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Updated: Dec 14, 2025

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

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Published on: April 8, 2017

Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics.

Lian Liu¹, Bowen Song^2,3, Jiani Ma⁴

¹School of Computer Sciences, Shannxi Normal University, Xi'an, Shaanxi 710119, China.

Computational and Structural Biotechnology Journal

|July 17, 2020

Summary

This summary is machine-generated.

Bioinformatics tools are essential for studying RNA modifications across the transcriptome. This review covers current methods for analyzing epitranscriptome data, databases, and future research directions.

Keywords:

Bioinformatics approaches Epitranscriptome Future perspective RNA modification Recent progress

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Post-transcriptional RNA modifications regulate diverse RNA functions.
High-throughput sequencing enables transcriptome-wide profiling of RNA modifications.
Bioinformatics is crucial for analyzing large epitranscriptome datasets.

Purpose of the Study:

To review recent advancements in bioinformatics approaches for epitranscriptome research.
To cover data analysis, databases, disease association, functional annotation, and site prediction.
To discuss current limitations and future perspectives in the field.

Main Methods:

Literature review of bioinformatics approaches for epitranscriptome analysis.
Summarization of techniques for RNA modification data analysis.
Overview of RNA modification databases and prediction tools.

Main Results:

Significant progress has been made in bioinformatics tools for epitranscriptome deciphering.
Key areas include data analysis, database development, and functional annotation.
RNA modification site prediction methods are continually improving.

Conclusions:

Bioinformatics plays a critical role in advancing epitranscriptomics.
Further development of computational tools is needed to fully understand RNA modifications.
Future research should focus on integrating diverse data types for comprehensive analysis.