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Related Concept Videos

RNA Structure01:19

RNA Structure

The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. 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) involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three...
RNA Structure01:23

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 Structure01:23

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 Stability01:53

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 Stability01:53

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-seq03:21

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 microarray-based...

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Related Experiment Video

Updated: May 8, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
10:34

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

Published on: December 9, 2022

Mitigating Family Effects in RNA Secondary-Structure Prediction with Latent-Space Continual Learning.

Wissal Mokkedem, Giulia Pedrielli, Teresa Wu

    Biorxiv : the Preprint Server for Biology
    |May 7, 2026
    PubMed
    Summary

    RNA secondary-structure prediction is challenging due to noisy data. RNAFoLBO, a continual learning approach, improves prediction accuracy and model adaptability by optimizing heterogeneous models on RNA clusters.

    More Related Videos

    RNA Secondary Structure Prediction Using High-throughput SHAPE
    13:42

    RNA Secondary Structure Prediction Using High-throughput SHAPE

    Published on: May 31, 2013

    Related Experiment Videos

    Last Updated: May 8, 2026

    Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
    10:34

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

    Published on: December 9, 2022

    RNA Secondary Structure Prediction Using High-throughput SHAPE
    13:42

    RNA Secondary Structure Prediction Using High-throughput SHAPE

    Published on: May 31, 2013

    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Machine Learning

    Background:

    • Accurate RNA secondary-structure prediction is crucial but remains a significant challenge.
    • Existing thermodynamics-based algorithms and deep learning models struggle with low-quality, noisy, and imbalanced datasets.
    • These data limitations hinder generalization and lead to catastrophic forgetting in machine learning models.

    Purpose of the Study:

    • To develop a novel continual-learning approach, RNAFoLBO, to enhance RNA secondary-structure prediction.
    • To address data quality issues and improve model generalization and knowledge preservation.
    • To enable robust and transferable RNA structure prediction by integrating new RNA data effectively.

    Main Methods:

    • Proposed RNAFoLBO, a Lifelong Bayesian Optimization (LBO) approach.
    • Treated RNA classes from latent-space clustering as sequential tasks.
    • Jointly optimized heterogeneous models (UFold, RNA-FM, RNADiffFold) and hyperparameters, preserving prior knowledge.
    • Applied LBO to 15 clusters from RNAStrAlign within the RNAGenesis latent space.

    Main Results:

    • Achieved a mean F1 score of 0.931 per cluster (range 0.177).
    • Outperformed the strongest one-shot baseline models.
    • Mitigated catastrophic forgetting without requiring full retraining of models.
    • Demonstrated persistent performance gains with the introduction of additional RNA clusters.

    Conclusions:

    • RNAFoLBO offers a scalable and robust solution for RNA secondary-structure prediction.
    • The approach enhances prediction accuracy and stability across diverse RNA families.
    • RNAFoLBO facilitates the seamless integration of new RNA data, improving model transferability.