Dissecting newly transcribed and old RNA using GRAND-SLAM

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Global RNA quantification assesses gene expression steady states.
• Distinguishing new from pre-existing RNA offers insights into RNA half-lives and regulatory dynamics.
• Metabolic labeling and RNA-seq enable new RNA quantification but face challenges from low incorporation and sequencing errors.

Purpose of the Study:

• To develop a statistical approach for accurately estimating the proportion of new and old RNA.
• To quantify uncertainty in RNA proportion estimates using a Bayesian framework.
• To analyze the impact of uncertainty on RNA half-life estimations and provide experimental design guidelines.

Main Methods:

• Development of the GRAND-SLAM statistical method.
• Bayesian framework for quantifying uncertainty in RNA proportion estimates.
• Simulation experiments to assess accuracy and bias.

Main Results:

• GRAND-SLAM provides unbiased and highly accurate estimates of new and old RNA proportions.
• The method quantifies uncertainty, enabling robust RNA half-life calculations.
• RNA half-life estimates show improved accuracy and statistical power compared to existing methods.

Conclusions:

• GRAND-SLAM is the first method to accurately differentiate and quantify new and old RNA.
• The approach enhances the study of RNA stability and regulatory mechanisms.
• Improved RNA half-life estimation provides greater power for investigating biological processes.