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This book presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters.
- Sales Rank: #1172118 in Books
- Published on: 2007-08-13
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x 1.25" w x 6.14" l, 2.10 pounds
- Binding: Hardcover
- 535 pages
Review
From the reviews:
"The book is useful for its breadth. An impressive variety of topics are surveyed...." Short Book Reviews of the ISI, June 2006
"It is a very good book indeed and I would strongly recommend it both to the student hoping to take this study further and to the general reader who wants to know what computational genome analysis is all about." Mark Bloom for the JRSS, Series A, Volume 169, p. 1006, October 2006
"Richard C. Deonier, Simon Tavare and Michael S. Waterman provide us wtih a 'roll up your sleeves and get dirty' (as the authors phrase it in their preface) introduction to the field of computational genome analysis...The book�is carefully written and carefully edited..." Ralf Schmid for Genetic Research, Volume 87, p. 218, 2006�
"This book provides an introduction to a broad spectrum of the biological and computation background required for genome analysis. Topics are illustrated with examples and exercises. … The computational problems encourage the reader to investigate concepts using R. The book is very useful for its breadth. An impressive variety of topics are surveyed … . This book is a useful starting point." (D. F. Andrews, Short Book Reviews, Vol. 26 (1), 2006)
"This book provides a practical introduction to computational molecular biology and bioinformatics. One of the strengths of the text is the breadth of the material … . The mathematical and statistical concepts … are presented clearly with the necessary detail. The book is nicely organized with a useful glossary and many informative tables and figures. The text is highly recommended for a course for upper level undergraduates or beginning graduate students, or as a reference for researchers … ." (Sharon M. Crook, Mathematical Reviews, Issue 2006 i)
"This book is broad and deep in its coverage, with chapters on genome assembly and comparative genomics (including gene prediction) as well as more common topics. ...Overall, this book and the book by Jones and Pevzner (2004) are the best texts that I have seen in the area..." (Paul Havlak, Journal of the American Statistical Association, Vol. 102, No. 477, 2007)
"‘Computational Genome Analysis: an introduction’ is a new teaching book aimed at master and PhD students. … As stated in its preface, this book is an introduction to the computational side of genomics and bioinformatics. In my opinion the authors largely succeed in providing just that. … the book should help physicists and computational scientists to simultaneously learn what type of computational problems are addressed in bioinformatics and what the biology behind these problem is." (Berend Snel, Mathematical Biosciences, Vol. 208, 2007)
"The goal of computational genomics is the understanding and interpretation of information encoded and expressed from the entire genetic complement of biological organisms: the genome. This book provides an introduction to the subject, on the level of a senior or first-year graduate-level course, to students from a variety of backgrounds. It is addressed to biologists, applied mathematicians, computer scientists, and persons working in the biotechnology industry." (Quarterly of Applied Mathematics, Vol. 66 (2), 2008)
From the Back Cover
Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.
This book features:Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation.
Presentation of fundamentals of probability, statistics, and algorithms.
Implementation of computational methods with numerous examples based upon the R statistics package.
Extensive descriptions and explanations to complement the analytical development.
More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature.
Exercises at the end of chapters.
Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.
Simon Tavar� holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavar�'s research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavar� is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.
Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.
About the Author
Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.
Simon Tavar� holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavar�'s research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavar� is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.
Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.
Most helpful customer reviews
6 of 6 people found the following review helpful.
Very nice book, but not really for beginners
By B. Mayes
This textbook is used as the main text for one of my graduate courses. It is a well written book and contains a plethora of information. The problem is that I find myself constantly re-reading sections and walking through examples to thoroughly understand them. Nothing seems to click the first time I read through the information (or sometimes even second, third, etc.).
This is my first time taking any coursework in the bioinformatics field so perhaps it is simply because this material is new to me, but I found this book fairly difficult to read. I had to supplement it with other books, wikipedia entries, etc. to be able to understand many of the terms (which this book fails to define).
If you're willing to put forth the effort of filling in the gaps, then this is a great book. If you already have a strong background in computer science and biology then this is likely an excellent book for reference material, or to expand you knowledge in an already familiar area.
Also note that there is a large amount of discussion of probability in this area of study. You may wish to brush up on your skills in probability prior to reading this.
6 of 8 people found the following review helpful.
"Computational genome analysis: An Introduction" Deonier R., Tavare S., Waterman M. Springer-Verlag New York, Inc., Secaucus, NJ
By L. C Silvern
This textbook was based on the authors' instructional experiences in undergraduate Computational Biology courses for Bachelor seniors, first-year Master's, and Ph.D. students at the University of Southern California. Readers could also include investigators in medical schools, computer scientists, biologists, applied mathematicians, biochemists, and persons working in the biotechnology industry.
This text is based on the classic man-machine-work model in which a human performs laboratory-level work while also interacting with a digital computer. The complete inventory of all DNA that determines the identity of an organism is known as the genome. The computer or 'machine' utilizes the R language and produces statistical solutions dealing with genomes. The objects analyzed fall into these categories: the basic unit of life or the cell; the chemical energy stored in ATP (Adenosine triphosphate), the genetic information encoded by DNA (Deoxyribonucleic Acid) , and that information transcribed into RNA (Ribonucleic Acid). Since all life on the planet is based on cells, except for viruses, one can see why this volume is an important contribution to the scientific knowledge base particularly with reference to the evolution of species.
The R language developed at Bell Laboratories is used throughout the text. R is a probability statistics environment available for free download and can be used with Windows, Macintosh, and Linux operating systems. It functions very much like the S-PLUS statistics package. Since the reader would need to know how to actually implement the concepts in computa�tional biology to fully understand them, the authors include examples of computations using R. This volume is described as a "roll up your sleeves and get dirty" introduction to the computational side of genomics and bioinformatics. It is intended to provide a foundation for an intelligent application of the available computational tools and for in�tellectual growth as new experimental approaches lead to new computational tools.
One must accept the fact that analyzing cells, DNA, and RNA is based on probability statistics. The text utilizes 1% algebra, 1 % integral calculus and 98% probability statistics --- the 98% being processed in R language. It isn't intended to describe the laboratory processes and protocols used to manipulate the samples but it does directly connect the computer solutions to the laboratory or work activity. Each chapter ends with a number of problems; while this is typical of the classical textbook, it would have been helpful if a teacher's answer book had been appended.
The Chapter headings are: Biology in a Nutshell; Words, Word Distributions and Occurences; Physical Mapping of DNA; Genome Rearrangements; Sequence Alignment; Rapid Alignment Methods: FASTA and BLAST; DNA Sequence Assembly; Signals in DNA; Similarity, Distance, and Clustering; Measuring Expression of Genome Information; Inferring the Past: Phylogenetic Trees; Genetic Variation in Populations; Comparative Geonomics; Glossary; A Brief Introduction to R; Internet Bioinformatics Resources; Miscellaneous Data.
Leonard C. Silvern
Systems Engineering Laboratories
Clarkdale, AZ
0 of 0 people found the following review helpful.
Introduction to genome analysis
By Jose I. Miranda
Dears:
The book is really a good intro to the subject. This was my first book on the subject and I think I did the right choice and ended up with a very good feeling on what means the application of computers and statistics on genome stuff. But I think the book's title rather be "Statistical Genome Analysis," due to the fact that the authors give more strength on the statistics techniques used when analyzing genome data, what is cool. "Computational" is tied to some R codes, shown throughout the book, actually, very good hints on using R to do some basic stuff with genome data. Of course, due to the date of publication of the book (2005) many web links are outdated or doesn't exist any more. But nothing that a Google search couldn't solve it. And, of course, due to the accelerated advance of the technology in the field of genomics, like sequencing, some concepts are outdated, too. I have heard from some bioinformatics PhD that microarray tech, for example, is with its days numbered, entering RNA-seq.
Of course, this doesn't take the merits of the book. If you, reader of this note, is interested in buying the book, go on and do it! You, like myself, will not be disappointed.
See all 4 customer reviews...
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