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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: May 27, 2025

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

Published on: August 8, 2013

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

Jason M Müller^1,2, Elisabeth Altendorfer³, Susanne Freier³

¹Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany.

NAR Genomics and Bioinformatics

|February 19, 2025

Summary

This summary is machine-generated.

Halfpipe is a new tool for analyzing metabolic RNA labeling data, accurately quantifying RNA synthesis and half-lives. It reveals that RNA stability remains constant for constantly expressed genes throughout the cell cycle.

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Last Updated: May 27, 2025

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

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Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts

Published on: January 2, 2018

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.