Statistical Methods in Bioinformatics [Hardcover]

Warren J. Ewens (Author), Gregory R. Grant (Author)

Book Description

ISBN-10: 0387952292 | ISBN-13: 978-0387952291 | Publication Date: April 20, 2001 | Edition: 1st

Bioinformatics concerns the application of information technology to the study and analysis of biological and in particular genetic, data. The field has been further developed by the increase in DNA data generation. This has led to the generation of massive data sets. However, students of bioinformatics should not simply restrict their interests to computer science theory. Substantial training in various areas of biology, such as molecular genetics, is essential for a deeper understanding of the subject. This book provides an introductory account of probability theory, statistics and stochastic process theory.

Editorial Reviews

Review

From the reviews of the second edition:

"Overall, Ewens and Grant have constructed a needed book in bioinformatics. It should help statisticians understand the emerging field of bioinformatics and serve as an introduction to bioinformatics for a statistician." Journal of the American Statistical Association, March 2006

"This book is the second edition of a book that was based on the content of a two-semester course in bioinformatics and computational biology … . is one of the most important books in this area from the perspective of teaching final year undergraduates and post-graduates in a range of disciplines. … this is a very good book, the best currently available for undergraduates and post-graduates at the intersection of computational biology, bioinformatics, statistics and applied mathematics and a worthwhile improvement on the first edition." (Mark Broom, Journal of the Royal Statistical Society, Vol. 169 (1), 2006)

"This is the second edition of Ewens and Grant’s very well written book on statistical methods in bioinformatics. … The authors have presented an excellent text for a graduate course … . It is clearly and interestingly written and is well organized and has comprehensive references to the literature. The writing style is excellent … . It is … truly a reference book for statistical methods in bioinformatics … . So I strongly recommend the book to both molecular biologists and statisticians … ." (Hamid Pezeshk, ISCB Newsletter, Issue 42, 2006)

"Ewens and Grant aim to fill a gap in the literature on statistics and probability in bioinformatics. … provides a review of the use of familiar statistical techniques and approaches to a new area. … it provides a rigorous treatment of statistical issues associated with bioinformatics tools and a strong statement of the statistical principles and philosophy which needs to underpin these tools. It admirably meets its objectives in this respect and is to be recommended." (David Lovell, Pharmaceutical Statistics, Issue 6, 2007)

"The most impressive achievement of this book is its development of blast theory. … The authors pace the knowledge flow smoothly. … The examples and exercises are well thought and highly motivated … . The authors do a fine job of emphasising the false discovery rate … . This book is structured perfectly for a textbook for everyone, statisticians, biologists and computer scientists. … I think this book does an excellent job in introducing many exciting statistical theories." (Lang Li, Briefings in Bioinformatics, Vol. 6 (4), 2005)

"In this book, Ewens and Grant seek to provide a link between bioinformatics and applied statistics. … The book provides detailed discussions of a number of useful distributions and highlights their role in bioinformatics. I found it quite useful and easy to follow. It is a good reference for multidisciplinary research teams in bioinformatics and students on some specialised taught courses." (Kassim S. Mwitondi, Journal of Applied Statistics, Vol. 33 (8), September, 2006)

--This text refers to the Paperback edition.

From the Back Cover

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.

This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.

The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.

The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.

Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.

Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.

Comments on the first edition:

"This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly." (Biometrics)

"Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces." (Naturwissenschaften)

"The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details." (Journal American Statistical Association)

"The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book." (Metrika)

--This text refers to the Paperback edition.

Product Details

• Hardcover: 552 pages
• Publisher: Springer; 1st edition (April 20, 2001)
• Language: English
• ISBN-10: 0387952292
• ISBN-13: 978-0387952291
• Product Dimensions: 9.6 x 6.4 x 1.1 inches
• Shipping Weight: 1.8 pounds
• Average Customer Review: 3.7 out of 5 stars  See all reviews (7 customer reviews)
• Amazon Best Sellers Rank: #847,411 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews

63 of 65 people found the following review helpful:

4.0 out of 5 stars Pretty good overview, September 18, 2002

By 

Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews
(VINE VOICE)    (HALL OF FAME REVIEWER)    (REAL NAME)   

This review is from: Statistical Methods in Bioinformatics (Hardcover)

This book is a timely introduction to the mathematical statistics used in computational biology and bioinformatics. The authors have done a superb job in the overview of a subject that students of biology and bioinformatics can rely on for study and for reference. The mathematics is done at an advanced undergraduate level, but the authors are pragmatic in their approach, and interlace the discussion with biological applications immediately after the appropriate mathematical background has been developed. It thus seems appropriate to discuss the quality of the presentation with these applications in mind.

Chapter one begins, appropriately, with an introduction to probability theory, with a consideration of discrete probability distributions of one variable beginning the chapter. The Bernoulli, binomial, uniform, geometric, generalized geometric, and Poisson distributions are discussed. The authors point out the use of geometric-like distributions in the BLAST application. The also caution the reader as to the difference between the mean and the average of a random variable. They then move on to consider continuous distributions, discussing briefly the uniform, Normal, exponential, gamma, and beta distributions. Moment-generating functions are also introduced, and they prove a "convexity" theorem for these functions that is important in the BLAST application. The authors also introduce the relative entropy and generalized support statistics, the later also being used in BLAST.

The next chapter is an overview of probability theory in many random variables. The results in chapter one are discussed in this context, and the authors give an interesting application to the sequencing of EST libraries. The authors also point out that the variance of the maximum of a collection random variables is finite as the number of variables increases, a fact that is used quite often in bioinformatics. Transformations of random variables are also discussed, with the goal of showing how these can be used to find the density function of a single random variable, this also being important in BLAST.

The most important subject of the book begins in chapter 3, wherein the authors introduce statistical inference. They begin with a very brief discussion of the differences between the frequentist and Bayesian approaches to statistical inference and then move on to classical hypothesis testing and nonparametric tests. This chapter is of great value to those readers, for example biologists/would-be bioinformaticists who are approaching statistics for the first time.

Chapter 4 introduces concepts that are of upmost importance in probabilistic computational biology, namely Markov chains. The discussion in this chapter sets up the strategies used in the next chapter on analyzing a single DNA sequence and a latter chapter on hidden Markov models. Shotgun sequencing is discussed as a tool to determine the an actual DNA sequence, and the authors discuss the probabilistic issues that arise in the reconstruction of long DNA sequences from shorter sequences. Missing in this chapter is a mathematical analysis of the advantages/disadvantages between shotgun and whole genome sequencing strategies.

Chapter 6 then generalizes the analysis of chapter 5 to multiple DNA and protein sequences. It is here that one begins to talk about alignments between sequences, which bring about some very subtle mathematical problems in computational biology. The computational complexity of the (global) alignment problem entails the use of softer techniques, such as dynamic programming, which is discussed in this chapter. The (local) alignment problem is also discussed in some detail, using the linear gap model. The alignment problem and the issues with scoring for protein sequences are also discussed in detail. The reader first encounters the famous PAM and BLOSUM matrices in this chapter. The authors do not discuss any connections with the protein folding problem, unfortunately.

The next chapter introduces the basic probability theory behind the BLAST algorithm, namely random walks. They do so with emphasis on moment generating functions, which might be a little abstract for the biologist reader.

The authors return to tatistical estimation and hypothesis testing in chapter 8, with maximum liklihood and fixed sample size tests discussed in some detail. Again connecting with the BLAST algorithm, the sequential probability ratio test is treated.

The authors finally get down to the BLAST algorithm in chapter 9, using an older version of the software (1.4). The connection of the algorithm with random walks and how to assign scores is immediately apparent, as is the ability of BLAST to do database queries against a chosen sequence. The algorithm is compared with the sequential analysis discussed in the last chapter.

The authors return to Markov chains in chapter 10, and give some numerical examples. In addition, they treat the important topic of Markov chain Monte Carlo via the Hastings-Metropolis algorithm, Gibbs sampling, and simulated annealing. An application of simulated annealing to the double digest problem is described. The authors also spend a litte time discussing continuous-time Markov chains.

Hidden Markov models are finally discussed in chapter 11. These have been the most effective tools in sequence analysis and the authors give a nice overview of their construction and properties in this chapter. The Pfam package is discussed as a software implementation of HMMs for determining protein domains. Unfortunately, they do not discuss the excellent package HMMER for implementing HMMs in sequence analysis.

Chapter 12 discusses computationally intensive methods in classical inference. One of these methods, the bootstrap procedure, which is used for large sample sizes, is described. Used to estimate confidence intervals in situations where there is not enough information to employ classical methods, the authors detail a method using quantiles to estimate the confidence interval for the standard deviation of the expression intensity of a gene. This is followed by a return to the multiple testing problem of chapter 3 in the context of the data analysis of expression arrays.

I did not read the last two chapters on evolutionary models and phylogenetic tree estimation so I will omit their review.

25 of 25 people found the following review helpful:

5.0 out of 5 stars modern bioinformatics, January 23, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Statistical Methods in Bioinformatics (Hardcover)

This topic should be of prime interest to statisticians. The authors are mathematical biologists and they bring out the theory and methodology in probability and statistics that is applicable to DNA and protein sequencing and matching. They provide a treatment of probability, stochastic processes and statistics that starts with the very basics and builds up.
Topics include basic probability and statistical inference, Poisson processes and Markov chains, DNA sequencing, hidden Markov models, computer intensive methods, evolutionary models and phylogenetic tree estimation.

Of particular interest to me is the material on permutation methods and the bootstrap. The bootstrap has been applied in phylogenetics and there has been some controversy about its application there. The authors cover this in Chapter 14 where they appear to have a resolution for the controversy.

Permutation tests are first discussed in Chapter 3 "A Introduction to Statistical Inferrence" and are compared with other computer intensive methods in Chapter 12. In Section 12.3 they discuss the Behrens-Fisher problem pointing out why permutation tests are not possible due to the unequal variances. They give the bootstrap t solution. Section 12.2.2 gives a brief, but nicely described, account of bootstrap estimation and confidence intervals and provides a number of references including the following books: Efron and Tibshirani (1993), Davison and Hinkley (1997), Efron (1982), Hall (1992), Manly (1997), Sprent (1998) and Chernick (1999). Bootstrap and permutation approaches to multiple testing are covered in Section 12.4.

My review does not really do justice to the detail and significance of the methodology of statistical genetics described in this book. For that I refer you to the detailed amazon review by Lee Carlson.

29 of 30 people found the following review helpful:

5.0 out of 5 stars guide into the right direction, September 6, 2001

By 

Ellen Baake (University of Greifswald, Germany) - See all my reviews

This review is from: Statistical Methods in Bioinformatics (Hardcover)

This is one of the books I have been waiting for. For a population geneticist who wants to learn bioinformatics, most texts are unacceptable: They present heuristic methods in a cookbook fashion, with little reference to what is going on biologically as well as mathematically.

This book is the first exception I know of. It builds, and rests on, solid foundations of genetic stochastic processes and still goes all the way to real-life problems. Let me illustrate this by means of an example, rather than enumerating all the topics in the book.

Chap. 14, entitled `phylogenetic tree estimation' (as opposed to the more common term `phylogenetic tree reconstruction' - not without reason, I presume) builds on, and is firmly interlaced with, Chap. 13 about `evolutionary models', which systematizes the zoo (if not jungle) of substitution models in both discrete and continuous time. On this basis, the overview of tree-building methods makes a lot of sense. Even better, it does not stop here, but presents an application (to real sequence data), followed by a careful analysis of where the various methods agree, and where - and maybe why - they disagree. This way, it clears away some common misconceptions; in particular, it presents a careful analysis of what bootstrap does and what it does not in this context. The chapter closes with a discussion of unresolved problems (like inhomogeneity of substitution rates), and methods and possible pitfalls related to testing of nested and non-nested hypotheses in tree estimation.

The book is written in an informal style without being imprecise, which makes it pleasant reading. It is particularly suitable for teaching at a high level. This is enhanced by realistic (and even real-life) examples that furnish the text, as well as carefully chosen exercises at the end of each chapter.

Certainly, this first edition of `Statistical Methods in Bioinformatics' cannot be the last word in this fast-moving field. But it is an excellent guide into the `right' direction.