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

Area of Science:

• Molecular Biology
• Systems Biology
• Bioinformatics

Background:

• Simultaneous dynamic profiling of mRNA and protein expression is crucial for understanding gene regulation.
• Existing algorithms struggle to identify regulatory layers and time dependency in gene expression data.
• Measurement noise in proteomic data complicates accurate analysis.

Purpose of the Study:

• To present PECAplus, a comprehensive set of statistical analysis tools for dynamic mRNA and protein expression profiling.
• To address the critical need for algorithms that identify regulatory layers and time dependency in gene expression.
• To provide tools for deconvoluting gene expression regulation in the presence of measurement noise.

Main Methods:

• PECAplus adapts the Protein Expression Control Analysis (PECA) mass action model.
• The tool is applicable to various proteomic data types, including pulsed SILAC and generic protein expression data.
• Includes modules for fitting smooth curves to time series data and time-dependent interpretation.

Main Results:

• PECAplus computes probability scores for changes in mRNA and protein regulatory parameters.
• The software effectively deconvolutes gene expression regulation, accounting for measurement noise.
• Demonstrated utility on mammalian cell time course datasets responding to unfolded proteins and pathogens.

Conclusions:

• PECAplus offers a robust computational framework for analyzing dynamic gene expression data.
• The tool facilitates a deeper understanding of time-dependent gene regulation at both mRNA and protein levels.
• PECAplus is valuable for researchers studying complex biological responses over time.