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Related Experiment Video

Updated: Jul 31, 2025

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Published on: February 18, 2022

IRSOM2: a web server for predicting bifunctional RNAs.

Guillaume Postic¹, Christophe Tav¹, Ludovic Platon¹

¹Université Paris-Saclay, Univ Evry, IBISC, 91020, Evry-Courcouronnes, France.

Nucleic Acids Research

|May 9, 2023

Summary

This summary is machine-generated.

Scientists developed IRSOM2, a new computational tool to identify bifunctional RNAs, which are non-coding RNAs (ncRNAs) that also produce functional proteins. This advances the understanding of RNA biology and protein synthesis.

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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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Published on: February 18, 2022

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Published on: November 3, 2011

The ITS2 Database

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Published on: March 12, 2012

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Recent discoveries reveal that some non-coding RNAs (ncRNAs) can be translated into functional polypeptides, challenging traditional RNA classification.
This necessitates the development of computational tools capable of identifying these 'bifunctional RNAs' that possess dual roles.

Purpose of the Study:

To introduce IRSOM2, an enhanced computational method for identifying bifunctional RNAs.
To provide a user-friendly web interface for RNA sequence analysis and prediction.
To establish a benchmark of experimentally validated bifunctional RNAs.

Main Methods:

IRSOM2 was developed by adapting the binary statistical model of the existing IRSOM algorithm into a ternary classifier.
The method identifies bifunctional RNAs by classifying sequences that are rejected by the coding and non-coding RNA classes.
Self-organizing maps (SOM) were implemented for visualizing and analyzing classification results.

Main Results:

IRSOM2 demonstrated promising performance in identifying bifunctional transcripts, including circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs).
The tool effectively detected shorter lncRNAs with bifunctional potential.
A new benchmark dataset of experimentally validated bifunctional RNAs across different organisms was curated.

Conclusions:

IRSOM2 represents a significant advancement in the computational prediction of bifunctional RNAs.
The developed web server offers a valuable resource for researchers studying RNA function and translation.
This work contributes to a deeper understanding of the complex roles of RNA in biological systems.