Comprehensive and concise, this handbook has chapters on computing visualization, large database designs, advanced pattern matching and other key bioinformatics techniques. It is a practical guide to computing in the growing field of Bioinformatics--the study of how information is represented and transmitted in biological systems, starting at the molecular level.
This textbook introduces fundamental concepts of bioinformatics and computational biology to the students and researchers in biology, medicine, veterinary science, agriculture, and bioengineering . The respective chapters provide detailed information on biological databases, sequence alignment, molecular evolution, next-generation sequencing, systems biology, and statistical computing using R. The book also presents a case-based discussion on clinical, veterinary, agricultural bioinformatics, and computational bioengineering for application-based learning in the respective fields. Further, it offers readers guidance on reconstructing and analysing biological networks and highlights computational methods used in systems medicine and genome-wide association mapping of diseases. Given its scope, this textbook offers an essential introductory book on bioinformatics and computational biology for undergraduate and graduate students in the life sciences, botany, zoology, physiology, biotechnology, bioinformatics, and genomic science as well as systems biology, bioengineering and the agricultural, and veterinary sciences.
This book introduces some key problems in bioinformatics, discusses the models used to formally describe these problems, and analyzes the algorithmic approaches used to solve them. After introducing the basics of molecular biology and algorithmics, Part I explains string algorithms and alignments; Part II details the field of physical mapping and DNA sequencing; and Part III examines the application of algorithmics to the analysis of biological data. Exciting application examples include predicting the spatial structure of proteins, and computing haplotypes from genotype data. Figures, chapter summaries, detailed derivations, and examples, are provided.
Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication. The work is organized into five parts: * Algorithms and models * Sequence analysis and microarrays * Phylogenetics * Protein folding * Platforms and enabling technologies Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries.
Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
This practical, hands-on guide shows how to develop a structured approach to biological data and the tools needed to analyze it. It's aimed at scientists and students learning computational approaches to biological data, as well as experienced biology researchers starting to use computers to handle data.
This book offers comprehensive coverage of all the core topics of bioinformatics, and includes practical examples completed using the MATLAB bioinformatics toolboxTM. It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics. This chapter will enable physical science students to fully understand and appreciate the ultimate goals of applying the principles of information technology to challenges in biological data management, sequence analysis, and systems biology. The first part of the book also includes a survey of existing biological databases, tools that have become essential in today’s biotechnology research. The second part of the book covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The main focus of the third part is on modeling biological sequences and patterns as Markov chains. It presents key principles for analyzing and searching for sequences of significant motifs and biomarkers. The last part of the book, dedicated to systems biology, covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In brief, the book offers the ideal hands-on reference guide to the field of bioinformatics and computational biology.
Overview and Goals This book describes how to visualize and compare bacterial genomes. Sequencing technologies are becoming so inexpensive that soon going for a cup of coffee will be more expensive than sequencing a bacterial genome. Thus, there is a very real and pressing need for high-throughput computational methods to compare hundreds and thousands of bacterial genomes. It is a long road from molecular biology to systems biology, and in a sense this text can be thought of as a path bridging these ? elds. The goal of this book is to p- vide a coherent set of tools and a methodological framework for starting with raw DNA sequences and producing fully annotated genome sequences, and then using these to build up and test models about groups of interacting organisms within an environment or ecological niche. Organization and Features The text is divided into four main parts: Introduction, Comparative Genomics, Transcriptomics and Proteomics, and ? nally Microbial Communities. The ? rst ? ve chapters are introductions of various sorts. Each of these chapters represents an introduction to a speci? c scienti? c ? eld, to bring all readers up to the same basic level before proceeding on to the methods of comparing genomes. First, a brief overview of molecular biology and of the concept of sequences as biological inf- mation are given.
Bioinformatics Algorithms: an Active Learning Approach is one of the first textbooks to emerge from the recent Massive Online Open Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed online course (http://coursera.org/course/bioinformatics), this book presents students with a dynamic approach to learning bioinformatics. It strikes a unique balance between practical challenges in modern biology and fundamental algorithmic ideas, thus capturing the interest of students of biology and computer science students alike.Each chapter begins with a central biological question, such as "Are There Fragile Regions in the Human Genome?" or "Which DNA Patterns Play the Role of Molecular Clocks?" and then steadily develops the algorithmic sophistication required to answer this question. Hundreds of exercises are incorporated directly into the text as soon as they are needed; readers can test their knowledge through automated coding challenges on Rosalind (http://rosalind.info), an online platform for learning bioinformatics.The textbook website (http://bioinformaticsalgorithms.org) directs readers toward additional educational materials, including video lectures and PowerPoint slides.
Bioinformatics, Biocomputing and Perl presents a modern introduction to bioinformatics computing skills and practice. Structuring its presentation around four main areas of study, this book covers the skills vital to the day-to-day activities of today’s bioinformatician. Each chapter contains a series of maxims designed to highlight key points and there are exercises to supplement and cement the introduced material. Working with Perl presents an extended tutorial introduction to programming through Perl, the premier programming technology of the bioinformatics community. Even though no previous programming experience is assumed, completing the tutorial equips the reader with the ability to produce powerful custom programs with ease. Working with Data applies the programming skills acquired to processing a variety of bioinformatics data. In addition to advice on working with important data stores such as the Protein DataBank, SWISS-PROT, EMBL and the GenBank, considerable discussion is devoted to using bioinformatics data to populate relational database systems. The popular MySQL database is used in all examples. Working with the Web presents a discussion of the Web-based technologies that allow the bioinformatics researcher to publish both data and applications on the Internet. Working with Applications shifts gear from creating custom programs to using them. The tools described include Clustal-W, EMBOSS, STRIDE, BLAST and Xmgrace. An introduction to the important Bioperl Project concludes this chapter and rounds off the book.
