This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.

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Thoroughly Describes Biological Applications, Computational Problems, and Various Algorithmic Solutions

Developed from the author’s own teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied in bioinformatics. For each topic, the author clearly details the biological motivation and precisely defines the corresponding computational problems. He also includes detailed examples to illustrate each algorithm and end-of-chapter exercises for students to familiarize themselves with the topics. Supplementary material is available at http://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/

This classroom-tested textbook begins with basic molecular biology concepts. It then describes ways to measure sequence similarity, presents simple applications of the suffix tree, and discusses the problem of searching sequence databases. After introducing methods for aligning multiple biological sequences and genomes, the text explores applications of the phylogenetic tree, methods for comparing phylogenetic trees, the problem of genome rearrangement, and the problem of motif finding. It also covers methods for predicting the secondary structure of RNA and for reconstructing the peptide sequence using mass spectrometry. The final chapter examines the computational problem related to population genetics.

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Bioinformatics is interpreted as the application of information science to biology, in which it plays a fundamental and all-pervasive role. The field continues to develop intensively in both academia and commercially, and is highly interdisciplinary. This broad-ranging and thoroughly updated second edition covers new findings while retaining the successful formula of the original text.

Bioinformatics: An Introduction is structured into three parts devoted to Information, Biology, and Applications. Every section of the book has been completely revised for currency, and expanded where relevant, to take account of significant new discoveries and realizations of the importance of key concepts. Furthermore, two new chapters provide instruction about algorithms and knowledge representation. Emphases are placed on the underlying fundamentals and on acquisition of a broad and comprehensive grasp of the field as a whole.

Features:

• Provides a solid foundation in, and self-contained introduction to, the field of bioinformatics and its state-of-the-art as it relates to computational biology research

• Imparts a thorough grounding of core concepts, enabling the reader to understand contemporary work within an optimal context

• Includes examples, definitions, problems and a comprehensive and useful bibliography

• Offers additional chapters on algorithms and knowledge representation, including text mining [NEW]

• Presents new experimental methods, and serves as a springboard for new research [NEW]

• Contains a greatly expanded chapter on interactions and regulatory networks [NEW]

• Incorporates discussion of the method of drawing inferences from abstract sequence analysis based on frequency dictionaries

• Contains an extensively revised chapter on medical applications [NEW]

• Emphasizes the underlying fundamentals and acquisition of a broad and comprehensive grasp of the field as a whole

This significantly improved second edition of a successful textbook is intended to be a complete study companion for the advanced undergraduate or graduate student. It is self-contained, bringing together the multiple disciplines necessary for a profound grasp of the field into a coherent whole, thereby allowing the reader to gain much insight into the state-of-the-art of bioinformatics.

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Presenting an area of research that intersects with and integrates diverse disciplines, including molecular biology, applied informatics, and statistics, among others, Bioinformatics for Omics Data: Methods and Protocols collects contributions from expert researchers in order to provide practical guidelines to this complex study.  Divided into three convenient sections, this detailed volume covers central analysis strategies, standardization and data-management guidelines, and fundamental statistics for analyzing Omics profiles, followed by a section on bioinformatics approaches for specific Omics tracks, spanning genome, transcriptome, proteome, and metabolome levels, as well as an assortment of examples of integrated Omics bioinformatics applications, complemented by case studies on biomarker and target identification in the context of human disease.  Written in the highly successful Methods in Molecular Biology™ series format, chapters contain introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and notes on troubleshooting and avoiding known pitfalls.

Authoritative and accessible, Bioinformatics for Omics Data: Methods and Protocols serves as an ideal guide to scientists of all backgrounds and aims to convey the appropriate sense of fascination associated with this research field.

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This book details the statistical concepts used in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.

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Science used to be experiments and theory, now it is experiments, theory and computations. The computational approach to understanding nature and technology is currently flowering in many fields such as physics, geophysics, astrophysics, chemistry, biology, and most engineering disciplines. This book is a gentle introduction to such computational methods where the techniques are explained through examples. It is our goal to teach principles and ideas that carry over from field to field. You will learn basic methods and how to implement them. In order to gain the most from this text, you will need prior knowledge of calculus, basic linear algebra and elementary programming.

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This book provides a foundation for understanding the fundamentals of biomedical informatics, which deals with the storage, retrieval, and use of biomedical data for biological problem solving and medical decision making. It covers the application of these principles to the three main biomedical domains of basic biology, clinical medicine, and public health. The author offers a coherent summary, focusing on the three core concept areas of biomedical data and knowledge representation, biomedical information access, biomedical decision making, and information and technology use in biomedical contexts.

* Develops principles and methods for representing biomedical data, using information in context and in decision making, and accessing information to assist the medical community in using data to its full potential

* Provides a series of principles for expressing biomedical data and ideas in a computable form to integrate biological, clinical, and public health applications

* Includes a discussion of user interfaces, interactive graphics, and knowledge resources and reference material on programming languages to provide medical informatics programmers with the technical tools to develop systems

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Elements of Scientific Computing (Texts in Computational Science and Engineering)

Science used to be experiments and theory, now it is experiments, theory and computations. The computational approach to understanding nature and technology is currently flowering in many fields such as physics, geophysics, astrophysics, chemistry, biology, and most engineering disciplines. This book is a gentle introduction to such computational methods where the techniques are explained through examples. It is our goal to teach principles and ideas that carry over from field to field. You will learn basic methods and how to implement them. In order to gain the most from this text, you will need prior knowledge of calculus, basic linear algebra and elementary programming.

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Price: $ 52.00

The rapidly developing methods of systems biology can help investigators to make inference and predictions from their data that intuition alone would not discern; however, the difficulty lies in the broad knowledge of mathematics and engineering that seems to be required to even evaluate the applicability of the methods to the problem at hand. In “Systems Biology”, scientists who possess that mathematical knowledge and have applied it effectively to various biological problems contribute methods which reflect their experience, crystallized in a form very similar to typical biological laboratory protocols. In order to best aid researchers, the chapters supply the theory behind the method, its step-by-step implementation, and the pitfalls explained thoroughly and from the practical angle. As a volume of the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results.

Cutting-edge and easy to use, “Systems Biology” shatters the perceived insurmountable barrier between the laboratory and systems-biological research techniques.

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As biotechnology defines the new millennium, genetic codes and computer codes increasingly merge—life understood as data, flesh rendered programmable. Where this trend will take us, and what it might mean, is what concerns Eugene Thacker in this timely book, a penetrating look into the intersection of molecular biology and computer science in our day and its likely ramifications for the future.

Integrating approaches from science and media studies, Biomedia is a critical analysis of research fields that explore relationships between biologies and technologies, between genetic and computer “codes.” In doing so, the book looks beyond the familiar examples of cloning, genetic engineering, and gene therapy—fields based on the centrality of DNA or genes—to emerging fields in which “life” is often understood as “information.” Focusing especially on interactions between genetic and computer codes, or between “life” and “information,” Thacker shows how each kind of “body” produced—from biochip to DNA computer—demonstrates how molecular biology and computer science are interwoven to provide unique means of understanding and controlling living matter.

Throughout, Thacker provides in-depth accounts of theoretical issues implicit in biotechnical artifacts—issues that arise in the fields of bioinformatics, proteomics, systems biology, and biocomputing. Research in biotechnology, Biomedia suggests, flouts our assumptions about the division between biological and technological systems. New ways of thinking about this division are needed if we are to understand the cultural, social, and philosophical dimensions of such research, and this book marks a significant advance in the coming intellectual revolution.

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Programming knowledge is often necessary for finding a solution to a biological problem. Based on the author’s experience working for an agricultural biotechnology company, Python for Bioinformatics helps scientists solve their biological problems by helping them understand the basics of programming. Requiring no prior knowledge of programming-related concepts, the book focuses on the easy-to-use, yet powerful, Python computer language.

The book begins with a very basic introduction that teaches the principles of programming. It then introduces the Biopython package, which can be useful in solving life science problems. The next section covers sophisticated tools for bioinformatics, including relational database management systems and XML. The last part illustrates applications with source code, such as sequence manipulation, filtering vector contamination, calculating DNA melting temperature, parsing a genbank file, inferring splicing sites, and more. The appendices provide a wealth of supplementary information, including instructions for installing Python and Biopython and a Python language and style guide.

By incorporating examples in biology as well as code fragments throughout, the author places a special emphasis on practice, encouraging readers to experiment with the code. He shows how to use Python and the Biopython package for building web applications, genomic annotation, data manipulation, and countless other applications.

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