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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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Updated: May 6, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Open-source liquid chromatography-mass spectrometry data processing tools for RNA modification analysis.

Jiawei Wang¹, Zhenwei Jiang¹, Zihui Jin¹

¹Jiangsu Key Laboratory of Drug Discovery and Translational Research for Brain Diseases, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu 215123, China; Department of Pharmaceutical Analysis, College of Pharmaceutical Sciences, Soochow University, Suzhou, Jiangsu, 215123, China.

Journal of Chromatography. A

|September 13, 2025

Summary

This summary is machine-generated.

Researchers are exploring RNA modifications for biological functions and therapeutics. This review covers liquid chromatography-mass spectrometry (LC-MS) methods and open-source tools for analyzing these crucial RNA changes.

Keywords:

Data processing LC-MS Oligonucleotide Open-source RNA modification

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Area of Science:

Biochemistry
Molecular Biology
Bioinformatics

Background:

Over 170 RNA modifications are known, impacting biological functions and disease.
Synthetic RNA modifications are vital for developing RNA-based therapeutics.
Liquid chromatography-mass spectrometry (LC-MS) is key for comprehensive RNA modification analysis.

Purpose of the Study:

To review LC-MS methodologies for RNA modification analysis.
To evaluate open-source data processing tools for LC-MS in RNA research.
To discuss future directions, including AI/ML integration.

Main Methods:

Examination of current LC-MS techniques for RNA modification characterization.
Analysis of existing open-source software for processing LC-MS data.
Literature review of advancements in RNA modification analysis.

Main Results:

LC-MS is a powerful technique for identifying and quantifying RNA modifications.
Open-source tools offer accessible, flexible, and cost-effective alternatives to commercial software.
Specific strengths and limitations of various tools are identified for different research needs.

Conclusions:

Open-source tools enhance research accessibility and customization in RNA modification analysis.
Continued development of LC-MS methods and data analysis tools is crucial for advancing RNA therapeutics.
Integration of AI and machine learning promises to further improve the efficiency and depth of RNA modification analysis.