RNA Structure Prediction

Preface
	Organization of This Book
		RNA Secondary Structure Prediction
		Application of RNA Secondary Structure Prediction
		Other Applications of RNA Secondary Structure Prediction
		RNA 3D Structure Databases
		Computational Prediction and Design of RNA 3D Structure
		Representative RNA 3D Structure Dataset and RNA-Puzzles
	References
Contents
Contributors
Chapter 1: Rtools: A Web Server for Various Secondary Structural Analyses on Single RNA Sequences
	1 Introduction
	2 Design of the Web Server
	3 Implemented Computational Tools
		3.1 CentroidFold
		3.2 CentroidHomfold
		3.3 IPknot
		3.4 Rchange
		3.5 CapR
		3.6 Raccess
		3.7 RintD
		3.8 RintW
	References
Chapter 2: Linear-Time Algorithms for RNA Structure Prediction
	1 Introduction
		1.1 Free Energy Minimization
		1.2 Partition Function and Base Pair Probabilities
	2 Web Server
		2.1 Web Server of LinearFold
		2.2 Web Server of LinearPartition
	3 Command Line Protocols
		3.1 Installation
		3.2 Run LinearFold for Minimum Free Energy Structure
		3.3 Run LinearFold for Suboptimal Structures
		3.4 Constrained Folding
		3.5 Free Energy Evaluation
		3.6 Partition Function and Free Energy of Ensemble
		3.7 Base Pairing Probabilities and MEA Structure Prediction
	References
Chapter 3: Genome-Wide RNA Secondary Structure Prediction
	1 Introduction
	2 Methods
		2.1 RNA Secondary Structure Analysis in Single-Node Mode
		2.2 Transfer RNA Secondary Structure Prediction by ParasoR: An Example
		2.3 Rfold Algorithm
		2.4 ParasoR Algorithm
	3 Notes
	References
Chapter 4: Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User´s Guide to the...
	1 Introduction
		1.1 Pseudoknots Are Not Well-Modeled by Most Current Tools
		1.2 LandscapeFold Can Predict the Complete Secondary Structure Landscape Including Pseudoknots
	2 Overall Use of the Code
		2.1 Simple Example Usage
		2.2 Python Jargon
		2.3 The Sequences Input
	3 Enumerating the Complete Free Energy Landscape
		3.1 The START Function
			3.1.1 Determining Nucleotide Complementarity
			3.1.2 Enumerating All Possible Stems
			3.1.3 S-Table Storage and Computation Time
			3.1.4 Determining the Compatibility of Stems
		3.2 The PERMU Function
	4 Performing the Free Energy Calculation
		4.1 The Cost of Intermolecular Pairing
			4.1.1 Origins of This Penalty
			4.1.2 Estimates for the Penalty
			4.1.3 Details of LandscapeFold Implementation
			4.1.4 Symmetry Penalties
		4.2 The Stem Free Energy Model
			4.2.1 The Basic Nearest-Neighbor Free Energy Model
			4.2.2 Terminal A-U, G-U, and A-T Penalties
			4.2.3 Dangling Ends
			4.2.4 Flush Coaxial Stacks
			4.2.5 Terminal Mismatches Which Could Bind
			4.2.6 Modifying the Nearest-Neighbor Model Parameters
		4.3 The Configurational Loop Entropy Model
			4.3.1 Converting from a Structure to a Graph
			4.3.2 Decomposing the Graph into Minimal Graphs
			4.3.3 Converting Each Graph to an Integral
			4.3.4 Graph Decomposition Revisited
			4.3.5 Using the Integrals to Calculate the Configurational Entropy
			4.3.6 The Entropy of Non-pseudoknotted Structures
			4.3.7 The Entropy of Pseudoknotted Structures
	5 The Results of the LandscapeFold Calculation
		5.1 Accessing the Structures and Their Free Energies
		5.2 Multiple Sequences
			5.2.1 Implementation of Multiple Sequences in LandscapeFold
			5.2.2 Potential Speed-ups for Multiple Sequences
			5.2.3 Prediction of Monomer and Dimer Concentrations: User Inputs and Outputs
			5.2.4 Prediction of Monomer and Dimer Concentrations: Details of LandscapeFold´s Process
		5.3 Re-running the Code with Different Parameters
		5.4 Returning Graph Topologies
		5.5 Visualizing Results
	References
Chapter 5: Metrics for RNA Secondary Structure Comparison
	1 Introduction
	2 Materials
	3 Methods
		3.1 Topological Centroid Identification of Plane Graph
		3.2 Comparison of Topological Centroid Trees by Tree Edit Distance
		3.3 Protocol: Run Topological Centroid Identification and Topological Centroid Tree Generation
		3.4 Protocol: Run Topological Centroid Tree Comparison
		3.5 Application Example
	4 Notes
	References
Chapter 6: RNA Secondary Structure Prediction Based on Energy Models
	1 Introduction
	2 Nearest Neighbor Model
	3 Parameterization
		3.1 Thermodynamic Approach
		3.2 Machine Learning-Based Approach
			3.2.1 Probabilistic Approach
			3.2.2 Weight-Based Approach
		3.3 Integrated Approach
	4 Folding Algorithms
		4.1 Minimum Free Energy
		4.2 Partition Function
		4.3 Maximum Expected Accuracy
	5 Comparison of Methods
	6 Prospects and Summary
	References
Chapter 7: RNA Secondary Structure Alteration Caused by Single Nucleotide Variants
	1 Introduction
	2 Methods
		2.1 Radiam Algorithm
		2.2 Preprocessing of Radiam to Construct ParasoR Databases
		2.3 Simulation of Single Point Mutations
		2.4 Computation of p-Values
	3 Notes
	References
Chapter 8: Evolutionary Conservation of RNA Secondary Structure
	1 Introduction
		1.1 Structure Prediction
		1.2 Tools for Studying ncRNAs
		1.3 Evolutionary Conservation and Orthology Annotation
		1.4 Positive Selection Annotation
			1.4.1 Species-Specific Changes
			1.4.2 Rank Statistics
			1.4.3 Selection Scores
		1.5 Family Divergence
		1.6 Evolutionary History of a Structure
		1.7 Visualization of Base Pair Probabilities
	2 Methods
		2.1 Software Installation
		2.2 Usage of the SSS-Test
		2.3 Usage of the Local Structure Pipeline
		2.4 Running the SSS-Test with an Example
		2.5 Running the Local Structure Pipeline with an Example
		2.6 Interpreting Results
		2.7 Analyzing and Filtering Families
		2.8 Analyzing Selection Scores
		2.9 Visual Analysis
	3 Notes
	References
Chapter 9: Network-Based Structural Alignment of RNA Sequences Using TOPAS
	1 Introduction
	2 Network Alignment
	3 Overview of the TOPAS Algorithm
	4 Running the TOPAS Algorithm
	5 Notes
	References
Chapter 10: Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets
	1 Introduction
	2 Basic Algorithms for Fast RNA-RNA Interaction Prediction
	3 Recent Updates of RIblast
	4 Applications of Fast RNA-RNA Interaction Prediction Methods
	5 Conclusion and Future Perspectives
	References
Chapter 11: Web Services for RNA-RNA Interaction Prediction
	1 Introduction
	2 Web Services for RNA-RNA Interaction Prediction
		2.1 IntaRNA and CopraRNA
		2.2 RNAup and RNAcofold
		2.3 TargetScan
		2.4 Snoscan/snoGPS
		2.5 CRISPRdirect
		2.6 LncRRISearch
	3 Conclusion
	References
Chapter 12: ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks
	1 Introduction
	2 Methods
		2.1 ResidualBind Package Overview
			2.1.1 Dependencies
			2.1.2 Source Files
			2.1.3 Example Files
		2.2 Data
		2.3 Secondary Structure Features
		2.4 ResidualBind
		2.5 Training
	3 Model Interpretability with Global Importance Analysis
		3.1 In Silico Mutagenesis
		3.2 Global Importance Analysis
	4 Notes
	References
Chapter 13: RNA Structure Determination by High-Throughput Structural Analysis
	1 Introduction
		1.1 Background
		1.2 Characteristic Features of PROBer, BUMHMM, and reactIDR
	2 Materials
		2.1 Installation
		2.2 Input Data
	3 Methods
		3.1 PROBer
		3.2 BUMHMM
		3.3 reactIDR
	4 Notes
	References
Chapter 14: RNA 3D Modeling with FARFAR2, Online
	1 Introduction
	2 Method
		2.1 The ``Basic´´ Interface to the FARFAR2 ROSIE Server
			2.1.1 Sequence Specification
			2.1.2 Secondary Structure Specification
			2.1.3 Specification of the Number of Structures Generated
			2.1.4 Analysis of the Resulting Structural Ensemble
		2.2 The ``Advanced´´ Interface to the FARFAR2 ROSIE Server
			2.2.1 Sequence Specification Through a Specially Formatted FASTA  File
			2.2.2 Secondary Structure Specification Through an Uploaded  File
			2.2.3 Specification of Noncanonical Pairs
			2.2.4 Chain Connections
			2.2.5 Constraints
			2.2.6 Input Template PDB Files
			2.2.7 Alignment PDB Structure
			2.2.8 Native PDB Structure
			2.2.9 High-Resolution Minimization Settings
			2.2.10 Low-Resolution Fragment Assembly Settings
			2.2.11 Experimental  Data
			2.2.12 Number of Structures to Generate
			2.2.13 Analyze the Results
		2.3 Additional Illustrations of Advanced Interface: Experimental  Data
			2.3.1 MOHCA-Seq with FARFAR2
			2.3.2 Chemical Shift-Guided FARFAR2 (CS-Rosetta-RNA)
	3 Conclusions
	Appendix
	References
Chapter 15: Automated 3D Design and Evaluation of RNA Nanostructures with RNAMake
	1 Introduction
		1.1 RNAMake Software Can Automate the Design of RNA 3D Structures
		1.2 RNAMake-ΔΔG: A Predictive Model for Helical RNA 3D Thermodynamics
	2 Materials
	3 Methods
		3.1 Automatic Design of RNA Nanostructures
		3.2 Performing 3D Thermodynamic Stability Predictions
	References
Chapter 16: RNA 3D Structure Comparison Using RNA-Puzzles Toolkit
	1 Introduction
	2 Materials
		2.1 Input Data
		2.2 Programs in RNA-Puzzles Toolkit
		2.3 Website
	3 Methods
		3.1 Standardize the Structure Format
			3.1.1 Nomenclature Standardization
			3.1.2 Missing Atoms
			3.1.3 Missing Fragments
			3.1.4 Sequence Mismatches
			3.1.5 Chain Identifiers
		3.2 Calculate Structure Comparison Metrics
			3.2.1 Root-Mean-Square Deviation (RMSD)
			3.2.2 Interaction Network Fidelity (INF) and Deformation Index (DI)
			3.2.3 Deformation Profile (DP)
			3.2.4 Other Metrics
		3.3 Visualize the Comparison Results
			3.3.1 PyMOL Visualization
			3.3.2 Structure Clustering
			3.3.3 Contacts Analysis
			3.3.4 Generate Preview Thumbnails
	4 Notes
	References
Index