ArticleBioinformatics (Oxford, England)2024
Partial RNA design.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rational design of mechanically active RNAs: de novo engineering of functional exoribonuclease-resistant RNAs.Nucleic acids research · 2026Article
- Genetically encoded biosensors enabled high-throughput screening of microbial cell factories.Engineering microbiology · 2026Review
- SamplingDesign: RNA design via continuous optimization with coupled variables and Monte-Carlo sampling.Nature communications · 2026Article
- Artificial intelligence in bioinformatics: a survey.Briefings in bioinformatics · 2025Review
- DRAG: design RNAs as hierarchical graphs with reinforcement learning.Briefings in bioinformatics · 2025Article
- Secondary-Structure-Informed RNA Inverse Design via Relational Graph Neural Networks.Non-coding RNA · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationRNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space.
resultsIn this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness. AVAILABILITY AND IMPLEMENTATION: libLEARNA is open-source and publicly available at: https://github.com/automl/learna_tools.
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Registered trials
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