ArticleFrontiers in genetics2012
Multi-objective genetic algorithm for pseudoknotted RNA sequence design.
Article in Frontiers in genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- aRNAque: an evolutionary algorithm for inverse pseudoknotted RNA folding inspired by Lévy flights.BMC bioinformatics · 2022Article
- Design of RNAs: comparing programs for inverse RNA folding.Briefings in bioinformatics · 2018Review
- Design of Artificial Riboswitches as Biosensors.Sensors (Basel, Switzerland) · 2017Review
- incaRNAfbinv: a web server for the fragment-based design of RNA sequences.Nucleic acids research · 2016Article
- An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures.Frontiers in genetics · 2016Article
- antaRNA--Multi-objective inverse folding of pseudoknot RNA using ant-colony optimization.BMC bioinformatics · 2015Article
- Multi-objective optimization for RNA design with multiple target secondary structures.BMC bioinformatics · 2015Article
- ERD: a fast and reliable tool for RNA design including constraints.BMC bioinformatics · 2015Article
- Inverse RNA folding solution based on multi-objective genetic algorithm and Gibbs sampling method.EXCLI journal · 2013Article
- Frnakenstein: multiple target inverse RNA folding.BMC bioinformatics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA inverse folding is a computational technology for designing RNA sequences which fold into a user-specified secondary structure. Although pseudoknots are functionally important motifs in RNA structures, less reports concerning the inverse folding of pseudoknotted RNAs have been done compared to those for pseudoknot-free RNA design. In this paper, we present a new version of our multi-objective genetic algorithm (MOGA), MODENA, which we have previously proposed for pseudoknot-free RNA inverse folding. In the new version of MODENA, (i) a new crossover operator is implemented and (ii) pseudoknot prediction methods, IPknot and HotKnots, are used to evaluate the designed RNA sequences, allowing us to perform the inverse folding of pseudoknotted RNAs. The new version of MODENA with the new crossover operator was benchmarked with a dataset composed of natural pseudoknotted RNA secondary structures, and we found that MODENA can successfully design more pseudoknotted RNAs compared to the other pseudoknot design algorithm. In addition, a sequence constraint function newly implemented in the new version of MODENA was tested by designing RNA sequences which fold into the pseudoknotted structure of a hepatitis delta virus ribozyme; as a result, we successfully designed eight RNA sequences. The new version of MODENA is downloadable from http://rna.eit.hirosaki-u.ac.jp/modena/.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.