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RNA Structure

RNA Structure

Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...

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...

Experimental RNAi

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...

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Related Experiment Video

Updated: Aug 1, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Machine learning for RNA 2D structure prediction benchmarked on experimental data.

Marek Justyna¹, Maciej Antczak^1,2, Marta Szachniuk^1,2

¹Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland.

Briefings in Bioinformatics

|April 25, 2023

Summary

This summary is machine-generated.

Deep learning (DL) methods excel at RNA secondary structure prediction when training and testing data match. However, their advantage diminishes for novel RNA families, performing similarly to shallow learning (SL) and traditional approaches.

Keywords:

RNA 2D structure prediction algorithm benchmarking deep learning machine learning

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Related Experiment Videos

Last Updated: Aug 1, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

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

Area of Science:

Computational biology
Bioinformatics
Molecular biology

Background:

Predicting RNA secondary structure is crucial for understanding RNA function.
Numerous computational methods exist, including traditional optimization and recent machine learning (ML) algorithms.
ML methods, particularly deep learning (DL), require comprehensive benchmarking against established approaches.

Conclusions:

The choice of RNA secondary structure prediction method depends on the dataset's characteristics, particularly for novel sequences.
Deep learning shows promise but requires careful consideration of data similarity for optimal application.
Further research is needed to enhance the generalization capabilities of ML models for diverse RNA structures.