Halfpipe: a tool for analyzing metabolic labeling RNA-seq data to quantify RNA half-lives

Area of Science:

• Molecular Biology
• Genomics
• Biotechnology

Background:

• Metabolic RNA labeling experiments, such as SLAM-seq, are crucial for studying RNA dynamics.
• Analyzing this data presents challenges due to low labeling efficiency and associated biases.
• Accurate quantification of RNA synthesis and degradation is essential for understanding gene regulation.

Purpose of the Study:

• To introduce Halfpipe, a novel computational tool for analyzing metabolic RNA labeling data.
• To enable absolute quantification of 4-thiouridine-induced T>C conversions and estimate subcellular RNA half-lives.
• To investigate and compare RNA metabolism during different cell cycle phases (G1 and mitosis).

Main Methods:

• Development and application of the Halfpipe software tool.
• Utilizing SLAM-seq data for analysis of T>C conversions.
• Measuring and comparing RNA metabolism in synchronized human cells during G1 and mitosis.

Main Results:

• Halfpipe effectively corrects biases from low labeling efficiency in metabolic RNA labeling data.
• The proportion of newly synthesized transcripts and subcellular RNA half-lives were estimated.
• RNA half-lives for constantly expressed RNAs were found to be similar in G1 and mitosis.

Conclusions:

• RNA stability of constantly expressed genes is consistent throughout the cell cycle.
• Halfpipe provides accurate estimates of RNA metabolism, correlating well with existing literature and sequence features.
• The Halfpipe tool is freely available for researchers studying RNA dynamics.