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Related Concept Videos

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 Structure01:19

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-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...
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...

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  1. Home
  3. Virse: A Variational Bayesian Framework For Rna Structural Ensemble Inference.
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  3. Virse: A Variational Bayesian Framework For Rna Structural Ensemble Inference.

Related Experiment Video

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

VIRSE: a variational Bayesian framework for RNA structural ensemble inference.

Jialu Liang1, Yanfei Wang1, Xiao Fan2

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Rd, Suite 7000, Gainesville, FL 32611, United States.

Briefings in Bioinformatics
|June 15, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed VIRSE, a new computational framework for reconstructing RNA conformational ensembles from chemical probing data. VIRSE offers improved stability, scalability, and accuracy over existing methods for analyzing complex RNA structures.

Keywords:
Bayesian modelingRNA conformational heterogeneityRNA structural ensembleschemical probingsingle-molecule mutation profilesvariational inference

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Related Experiment Videos

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

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

  • Computational Biology
  • Structural Biology
  • Biophysics

Background:

  • RNA molecules exhibit dynamic structural heterogeneity, forming ensembles not captured by single-structure predictions.
  • Chemical probing methods (DMS-MaPseq, SHAPE-MaP) provide single-molecule signals reflecting RNA structural diversity.
  • Existing ensemble-inference methods like expectation-maximization (EM) face challenges with stability, scalability, and local optima for complex RNA data.

Purpose of the Study:

  • To develop a robust and scalable computational framework for reconstructing RNA conformational ensembles from chemical probing data.
  • To overcome the limitations of existing ensemble-inference approaches, particularly for high-dimensional and sparse mutation matrices.
  • To enable accurate and efficient analysis of RNA structural heterogeneity and dynamics.

Main Methods:

  • Developed VIRSE, a variational Bayesian framework utilizing coordinate ascent variational inference.
  • Evaluated VIRSE using extensive simulations, including mechanism-informed mutation simulations mimicking DMS-MaP-seq.
  • Tested VIRSE on idealized Bernoulli-mixture datasets and real-world experimental datasets.

Main Results:

  • VIRSE demonstrated superior ensemble separation and cluster identifiability compared to EM across various simulation conditions.
  • The framework showed improved stability, resolved low-abundance states, and scaled effectively to thousands of nucleotide positions.
  • VIRSE successfully reconstructed biologically meaningful RNA conformational ensembles from experimental data.

Conclusions:

  • VIRSE provides an efficient, scalable, and noise-robust solution for reconstructing RNA conformational mixtures from chemical probing data.
  • The variational Bayesian approach overcomes limitations of traditional EM methods for complex RNA structural analysis.
  • VIRSE accurately captures RNA structural heterogeneity, offering valuable insights into RNA function and dynamics.