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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: Mar 1, 2026

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

Published on: December 2, 2009

Numerical integration methods and layout improvements in the context of dynamic RNA visualization.

Boris Shabash¹, Kay C Wiese²

¹School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada.

BMC Bioinformatics

|June 1, 2017

Summary

New methods improve RNA visualization software by using compressed graphs and advanced integration for better performance and aesthetics. These enhancements offer more stable and interactive RNA secondary structure representations.

Keywords:

Graph layout Numerical integration RNA Visualization

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Design and Synthesis of a Reconfigurable DNA Accordion Rack

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Published on: August 15, 2018

Area of Science:

Computational Biology
Bioinformatics
Structural Biology

Background:

Traditional RNA visualization tools offer limited interactivity and static outputs.
Existing tools like jViz.RNA use detailed graphs and Newtonian mechanics for RNA layout.
There's a need for improved RNA visualization with better adherence to drawing conventions and enhanced user interaction.

Purpose of the Study:

To enhance jViz.RNA for drawing RNA secondary structures following common conventions.
To significantly improve the run-time performance of RNA visualization.
To introduce more dynamic and interactive capabilities for RNA molecule representation.

Main Methods:

Developed an alternative 'compressed graph' method for mapping RNA molecules.
Implemented advanced numerical integration techniques for the compressed graph representation.
Compared 'compressed graph' with 'detailed graph' methods and evaluated integration techniques.

Main Results:

The 'compressed graph' method yields results more aligned with standard RNA drawing conventions.
The Backward Euler integration method demonstrated superior stability and larger time step handling compared to Forward Euler.
Optimized methods enhance run-time performance and usability for RNA visualization.

Conclusions:

Compressed graphs are preferred over detailed graphs for RNA secondary structure visualization.
The Backward Euler method is advantageous over the Forward Euler method for stability and efficiency.
These advancements lead to more stable, visually appealing, and user-friendly RNA secondary structure representations.