Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Rnall: an efficient algorithm for predicting RNA local secondary structural landscape in genomes.

Xiu-Feng Wan1, Guohui Lin, Dong Xu

  • 1Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri - Columbia, Columbia, MO 65211, USA. wanx@muohio.edu

Journal of Bioinformatics and Computational Biology
|November 14, 2006
PubMed
Summary

We developed Rnall, a new tool for predicting RNA local secondary structures (LSSs) genome-wide. This method improves accuracy and facilitates RNA structural motif mining.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis.

BMC genomics·2015
Same author

Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms.

BMC bioinformatics·2015
Same author

The I-TASSER Suite: protein structure and function prediction.

Nature methods·2014
Same author

Genome-wide expression analysis of soybean NF-Y genes reveals potential function in development and drought response.

Molecular genetics and genomics : MGG·2014
Same author

Classification of lung cancer using ensemble-based feature selection and machine learning methods.

Molecular bioSystems·2014
Same author

Resveratrol possesses protective effects in a pristane-induced lupus mouse model.

PloS one·2014

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • RNA local secondary structures (LSSs) are crucial for identifying functional motifs and understanding RNA roles.
  • Existing genome-scale RNA LSS prediction tools face challenges due to high computational complexity.

Purpose of the Study:

  • To develop an efficient computational tool for large-scale RNA LSS prediction.
  • To introduce a novel method for visualizing RNA LSSs to aid motif discovery.

Main Methods:

  • Developed Rnall, a dynamic programming package utilizing a sliding window approach.
  • Employed nearest neighbor thermodynamic parameters for LSS extraction.
  • Introduced the concept of an energy landscape for RNA LSS representation.

Related Experiment Videos

Main Results:

  • Rnall demonstrates superior prediction accuracy compared to Lfold and Quickfold.
  • Genome-wide scans using Rnall successfully identified known RNA motifs.
  • Observed practical running time complexity of O(W(2)L) for Rnall.

Conclusions:

  • Rnall is a specialized tool for RNA LSS prediction, offering unique capabilities for genomic structural motif mining.
  • The integration of Rnall with energy landscapes provides a powerful approach for RNA research.
  • Rnall is freely accessible for academic use.