Bioinformatics: Sequence and Genome Analysis

by David W. Mount

Publisher: Cold Spring Harbor Laboratory; 2nd Edition
ISBN: 0879697121

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A textbook based on Mount's (U. of Arizona) undergraduate and graduate courses on applying computational methods to the analysis of DNA and protein sequences. Convinced that people who use a computer program should understand how it works, he explains to biologists the underlying algorithms used and assumptions made, as well as limitations of the methods and strategies. Most of the chapters include a flowchart proposing an orderly use of the methods discussed therein. Tables in hyperlinked form identify web sites where software and programming resources are available.

Book Description

As more species genomes are sequenced, computational analysis of these data has become increasingly important. The second, entirely updated edition of this widely praised textbook provides a comprehensive and critical examination of the computational methods needed for analyzing DNA, RNA, and protein data, as well as genomes. The book has been rewritten to make it more accessible to a wider audience, including advanced undergraduate and graduate students.

New features include chapter guides and explanatory information panels and glossary terms. New chapters in this second edition cover statistical analysis of sequence alignments, computer programming for bioinformatics, and data management and mining. Practically oriented problems at the ends of chapters enhance the value of the book as a teaching resource. The book also serves as an essential reference for professionals in molecular biology, pharmaceutical, and genome laboratories. The application of computational methods to DNA and protein science is a new and exciting development in biology. Bioinformatics: Sequence and Genome Analysis is a comprehensive introduction to this emerging field of study.

The book has many unique and valuable features:

• It is written for any biologist who wants to understand methods of sequence and structure analysis and how the necessary computer programs work
• Sequence alignment, structure prediction, phylogenetic and gene prediction, database searching, and genome analysis are clearly explained and amply illustrated
• Underlying algorithms and assumptions are clearly explained for the non-specialist
• Examples are presented in simple numerical terms rather than complex formulas and notation
• Theoretical underpinnings are linked to biological problems and their solutions
• Extensive tables provide descriptions and Web sources for a broad range of publicly available software

Based on the author's extensive experience as a molecular geneticist and bioinformaticist at the University of Arizona, this is a uniquely educational book, ideal as a laboratory reference for investigators and also as teaching reference for graduate and undergraduate students studying this fast-changing discipline.

Customer Reviews

This book will give you very strong foundations in the basics of computation in the bio world. Though this book does not give details of the computation methods, it does give a very clear picture of math-ematics and the science involved.>

This book has a good coverage of FASTA and BLAST. (Though a little bit short)

The programming techniques coverd are bare. Though concepts like searching sequences using dynamic programming are covered, you are better off reading something like Proteome Research by wilkins et al.

I am yet to find a good book that deals only with the technical and programming aspects of bio informatics if you do find some thing interesting lemme know.

On the whole this book helped me understand a lot about sequencing, alignment and prediction. The illustrations and pictures provided are good and the text to the point.

If you are reading this review pls understand that I am primarily a programmer trying to get into the bio informatics business. I do not have any schooling or degree or even experience in the bio informatics world.

Decent qualitative overview. Some discussions of algorithms are so superficial that they are misleading. Slick presentation. Used at Stanford's intro to methods course - a good recommendation.

So far, the best there is for a survey course - but for depth and accuracy in sequence analysis algorithms, go to Durbin et al or Gussfield.

Dr. Mount provides an excellent text/guide for those who are interested in making the steep climb into the field of bioinformatics. As a bioinformatics graduate student with an undergraduate degree in biology, I often find that I am over my head in the mathematical jargon that is used in bioinformatics literature. Mount's book gave me the step-up I needed in order to digest the more technically cutting edge alogrithms in the field. He covers all the major areas of bioinformatics (from a biologist's point of view) with the exception of microarray data analysis (which I believe was just coming out at the time the book was printed).

I cannot recommend this text more to anyone who is coming into the field. It is especially useful for senior undergraduates or early graduate students. And don't let the price-tag scare you off... CSHL Press books are always expensive, but they are usually worth every nickle.

The field of bioinformatics has exploded in the last five years, and several monographs and textbooks have appeared to assist in the elucidation of the concepts involved. Bioinformatics is a field that grew hand-in-hand with the rise of the Internet, and anyone going into it will need expertise in the PERL and JAVA programming languages, as well as a fairly strong mathematical background. In this book, the author gives a very good overview of bioinformatics from mostly a qualitative and descriptive point of view, although some elementary mathematical discussions are inserted in various places. Because of the level of mathematics used, this might not be the book to use for the mathematician who desires to go into bioinformatics or computational biology. On the other hand, for the student of biology or mathematics who intends to pursue bioinformatics as a profession, this book would be an excellent choice. One cannot read the book however without visiting its accompanying Website, for the author extends some of the results of the book there.

The book begins with an historical introduction to the subject, and a newcomer to the subject will get a brief overview of some of the first sequence analysis programs and some of the first DNA sequence databases developed long before bioinformatics was recognized as a real discipline. The author introduces some of the techniques that will be discussed in the book, such as global and local sequence alignment, dynamic programming, RNA structure prediction, and protein structure prediction. This is followed in chapter 2 by an overview of the procedures used to collect and store sequences in the laboratory. To the reader not familiar with these techniques, the discussion may be too brief. The different sequence formats used are outlined, as well as techniques used to convert from one sequence format to another.

Chapter 3 takes a closer look at the pairwise alignment of sequences, and the author also outlines the reasons behind examining sequence alignment in the first place, namely that of finding the functional, structural, and phylogenetic information. The view of sequence alignment as an optimization problem is emphasized via the dynamic programming algorithm for sequence alignment. Dot matrix analysis is discussed a sequence alignment strategy that allows all possible matches of residues between two sequences. The author is careful to note that local alignment algorithms might give global alignments, and vice versa, because of small changes in the scoring system. The PAM and BLOSUM substitution matrices are compared as to their relative merits and pitfalls. A very detailed discussion of gap penalties is given, along with the role of the Gumbel extreme value distribution in the determination of the statistical significance of a local alignment score between two sequences. And, after a brief introduction to Bayesian statistics, the author shows how to to use it produce alignments between pairs of sequences and to calculate distances between sequences. The Bayes block aligner software package is discussed in detail as a tool for Bayesian sequence alignment.

In chapter 4, the author gives an extensive discussion of multiple sequence alignment algorithms, the most important of these by contemporary standards being hidden Markov models. The author though does treat the "progressive" methods, as well as the use of genetic algorithms in doing multiple sequence alignment. The former include the classic CLUSTALW package and the PILEUP program for doing msa. Although the discussion of hidden Markov models makes sparing use of mathematics, is does serve to explain how they work and should assist readers who need a solid understanding of them.

I did not read chapters 5 and 6 so I will omit their review. Chapter 7 is an introduction to database searches in order to find similar sequences. The algorithms developed in chapters 3 and 4 again make their appearance, and the reader is confronted with various user interfaces for performing genetic database searching online. The FASTA and BLAST tools are introduced as fast methods to do database searching. As computer performance increases in the years ahead, these and other currently existing tools will no doubt be replaced by more powerful search routines. While perusing this chapter, one cannot help but be fascinated by the current situation in the biological/genetic sciences. Once thought of as a purely descriptive science, it is now dominated by a reductionist philosophy, involving huge amounts of data, and sophisticated mathematics for the analysis of this data.

The author moves on to the methods for detecting protein-encoding regions of DNA sequences in chapter 8. The simplest method according to the author for doing this is to search for ORFs, and he discusses the reliability of methods for accomplishing this. Hidden Markov models again make their appearance as a tool to study eukaryotic internal exons and in gene prediction in microbial genomes. And, neural networks are introduced as tools for finding complex patterns and relationships among sequence positions, and Grail II is discussed as a system for exon finding in eukaryotic genes. Promotor prediction in E. Coli is also briefly overviewed.

I did not read chapter 9 so I will omit its review. Chapter 10 though is an introduction to one of most interesting parts of bioinformatics, namely that of analyzing the entire genomes of organisms. Due to rapid experimental advances in genetics, several genomes are now available, and this allows a more global, dynamical view of the role of genes and how their expression correlates to result in a fully-developed functioning organism. The techniques discussed in earlier chapters come into play in genomic analysis, and many other more novel techniques will have to be invented if sense is to be made of the enormous amount of genomic data currently available.

I am an undergraduate Biotechnology student who is using this book for an intro Bioinformatics class at the Junior/Senior level. It describes the field from a biologists perspective, and doesn't include too much math. It describes the steps that an algorithm within the program would use, and the logic behind them, without going into the complexities of the coding.

While this is a book by and for Biologists, I have found the book to be very rough and in need of extensive editing. On the first test the professor was disappointed as only 1-2 people made it into the A range. At the time I wondered if it was related to the difficulty of the text. To my surprise, my professor began to give us 10 to 20 page handouts per class, covering the material in his lectures. Although he never directly stated this, the handouts were apparently there to make up for the weakness of the text.

It definitely has potential to be a good text for biologists. If the author and editor put in significant work, this could really become a very good book. However I really can't recommend the current edition.

The book has a great table of contents, but it reads more like a first draft than a polished presentation. Many of the techniques are explained in the same style as the research paper they came from, and so the book comes across like a series of synopses, which can make it hard to read. I often found the explanations unclear -- it didn't read like a book that had been class tested.

Still, I have occasionally found some useful tidbits in the book, so I gave it two stars rather than one. There's a good book buried in here -- hopefully the author will prepare a second edition.

David Mount did an excellent job introducing bioinformatics to biologists. Without a lot of Mathematics he explains the algorithms used for sequence alignments or phylogenetics, much better than any other book I have seen. Particularly, I found the chapter about Phylogenetic Prediction very helpful, that shows advantages and disadvantages of the numerous phylogenetic analysis programs with a lot of examples and helps the molecular biologist to decide which one to use. Although this book is quiet expensive, I think it is worth every penny!

This is the required book for our graduate-level computational biology class. I think I represent most of the class when I say the book does a very poor job explaining concepts. There seems to be a pervasive fear of mathematics, which leads to long confusing attempts to explain algorithms by words alone. The writing also seems unnecessarily wordy and opaque. This book also contains many typos, though that may improve in future editions. For some reason, it also costs at least twice as much as most other books on the topic.

The book covers a fairly broad range of topics but still manages to teach you something. It is not one of those books that just list a number of nice web sites to visit. You'll get an understandable explanation e.g. of how sequence alignment really works - or phylogenetic trees and so forth. After reading the book you will have actually understood the underlying concepts and algorithms so you could sit down at your computer and start writing little programms. At the same time the math in the book is not overwhelming to biologists who are probably the main audience of this text. For the seasoned programmer there is enough biological applications in the book to get some ideas what to play with.

Do I have any complaints? The price is a bit over the top!