Rna Sequence Structure And Function Computational And Bioinformatic Methods
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RNA Sequence, Structure, and Function
The existence of genes for RNA molecules not coding for proteins (ncRNAs) has been recognized since the 1950's, but until recently, aside from the critically important ribosomal and transfer RNA genes, most focus has been on protein coding genes. However, a long series of striking discoveries, from RNA's ability to carry out catalytic function, to discovery of riboswitches, microRNAs and other ribo-regulators performing critical tasks in essentially all living organisms, has created a burgeoning interest in this primordial component of the biosphere. However, the structural characteristics and evolutionary constraints on RNA molecules are very different from those of proteins, necessitating development of a completely new suite of informatic tools to address these challenges. In RNA Sequence, Structure, Function: Computational and Bioinformatic Methods, expert researchers in the field describe a substantial and relevant fraction of these methodologies from both practical and computational/algorithmic perspectives. Focusing on both of these directions addresses both the biologist interested in knowing more about RNA bioinformatics as well as the bioinformaticist interested in more detailed aspects of the algorithms. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results. Thorough and intuitive, RNA Sequence, Structure, Function: Computational and Bioinformatic Methods aids scientists in continuing to study key methods and principles of RNA bioinformatics.
RNA: Computational Methods for Structure, Kinetics, and Rational Design: Volume One
Comprising two volumes, RNA: Computational Methods for Structure, Kinetics, and Rational Design is a comprehensive treatment of computational methods concerning the secondary structure, folding kinetics and rational design of RNA. Volume One concerns energy and structure and is divided into five chapters. Chapter 1 describes the molecular structure of ribonucleotides, basic classes of RNA and databases of RNA sequences and structure. Chapter 2 presents the basic concepts of thermodynamics, since thermodynamics-based algorithms constitute an essential tool in rational design of functional RNA molecules. Chapter 3 describes how empirical secondary structure energy parameters are obtained from ultraviolet absorbance experiments via Van 't Hoff plots and least-squares data fitting. Chapter 4 describes methods from combinatorics, automata and formal language theory, and complex analysis. Chapter 5 provides an overview of some of the most important thermodynamics-based algorithms related to secondary structure. Exercises and solutions are provided at the end of every chapter and source code is available at the book's website (sometimes including computer programs using Python and extensions Numpy and Scipy). This book provides the nuts, bolts and tools to take the next steps in computational RNA synthetic biology. It is perfect for advanced undergraduate, graduate and post-graduate readers having analytical interests and skills from areas such as physical chemistry, physics, mathematics, computer science, and statistics.
Bioinformatics and Computational Biology
Bioinformatics and Computational Biology: Technological Advancements, Applications and Opportunities is an invaluable resource for general and applied researchers who analyze biological data that is generated, at an unprecedented rate, at the global level. After careful evaluation of the requirements for current trends in bioinformatics and computational biology, it is anticipated that the book will provide an insightful resource to the academic and scientific community. Through a myriad of computational resources, algorithms, and methods, it equips readers with the confidence to both analyze biological data and estimate predictions. The book offers comprehensive coverage of the most essential and emerging topics: Cloud-based monitoring of bioinformatics multivariate data with cloud platforms Machine learning and deep learning in bioinformatics Quantum machine learning for biological applications Integrating machine learning strategies with multiomics to augment prognosis in chronic diseases Biomedical engineering Next generation sequencing techniques and applications Computational systems biology and molecular evolution While other books may touch on some of the same issues and nuances of biological data analysis, they neglect to feature bioinformatics and computational biology exclusively, and as exhaustively. This book's abundance of several subtopics related to almost all of the regulatory activities of biomolecules from where real data is being generated brings an added dimension.