Gorodkin J., Ruzzo W.L. (eds.) RNA Sequence, Structure, and Function. Computational and Bioinformatic Methods

Humana Press, 2014. — 547 p.
Series: Methods in Molecular Biology, №1097The existence of noncoding RNA genes (ncRNAs) was proposed simultaneously with protein coding genes in 1961 by Jacob and Monod. Since then, a substantial focus has been on protein coding genes, while the area of ncRNA evolved more slowly and received less attention even though major breakthroughs were made, such as the discovery of RNA’s ability to carry out catalytic function, which also gave rise to the hypothesis that life originated from an RNA world, given that RNA also can store genetic information. It was also revealed early on that RNA might often be more conserved in structure rather than sequence. It quickly became apparent that adding consideration of structure to RNA sequence analysis programs was far more computationally demanding than, for example, comparing DNA by the primary sequence as in the case of sequence alignment methods. Whereas a pairwise sequence alignment scales with the square of the length being aligned, folding a single sequence scales with the cube of the length. Thus, doubling the length makes the raw version of the alignment methods run four times longer, but RNA folding algorithms run eight times longer. Thus, a likely contribution to the relative neglect of RNA bioinformatics in the early days can probably be attributed to the fact that this is a harder problem than many other bioinformatics problems. Today, much faster computers plus meaningful heuristics have made it possible to engineer practical RNA bioinformatics tools. Though RNA bioinformatics is still in its early phase with respect to practicality of genome-scale analyses, computational tools might help in uncovering the extent of RNAs in genomes. Given that, for example, protein coding sequence makes up about 1.2% of the human genome and that most of the genome is transcribed, this leaves an enormous potential for noncoding transcripts that might carry out a function and thus qualify as an ncRNA.
The ncRNAs have now been recognized as an abundant class of genes which often function through their structure. Protein coding genes have also been recognized to contain RNA structural motifs or RNA structures involved in, for example, regulation. Since even before the word bioinformatics was coined, researchers have been developing tools and computational methodologies for the analysis of RNA sequences, for aiding RNA (secondary) structure determination, for functional studies, and for a range of subsequent disciplines rooted in the principles for RNA structure prediction. Recognizing that RNA structure is a characteristic feature of ncRNAs, these tools have enabled genome-scale, in silico screens for ncRNAs. Furthermore, the same basic principles underlying RNA folding algorithms have been extended to a range of related problems such as homology search, design of interfering RNAs, and prediction of RNA–RNA interactions, to mention some examples. This book addresses a range of these methodologies from both a practical point of view as well from a computational and algorithmic perspective. Traditionally, the computational methods were referred to as computational RNA biology. However, with the recent applications on genomic and transcriptomic data, the more applied side of computational RNA biology, focused on processing experimental data (especially high-throughput data) is more commonly covered by the term Bioinformatics. This book covers a substantial and relevant fraction of both these directions and addresses both the biologist interested in knowing more about RNA bioinformatics as well as the bioinformaticist interested in aspects of the engine room.
Recent technological development pushes high-throughput data generation and motivates further improvement of the generally computational resource demanding programs in RNA bioinformatics, a cost inherited from the generic RNA folding algorithms. Whereas these issues are addressed and the concepts of many methods shown, it is beyond this book to enter the area of assembly and read mapping. Here, we walk through the key methods and principles of RNA bioinformatics. Whereas a substantial part of the methodologies originate in the principles employed for prediction of RNA secondary structure, they employ further layers for specific applications as well as restrictions to reduce computation time and memory requirements, for example. In particular, developments in this respect have pushed for making methods in computational RNA biology applicable within RNA bioinformatics. Here, we range from the methodologies to their actual applications.Concepts and Introduction to RNA Bioinformatics
The Principles of RNA Structure Architecture
The Determination of RNA Folding Nearest Neighbor Parameters
Energy-Directed RNA Structure Prediction
Introduction to Stochastic Context Free Grammars
An Introduction to RNA Databases
Energy-Based RNA Consensus Secondary Structure Prediction in Multiple Sequence Alignments
SCFGs in RNA Secondary Structure Prediction: A Hands-on Approach
Annotating Functional RNAs in Genomes Using Infernal
Class-Specific Prediction of ncRNAs
Abstract Shape Analysis of RNA
Introduction to RNA Secondary Structure Comparison
RNA Structural Alignments, Part I: Sankoff-Based Approaches for Structural Alignments
RNA Structural Alignments, Part II: Non-Sankoff Approaches for Structural Alignments
De Novo Discovery of Structured ncRNA Motifs in Genomic Sequences
Phylogeny and Evolution of RNA Structure
The Art of Editing RNA Structural Alignments
Automated Modeling of RNA 3D Structure
Computational Prediction of RNA–RNA Interactions
Computational Prediction of MicroRNA Genes
MicroRNA Target Finding by Comparative Genomics
Bioinformatics of siRNA Design
RNA–Protein Interactions: An Overview