Statistical Methods in Bioinformatics

1. Front Matter

   Pages i-xix

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2. Probability Theory (i): One Random Variable

   • Warren J. Ewens, Gregory R. Grant

   Pages 1-54

3. Probability Theory (ii): Many Random Variables

   • Warren J. Ewens, Gregory R. Grant

   Pages 55-104

4. Statistics (i): An Introduction to Statistical Inference

   • Warren J. Ewens, Gregory R. Grant

   Pages 105-127

5. Stochastic Processes (i): Poisson Processes and Markov Chains

   • Warren J. Ewens, Gregory R. Grant

   Pages 129-145

6. The Analysis of One DNA Sequence

   • Warren J. Ewens, Gregory R. Grant

   Pages 147-180

7. The Analysis of Multiple DNA or Protein Sequences

   • Warren J. Ewens, Gregory R. Grant

   Pages 181-217

8. Stochastic Processes (ii): Random Walks

   • Warren J. Ewens, Gregory R. Grant

   Pages 219-236

9. Statistics (ii): Classical Estimation and Hypothesis Testing

   • Warren J. Ewens, Gregory R. Grant

   Pages 237-267

10. BLAST

    • Warren J. Ewens, Gregory R. Grant

    Pages 269-302

11. Stochastic Processes (iii): Markov Chains

    • Warren J. Ewens, Gregory R. Grant

    Pages 303-325

12. Hidden Markov Models

    • Warren J. Ewens, Gregory R. Grant

    Pages 327-347

13. Computationally Intensive Methods

    • Warren J. Ewens, Gregory R. Grant

    Pages 349-363

14. Evolutionary Models

    • Warren J. Ewens, Gregory R. Grant

    Pages 365-384

15. Phylogenetic Tree Estimation

    • Warren J. Ewens, Gregory R. Grant

    Pages 385-422

16. Back Matter

    Pages 423-476

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Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. There is now a necessity for a text that introduces probability and statistics in the bioinformatics context. This book also describes some of the main statistical applications in the field, including BLAST, gene finding, and evolutionary inference, much of which has not yet been summarized in an introductory textbook format.
This book grew out of the bioinformatics courses given at the University of Pennsylvania. The material is, however, organized to appeal to biologists or 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 in bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematics background consists of courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context.

• BLAST
• BLAST-Algorithmus
• DNA
• Markov chain
• Random variable
• bioinformatics
• biology
• computational biology
• evolution
• hidden markov model
• mathematics
• protein

From the reviews:

SIAM REVIEW

"…the book covers an impressive array of topics and provides ample material for a two-semester graduate course…Where possible the concepts are illustrated with interesting examples from bioinformatics, and many of these examples, together with the exercises at the end of each chapter, could be used to liven up any introductory course on discrete probability…My favorite feature of the book is the scattering throughout of clear and very detailed descriptions of several commonly used procedures from bioinformatics, each of which might otherwise require a tedious trawl through the primary literature to locate…I recommend the book for those who want to know more about statistics in bioinformatics as it is currently practiced and as a very helpful resource for anyone preparing a course in bio informatics or computational biology."

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

"The book is generally well written, well organized and worthwhile…topics are well chosen to give a sense of the problems and approaches used in bioinformatics…successfully fills the gap that it was intended to fill. The highlights of this book are the chapters that actually cover bioinformatics. These are well written and at a good level for an introductory course. These chapters would also make a good reading for statisticians looking for an easy introduction to bioinformatics concepts."

SHORT BOOK REVIEWS

"The book is self-contained and even includes a few pieces from calculus. The first four chapters are a solid introduction to basic probability theory, stochastic processes and statistics. The material is well seasoned with examples and problems related to genetics. Starting with chapter 6, the authors focus their efforts on modeling and DNA and protein sequences. ... The book is a very substantial and highly professional contribution to bioinformatics and applied statistics."

MATHEMATICALREVIEWS

"This well-written textbook gives a survey of statistical, probabilistic and optimization methods that are used in bioinformatics. Without giving too many theoretical details, the book explains clearly how statistical and probabilistic techniques can be used to address bioinformatical problems…This book should be of interest both to graduate students and to trained statisticians, who want to learn more about the role of statistics in this fast growing field of application."

STATISTICAL METHODS IN MEDICAL RESEARCH

"This book provides an excellent survey of statistical analyses of biological sequence data and brief treatments of other areas of bioinformatics...The explanations and derivations of difficult ideas are usually clear. Frequent examples of bioinformatics applications help to maintain interest and to elucidate the statistical concepts presented. Without being excessively mathematical, the authors succeed in accurately presenting the assumptions and limitations of the statistical methods...This book describes and impressive breadth of applications including methods of sequencing, modeling searching, aligning and comparing DNA and protein sequences...this book I strongly recommended for an overview of statistical sequence analyses and for use in an advanced class in bioinformatics."

• Department of Biology, University of Pennsylvania, Philadelphia, USA

  Warren J. Ewens

• Penn Center for Computational Biology, University of Pennsylvania, Philadelphia, USA

  Gregory R. Grant

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