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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: Jun 8, 2025

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

Sci-ModoM: a quantitative database of transcriptome-wide high-throughput RNA modification sites.

Etienne Boileau^1,2,3, Harald Wilhelmi^1,2, Anne Busch⁴

¹Klaus Tschira Institute for Integrative Computational Cardiology, Im Neuenheimer Feld 669, 69120 Heidelberg, Germany.

Nucleic Acids Research

|November 5, 2024

Summary

This summary is machine-generated.

Sci-ModoM is a new database for RNA modifications, providing quantitative data and comparison tools. It aims to standardize epitranscriptomics research and make data accessible to all scientists.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

The epitranscriptomic landscape, encompassing RNA modifications, is crucial for gene regulation.
A lack of standardized, accessible data hinders comprehensive analysis and discovery in epitranscriptomics.

Purpose of the Study:

To introduce Sci-ModoM, a next-generation database for epitranscriptomic data.
To provide a holistic, FAIR-compliant platform for quantitative RNA modification analysis.
To foster community standards and empower researchers with accessible data.

Main Methods:

Development of a user-friendly interface for browsing, searching, and downloading epitranscriptomic data.
Implementation of FAIR data principles, standardized nomenclature, and interoperable formats.
Integration of quantitative measurements (score, coverage, stoichiometry) for data confidence assessment.
Inclusion of a novel site-wise comparison tool for analyzing modifications across datasets.

Main Results:

Sci-ModoM offers a comprehensive view of the epitranscriptomic landscape.
The database contains over six million modifications across 156 datasets.
Quantitative data with confidence levels and direct source traceability are provided.
A unique comparison tool allows users to analyze their data against published research.

Conclusions:

Sci-ModoM enhances the accessibility and utility of quantitative RNA modification data.
The platform empowers researchers, including non-experts, to advance biological and medical research.
Standardization and data sharing via Sci-ModoM will accelerate discoveries in epitranscriptomics.