Marketa J Zvelebil, Jeremy O. Baum
Published September 6, 2007
Textbook - 772 Pages
ISBN 9780815340249 - CAT# GS153
Published September 6, 2007
ISBN 9781136976964 - CAT# YE16108
September 6, 2007
by Garland Science
ISBN 9781136976964 - CAT# YE16108
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Suitable for advanced undergraduates and postgraduates, Understanding Bioinformatics provides a definitive guide to this vibrant and evolving discipline. The book takes a conceptual approach. It guides the reader from first principles through to an understanding of the computational techniques and the key algorithms. Understanding Bioinformatics is an invaluable companion for students from their first encounter with the subject through to more advanced studies.
The book is divided into seven parts, with the opening part introducing the basics of nucleic acids, proteins and databases. Subsequent parts are divided into 'Applications' and 'Theory' Chapters, allowing readers to focus their attention effectively. In each section, the Applications Chapter provides a fast and straightforward route to understanding the main concepts and 'getting started'. Each of these is then followed by Theory Chapters which give greater detail and present the underlying mathematics. In Part 2, Sequence Alignments, the Applications Chapter shows the reader how to get started on producing and analyzing sequence alignments, and using sequences for database searching, while the next two chapters look closely at the more advanced techniques and the mathematical algorithms involved. Part 3 covers evolutionary processes and shows how bioinformatics can be used to help build phylogenetic trees. Part 4 looks at the characteristics of whole genomes. In Parts 5 and 6 the focus turns to secondary and tertiary structure – predicting structural conformation and analysing structure-function relationships. The last part surveys methods of analyzing data from a set of genes or proteins of an organism and is rounded off with an overview of systems biology.
The writing style of Understanding Bioinformatics is notable for its clarity, while the extensive, full-color artwork has been designed to present the key concepts with simplicity and consistency. Each chapter uses mind-maps and flow diagrams to give an overview of the conceptual links within each topic.
The book is divided into seven parts, with the Part 1 introducing the basics of nucleic acids, proteins and databases. Subsequent parts are innovatively divided into "Applications" and "Theory" Chapters, allowing readers to focus their attention more effectively. In each part, the Applications Chapter provides a fast and straightforward route to understanding the main concepts and using the applications for analysis. Each of these is then followed by Theory Chapters which give greater detail and present the underlying mathematics.
Part 1, Background Basics (Ch 1-3)
The opening chapters introduce the raw material of bioinformatics: nucleic acid sequences, proteins and databases.
Part 2, Sequence Alignments (Ch 4-6)
The applications chapter shows the reader how to produce and analyse sequence alignments, while the following two theory chapters look more closely at the more advanced techniques and mathematical algorithms involved.
Part 3, Evolutionary Processes (Ch 7-8)
The applications chapter guides the reader through the process of recovering evolutionary history from the DNA data; the theory chapter gives detailed information on how to construct a phylogenetic tree.
Part 4, Genome Characteristics (Ch 9-10)
The basics of gene prediction are covered in the applications chapter, while the theory chapter elaborates on the basic, and the more advanced, techniques.
Part 5, Secondary Structures (Ch 11-12)
The book’s focus turns to structural aspects of macromolecules, with the first chapter giving an overview of web-based techniques for predicting secondary structure and their application while the second chapter covers the algorithms applied in the techniques.
Part 6, Tertiary Structures (Ch 13-14)
This section looks first at modeling protein tertiary structure using homology modelling, threading and ab initio modeling; the following chapter analyses structure-function relationships.
Part 7, Cells and Organisms (Ch 15-17)
In the closing chapters methods for protein and gene expression are analysed, including techniques of statistical analysis and classification, and the emerging field of systems biology is introduced.
"Congratulations on a fine book! I do not think I have seen such a comprehensive text on bioinformatics algorithms and techniques before. I think this will be an invaluable resource for the bioinformatics community and researchers of neighbouring disciplines." - Jaap Heringa, Free University, Amsterdam
"Many of the students in our program could benefit from reading this guide to genetics and molecular biology terms. Chapter 1 was well organized and easy to follow … I found that the illustrations were familiar and depicted the concepts well." - Sudha Iyengar, Case Western Reserve University, Cleveland, USA
"I found Chapter 6 to be a very detailed and informative overview of multiple alignment and pattern discovery methods for biological sequences. The level of detail was good, as were most of the explanations of the methods. I would use this chapter in an undergraduate course in bioinformatics." - Tim Bailey, University of Queensland, Australia
"This is very well done. Compared to other competing textbooks, your book will be probably the first one that explains gene finding in detail." - Sun Kim, Indiana University, Bloomington, USA
"This chapter is nicely done, an easy read for an upperclassman or graduate student. The chapter reads as a How-To in protein structure prediction." - Chris Bystroff, Rensselaer Polytechnic Institute, Troy, USA, writing about Chapter 13
"I enjoyed reading this chapter, and commend the authors on a well presented and explained introduction to the main algorithms and issues of analyzing this sort of data. The figures are useful and relevant and the techniques presented give a very good representation of the main algorithms in what is a diverse area of analysis.
I believe this chapter to be at a great level for biology graduates, and in fact any student who has not been exposed to data mining concepts previously. I commend that the algorithms have been placed in a biological contexts wherever possible making the concepts more accessible." - Jen Taylor, University of Oxford, UK, writing about Chapter 16
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