Titlebook: RNA Design; Methods and Protocol Alexander Churkin,Danny Barash Book 2025 The Editor(s) (if applicable) and The Author(s), under exclusive

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Sequence Design for RNA-RNA Interactions,gy and biomedicine. Most regulatory RNAs function by forming RNA-RNA interactions, e.g., in order to regulate mRNA expression. It is therefore natural to consider problems where a sequence is designed to form a desired RNA-RNA interaction and switch between structures upon binding. This contribution

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Complex In Silico RNA Design with MoiRNAiFold, be effective tools in practical contexts. Moreover, it is of utmost importance to develop and provide access to computational tools capable of designing such RNA constructs. Here we introduce one such novel diagnostics technology (Apta-SMART) and show how to design (using MoiRNAiFold) and implement

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Machine Learning for RNA Design: LEARNA,arkable results. In this chapter, we describe machine learning approaches specifically developed for the design of RNAs, with a focus on the learna_tools Python package, a collection of automated deep reinforcement learning algorithms for secondary structure-based RNA design. We explain the basic co

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RNA Design Using incaRNAfbinv Demonstrated with the Identification of Functional RNA Motifs in Hepantemporary RNA design techniques, in addition to improved efficiency, offer more precise control over the designed sequences. incaRNAfbinv (incaRNAtion with RNA fragment-based inverse) is one such extension that builds upon RNAinverse and includes coarse-graining manipulations. The idea is that an R

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gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design,ariety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present ., a .eometric .

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Toward Increasing the Credibility of RNA Design,ssible free energy and under certain constraints. The designed sequences have applications in synthetic biology and RNA-based nanotechnologies. There are also known cases of the successful use of inverse folding to discover previously unknown noncoding RNAs. Several computational methods have been d

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3D-Based RNA Function Prediction Tools in , in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remain time-consuming and lack standardization. In this chapter, we describe the use of ., to train supervised and unsupervised machine learning-based function prediction models on datasets of RNA