ReviewBriefings in bioinformatics2018
Design of RNAs: comparing programs for inverse RNA folding.
Review in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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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.
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Who cites it
41 citing papers in PubMed.
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- SamplingDesign: RNA design via continuous optimization with coupled variables and Monte-Carlo sampling.Nature communications · 2026Article
- The RNA-Puzzles Assessments of RNA-Only Targets in CASP16.Proteins · 2026Article
- RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation.PLoS computational biology · 2025Article
- Deep generative model of RNAs based on variational autoencoder with context-free grammar.Bioinformatics (Oxford, England) · 2025Article
- mRNA folding algorithms for structure and codon optimization.Briefings in bioinformatics · 2025Review
- Comprehensive datasets for RNA design, machine learning, and beyond.Scientific reports · 2025Article
- Generative and predictive neural networks for the design of functional RNA molecules.Nature communications · 2025Article
- gRNAde: Geometric Deep Learning for 3D RNA inverse design.bioRxiv : the preprint server for biology · 2025Article
- Secondary-Structure-Informed RNA Inverse Design via Relational Graph Neural Networks.Non-coding RNA · 2025Article
- DesiRNA: structure-based design of RNA sequences with a replica exchange Monte Carlo approach.Nucleic acids research · 2025Article
- RNA Design Using incaRNAfbinv Demonstrated with the Identification of Functional RNA Motifs in Hepatitis Delta Virus.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Riboswitch Design Using MODENA.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Toward Increasing the Credibility of RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A Computational Approach for Designing Synthetic Riboswitches for Next-Generation RNA Therapeutics.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Sequence Design Using RNAstructure.Methods in molecular biology (Clifton, N.J.) · 2025Article
- gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- R3Design: deep tertiary structure-based RNA sequence design and beyond.Briefings in bioinformatics · 2024Article
- RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models.Bioinformatics (Oxford, England) · 2024Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational programs for predicting RNA sequences with desired folding properties have been extensively developed and expanded in the past several years. Given a secondary structure, these programs aim to predict sequences that fold into a target minimum free energy secondary structure, while considering various constraints. This procedure is called inverse RNA folding. Inverse RNA folding has been traditionally used to design optimized RNAs with favorable properties, an application that is expected to grow considerably in the future in light of advances in the expanding new fields of synthetic biology and RNA nanostructures. Moreover, it was recently demonstrated that inverse RNA folding can successfully be used as a valuable preprocessing step in computational detection of novel noncoding RNAs. This review describes the most popular freeware programs that have been developed for such purposes, starting from RNAinverse that was devised when formulating the inverse RNA folding problem. The most recently published ones that consider RNA secondary structure as input are antaRNA, RNAiFold and incaRNAfbinv, each having different features that could be beneficial to specific biological problems in practice. The various programs also use distinct approaches, ranging from ant colony optimization to constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. This review compares between the various programs and provides a simple description of the various possibilities that would benefit practitioners in selecting the most suitable program. It is geared for specific tasks requiring RNA design based on input secondary structure, with an outlook toward the future of RNA design programs.
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