Drug Discovery
Cite this
PECAplus : statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments. / Teo, Guoshou; Bin Zhang, Yun; Vogel, Christine; Choi, Hyungwon.
In: npj Systems Biology and Applications, Vol. 4, No. 1, 3, 01.12.2018.Research output: Contribution to journal › Article
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TY - JOUR
T1 - PECAplus
T2 - statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments
AU - Teo, Guoshou
AU - Bin Zhang, Yun
AU - Vogel, Christine
AU - Choi, Hyungwon
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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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U2 - 10.1038/s41540-017-0040-1
DO - 10.1038/s41540-017-0040-1
M3 - Article
AN - SCOPUS:85052712570
VL - 4
JO - npj Systems Biology and Applications
JF - npj Systems Biology and Applications
SN - 2056-7189
IS - 1
M1 - 3
ER -