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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...

Nucleic Acid Structure

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...

Types of RNA

Types of RNA

Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

RNA Stability

RNA Stability

Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...

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 26, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

RNA structure prediction using deep learning - A comprehensive review.

Mayank Chaturvedi¹, Mahmood A Rashid¹, Kuldip K Paliwal¹

¹Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD, 4111, Australia.

Computers in Biology and Medicine

|February 21, 2025

Summary

This summary is machine-generated.

Deep learning methods significantly improve RNA secondary structure prediction accuracy, aiding RNA function studies and drug design. This review covers feature extraction, model architectures, and prediction approaches, identifying future research directions.

Keywords:

Deep learning Feature extraction Machine learning Neural networks RNA secondary structure prediction Transformers

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Area of Science:

Computational Biology
Bioinformatics
Artificial Intelligence in Life Sciences

Background:

Accurate RNA structure prediction is crucial for understanding RNA functions and developing RNA-based therapeutics.
Deep learning has revolutionized RNA structure prediction, achieving substantial gains in accuracy.

Purpose of the Study:

To provide a comprehensive review of deep learning strategies for RNA secondary structure prediction.
To categorize and analyze feature extraction methods, model architectures, and prediction approaches.
To identify research gaps, challenges, and future directions in the field.

Main Methods:

Review and synthesis of existing literature on deep learning for RNA structure prediction.
Categorization of methods into feature extraction, model architectures, and prediction strategies.
Comparative analysis of different techniques and models, highlighting strengths and weaknesses.

Main Results:

Deep learning models demonstrate significant improvements in RNA secondary structure prediction accuracy.
Various feature extraction techniques and model architectures are employed, each with specific advantages.
A comparative analysis reveals the performance landscape of current state-of-the-art methods.

Conclusions:

Deep learning is a powerful tool for advancing RNA structure prediction.
Further research is needed to address current challenges and enhance model performance and applicability.
This review offers insights for future advancements at the intersection of RNA biology and artificial intelligence.