Titlebook: RNA Structure Prediction; Risa Karakida Kawaguchi,Junichi Iwakiri Book 2023 Springer Science+Business Media, LLC, part of Springer Nature

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1064-3745 expertsThis book explores recent progress in RNA secondary, tertiary structure prediction, and its application from an expansive point of view. Because of advancements in experimental protocols and devices, the integration of new types of data as well as new analysis techniques is necessary, and th

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Linear-Time Algorithms for RNA Structure Prediction,m free energy (MFE) methods and partition function methods, suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Inspired by incremental parsing for context-free grammars in computational linguistics, we d

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Genome-Wide RNA Secondary Structure Prediction,sible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method des

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Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A Usemchi O et al., Biophys J 117(3):520–532, 2019), though it was not named there. The algorithm directly enumerates all possible secondary structures into which up to two RNA or single-stranded DNA sequences can fold. It uses a polymer physics model to estimate the configurational entropy of structures

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Metrics for RNA Secondary Structure Comparison,r functions and structures are strongly correlated. However, although the RNA secondary structures with pseudoknot play important roles in vivo, it is difficult to deal with such structures in silico due to their structural complexity, which is a major obstacle to the analysis of RNA functions..Here

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RNA Secondary Structure Prediction Based on Energy Models,res of modeling RNA secondary structure without pseudoknots. We discuss the parameterization and the parameter determination by experimental and machine learning-based approaches as well as an integrated approach that compensates each other’s shortcomings. Then, folding algorithms for the minimum fr

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