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Related Experiment Video

Updated: Nov 3, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

Published on: December 2, 2009

RNA Post-Transcriptional Modification Mapping Data Analysis Using RNA Framework.

Ilaria Manfredonia¹, Danny Incarnato²

¹Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, The Netherlands.

Methods in Molecular Biology (Clifton, N.J.)

|June 4, 2021

Summary

This summary is machine-generated.

RNA post-transcriptional modifications (PTMs) regulate RNA functions, with new tools enabling large-scale analysis. This study validates RNA Framework for analyzing transcriptome-wide mapping data of m1A and pseudouridine modifications.

Keywords:

High-throughput sequencing N1-methyladenosine Pseudouridine RNA immunoprecipitation RNA post-transcriptional modifications m1A

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Last Updated: Nov 3, 2025

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

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Published on: May 24, 2017

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

RNA post-transcriptional modifications (PTMs) are crucial for diverse RNA functions beyond coding.
These modifications dynamically regulate RNA physiology, including translation and stability.
Advancements in high-throughput technologies facilitate transcriptome-wide detection of RNA PTMs.

Purpose of the Study:

To demonstrate the utility of the RNA Framework toolkit.
To analyze transcriptome-scale datasets of RNA PTM mapping experiments.
To validate the framework using N1-methyladenosine (m1A) and pseudouridine (Ψ) mapping data.

Main Methods:

Reanalysis of two published transcriptome-scale datasets.
Utilized the RNA Framework, a comprehensive bioinformatics toolkit.
Applied the framework to m1A and Ψ mapping data generated by different experimental strategies.

Main Results:

The RNA Framework effectively analyzes RNA PTM mapping data.
The toolkit's utility is demonstrated across different experimental approaches.
Successful reanalysis of m1A and Ψ mapping datasets confirmed the framework's capabilities.

Conclusions:

RNA Framework is a valuable tool for analyzing RNA PTM mapping experiments.
The study highlights the importance of computational tools in understanding RNA PTMs.
The findings support the growing relevance of RNA PTMs in molecular biology.