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

Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign.

Arif Ozgun Harmanci1, Gaurav Sharma, David H Mathews

  • 1Department of Electrical and Computer Engineering, University of Rochester, Hopeman 204, Rochester, NY 14627, USA. arharman@ece.rochester.edu <arharman@ece.rochester.edu>

BMC Bioinformatics
|April 21, 2007
PubMed
Summary
This summary is machine-generated.

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

Computational Resources for Molecular Biology 2026.

Journal of molecular biology·2026
Same author

Reparameterization of the Amber RNA Force Field Non-Bonded Terms.

bioRxiv : the preprint server for biology·2026
Same author

Nearest Neighbor Parameters for Estimating the Folding Stability of RNA Including Pseudouridine.

bioRxiv : the preprint server for biology·2026
Same author

RNA Folding Nearest Neighbor Parameters Including the Modification 1-Methyl-Pseudouridine.

bioRxiv : the preprint server for biology·2026
Same author

Motif server: web server for undesignable RNA motifs and structures.

Journal of molecular biology·2026
Same author

SamplingDesign: RNA design via continuous optimization with coupled variables and Monte-Carlo sampling.

Nature communications·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

This study introduces a new method for RNA structure prediction that automates alignment constraints using probabilistic analysis. The approach improves accuracy and significantly reduces computation time and memory usage for predicting RNA secondary structures.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Molecular biology

Background:

  • Joint alignment and secondary structure prediction of RNA sequences enhance accuracy.
  • Existing methods use constraints that limit permissible alignments and structures, increasing computation.
  • A novel methodology is presented to establish alignment constraints based on nucleotide alignment and insertion posterior probabilities.

Purpose of the Study:

  • To develop a new methodology for establishing alignment constraints for RNA structure prediction.
  • To integrate these constraints into the Dynalign algorithm for joint alignment and secondary structure prediction.
  • To benchmark the performance of the new method against previous versions and other existing programs.

Main Methods:

  • Utilized a hidden Markov model to compute posterior probabilities of alignment and insertion for nucleotide position pairings.

Related Experiment Videos

  • Combined alignment and insertion posterior probabilities to derive probabilities of co-incidence for nucleotide position pairs.
  • Thresholded co-incidence probabilities to obtain alignment constraints, integrated with Dynalign's free energy minimization.

    • Main Results:

    • The proposed technique automates parameter selection and offers significant computational time savings compared to prior Dynalign constraints.
    • Achieved a small improvement in structural prediction accuracy and reduced memory requirements.
    • Demonstrated a twofold reduction in computation for a 5S RNA dataset and favorable performance against other pairwise RNA structure prediction programs.

    Conclusions:

    • Probabilistic analysis can automate alignment constraint determination for pairwise RNA structure prediction in a principled manner.
    • These constraints reduce computational and memory demands while maintaining or improving prediction accuracy.
    • The revised Dynalign code is freely available, extending the practical application of these methods to longer RNA sequences.