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RNA Folding

Overview of attention for book
RNA Folding
Springer US

Table of Contents

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    Book Overview
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    Chapter 1 Estimating RNA Secondary Structure Folding Free Energy Changes with efn2
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    Chapter 2 VarGibbs Usage in the Optimization of Nearest-Neighbor Parameters and Prediction of Melting Temperature of RNA Duplexes
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    Chapter 3 RNA Secondary Structure Thermodynamics
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    Chapter 4 RNA Secondary Structure Modeling Following the IPANEMAP Workflow.
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    Chapter 5 How Parameters Influence Shape -Directed Predictions
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    Chapter 6 Classified Dynamic Programming in RNA Structure Analysis.
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    Chapter 7 Classification and Identification of Non-canonical Base Pairs and Structural Motifs
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    Chapter 8 Modified Nucleotides and RNA Structure Prediction.
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    Chapter 9 How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA
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    Chapter 10 LocARNA 2.0: Versatile Simultaneous Alignment and Folding of RNAs
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    Chapter 11 Evolutionary Structure Conservation and Covariance Scores
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    Chapter 12 Developing Complex RNA Design Applications in the Infrared Framework.
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    Chapter 13 A Guide to Computational Cotranscriptional Folding Featuring the SRP RNA
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    Chapter 14 Assessing the Quality of Cotranscriptional Folding Simulations
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    Chapter 15 The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline.
Attention for Chapter 6: Classified Dynamic Programming in RNA Structure Analysis.
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Chapter title
Classified Dynamic Programming in RNA Structure Analysis.
Chapter number 6
Book title
RNA Folding
Published in
Methods in molecular biology, February 2012
DOI 10.1007/978-1-0716-3519-3_6
Pubmed ID
38780730
Book ISBNs
978-1-07-163518-6, 978-1-07-163519-3
Authors

Björn Voß, Voß, Björn

Abstract

Analysis of the folding space of RNA generally suffers from its exponential size. With classified Dynamic Programming algorithms, it is possible to alleviate this burden and to analyse the folding space of RNA in great depth. Key to classified DP is that the search space is partitioned into classes based on an on-the-fly computed feature. A class-wise evaluation is then used to compute class-wide properties, such as the lowest free energy structure for each class, or aggregate properties, such as the class' probability. In this paper we describe the well-known shape and hishape abstraction of RNA structures, their power to help better understand RNA function and related methods that are based on these abstractions.

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