Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

Book Description

Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.

Customer Reviews:

Great reference.......2007-09-06

A great reference and a good introduction to many important concepts in sequence analysis. However, if you don't have a reasonable grounding in math you may struggle with the terse notation.

Borodovsky's companion book is an excellent partner for this book. Get both.

One of the best available.......2007-08-17

Although this book is based primarily on work that was completed in 1998, and therefore somewhat out of date, it is the best book I have found for teaching bioinformatics. I selected this as the best of the available books on the subject for use in my bioinformatics and numerical methods course which is to be taught in the fall of 2007 at Univ. of Conn. This course is an upper division undergraduate and first year graduate course. That is roughly the level of this text and the comparative advantage of this book is the excellent presentation and thorough discussion of the algorithms. A student armed with Matlab or MathScriptor can take this book and start writing algorithms for sequence alignment and Hidden Markov Method (HMM) analysis after only the first three or four chapters. This book is in its 11th printing and is nearly error free (I found only a few in the figures). This book is strongly recommended for both students and researchers, particularly those interested in protein alignment, phylogenic analysis or an introduction to Hidden Markov Methods.

Biological Sequence Analysis.......2006-03-07

This is a very good book. I got it for a class and it is very helpful and insightful.

Truly an Excellent Book.......2006-02-18

I will agree and submit: this is an invaluable introduction to the field of bioinformatics. With introductions to everything from sequence analysis to hidden markov models and even a primer on grammars, this is a useful introduction both to biological applications for computer scientists *as well as* computational methods for biologists.

I am in a joint graduate-level biology/computer science class and we are using this book as a foundation to bring both groups up to speed and it seems to be working out nicely.

However, one criticism is that sometimes Durbin et al jump into subjects without an adequate introduction or with one that is overcomplexified. In other words, they sometimes break Einstein's the rule of "make everything as simple as possible but not simpler". Durbin et al do not always make things as simple as possible. And it is annoying when they do not. Especially when I see them confusing the bejebus out of the biology people over computer science concepts that are really not that complicated through overly technical jargon.

But this is rare and they provide many insightful diagrams to clear up their algorithms as well as lucid ways to introduce biological concepts. Sometimes the introduction of an algorithm/theory *and* a biological concept molds together beautifully such that the reader is simultaneously being infused with both. An example of this phenomenon is their dual introduction to CpG islands and markov models.

Excellent book ... a little boring to read ..........2005-09-30

I bought "Biological Sequence Analysis" for my introductory bioinformatics course. AS the course covers almost everything mentioned in the book I have (almost) finished reading and studying it.

I find this book an excellent textbook but wouldn't consider it a classic. There are some important topics missing or some topics are just briefly touched upon. (e.g. heuristic pairwaise alignment) Maybe it's just because of my theoretical background, but I find that the book does a poor job in explaining/proving the intuition behind certain aspects of the algorithms (e.d. why does a convex gap penalty lead to a different complexity than a strictly increasing gap penalty ...) . On the other hand, the probabilistic foundations of the different techniques is well written.

My final remark is that the book is not fun to read at all. The authors have made no effort to spice up the content with some historical background, some explanations of how the theory fits in the bigger picture ...

Summarized: an excellent textbook for anyone taking a course in bioinformatics but do not use this book to wet your appetite for the field ...

Statistical Methods in Bioinformatics: An Introduction (STATISTICS FOR BIOLOGY AND HEALTH)

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Lots of material made accessible
Most Elegant Account of Bioinformatics

Statistical Methods in Bioinformatics: An Introduction (STATISTICS FOR BIOLOGY AND HEALTH)
Warren J. Ewens
Manufacturer: Springer
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ASIN: 0387400826

Book Description

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 courseâ€¦[and] is equally well suited to individual studyâ€¦. 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 Staistical. 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).

Customer Reviews:

Lots of material made accessible.......2007-10-10

I'm a Statistics PhD student so you can condition on my prior to get at what's really going on with this book.

Bioinformatics is a departure from "regular" statistics and looks awfully messy at first pass. The sorts of assumptions one typically makes in other areas of statistical inference are patently false, so new techniques and intuitions have to be built up in order to attack these kinds of problems. This book does an excellent job of balancing the technical details with the necessary intuitions so one can really get a firm grasp on what's going on.

I wouldn't recommend this book to someone who hasn't done statistics at at least an advanced undergrad level (e.g., comfortable with Probability at the Ross-level and Statistical Inference at the Casella/Berger-level). But for people really interested in the material and coming from a solid statistical background the book is an excellent resource.

I would also strongly recommend it to teach out of.

Most Elegant Account of Bioinformatics.......2004-11-27

I was impressed with the 1st edition of this book for its most comprehensive and elegant of statistical techniques in bioinformatics. The book is slightly below the level of the now classic M S Waterman (1995)book:Introduction to Computational Biology: Maps, Sequences and Genomes. But this book is more update in some areas and has much more background materials on probability and statistics, which should provide a solid basis for understanding bioinformatics. Its pedagorical sense is unparalleled. It would make a very good choice for a stat/math oriented introduction to bioinformatics (as opposed to algorithimc/database oriented approach in cs).

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Get a solid foundation for microarray data analysis.
a great book to read about microarray data analysis
Simple Great
Excellent book. Highly recommended!
Introduction to Statistical Data Analysis of Microarrays

Data Analysis Tools for DNA Microarrays
Sorin Draghici
Manufacturer: CRC Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 1584883154

Book Description

Technology today allows the collection of biological information at an unprecedented level of detail and in increasingly vast quantities. To reap real knowledge from the mountains of data produced, however, requires interdisciplinary skills-a background not only in biology but also in computer science and the tools and techniques of data analysis. To help meet the challenges of DNA research, Data Analysis Tools for DNA Microarrays builds the foundation in the statistics and data analysis tools needed by biologists and provides the overview of microarrays needed by computer scientists. It first presents the basics of microarray technology and more importantly, the specific problems the technology poses from the data analysis perspective. It then introduces the fundamentals of statistics and the details of the techniques most commonly used to analyze microarray data. The final chapter focuses on commercial applications with sections exploring various software packages from BioDiscovery, Insightful, SAS, and Spotfire. The book is richly illustrated with more than 230 figures in full color and comes with a CD-ROM containing full-feature trial versions of software for image analysis (ImaGene, BioDiscovery Inc.) and data analysis (GeneSight, BioDiscovery Inc. and S-Plus Array Analyzer, Insightful Inc.). Written in simple language and illustrated in full color, Data Analysis Tools for DNA Microarrays lowers the communication barrier between life scientists and analytical scientists. It prepares those charged with analyzing microarray data to make informed choices about the techniques to use in a given situation and contribute to further advances in the field.

Customer Reviews:

Get a solid foundation for microarray data analysis........2007-02-18

I'm more than 2/3 through the book and I've never encountered a topic that I feel could have been better presented. My definition of a Great book is that I can understand and follow it, and this definitely is a Great book! Thanks to the author for writing such readable text. This text has not made it to my bookshelf at work, it stays on my desk.

a great book to read about microarray data analysis.......2006-08-07

I have entered the area of microarray data analysis three years ago, having an engineering/machine learning background which includes good knowledge of statistics. After reading many journal papers about particular algorithms for microarray data analysis, I felt the need to read a book so that I could get the big picture of the field. At the beginning I was skeptical about reading Draghici's book because it was recommended to me as "excellent" by a biologist. I was pretty sure that given my background I will get bored of it quickly. My intuition failed me in this case because after reading it, I found it too as being far from ordinary, and answering my needs as well.

The book is an easy-to-follow introduction to the area of microarray data analysis covering areas from image analysis and preprocessing, to differential expression, clustering, and high level analysis such as ontological analysis. The book is particularly useful in underlying common pitfalls with microarray data. Examples include failing to correct for multiple testing in microarray experiments and the misuse or overuse of the clustering algorithms. Abounding examples and clear illustration are given to support every single aspect treated in the text. In my opinion, graduate level students in biology, bioinformatics and statistics can greatly benefit from the lecture of this book.

Another positive aspect is the fact that, with the exception of one chapter about the available commercial software, this book was written by just one author. This gives a continuity of ideas and a consistency of notations and terms throughout the entire book. This is usually not found in many other books on this topic as they are sometimes just edited collections of chapters written independently by different authors (see for instance the text by Berrar et. al which has about 40 contributors).

A great incentive for me in writing this review was reading an overzealous critique to this book, written by Eric Wu in this webpage. I found some of his comments to be particularly misleading and out of context. For instance he says "the book only deals with the bare minimum of data analysis". Compared with other books in the field, the topics about data analysis covered in the book are not only more numerous but much more thoroughly explained. This book does not expedite the reader to some references but cares about explaining the things. If this book is the "bare minimum" at 500 pages, how is Mr. Wu going to characterize the other well known books in the field such as Knudsen, Simon, Speed, Baldi, etc. which have at most half as many as this book has. Knudsen, for instance, takes the reader from absolute measurements to and including ANOVA in 17 pages. Draghici covers the same topics in 7 chapters or about 250 pages, and that would be without counting the chapters on the basic statistics or image analysis. Another example of biased assessment is when Mr. Wu says "Exploratory data visualizing and data mining algorithms are not covered thoroughly in this book. For example, principal component analysis (PCA) is presented as a subsection of a chapter." The PCA description in the book is more than just fine to me. The book is not supposed to be an encyclopedia of statistics. What the reader needs to know is how PCA can help with the visualization of these multidimensional data sets and not necessarily give all the details about PCA.
A last example I give of superficial judgment in Mr. Wu's view is the so called "inflation of Type I error rate". Mr. Wu says: "... if the probability of making a correct conclusion excludes the probability of making Type II errors, 1 - p should be stated as the probability of not making Type I errors".. In general, this statement would be true. However, the paragraph from the book to which Mr. Wu is referring to actually starts by saying: "When the t statistic for a gene is more extreme than the threshold..." etc. If the observed statistic is more extreme than the threshold, the statistical reasoning requires us to reject the null hypothesis. In this case type II errors (false negatives) CANNOT occur. Hence, in this case, the probability of drawing the correct conclusion is indeed 1-p, exactly as stated in the book.

Overall, I find that the value you get per dollar spent when buying this book is high, and thereby I would strongly recommend it.

Dr. Adi L. Tarca, Windsor (CANADA)

Simple Great.......2006-05-16

This book is a must to understand fundamental statistical analysis of microarray data. Must have it.

Excellent book. Highly recommended!.......2006-04-04

Being a book worm, as soon as I started working with microarrays I bought a bunch of books on the subject. After six months working with this technique and reading chapters on all the books I've bought I can say with certainty that Draghici's is the best introductory book on microarrays. Other books around are better at describing protocols or explaining the math involved in microarray data analysis but Draghici's book does a very good job at explaining how to analyse microarray data for the biologist (and maybe for other publics but statisticians). Everytime some friend ask me for hints on chapters or books to read for learning (or re-learning) statistics I suggest this book. The first chapters are an excellent review of the basics of statistics necessary for day to day practice. The only complain I have is that the shareware software that comes with the book does not work anymore (it's trial period has already expired and therefore it is not possible to install it even if you get a brand new book). I read this book from cover to cover and I think that, considering how readable it is, anyone could do it.

Introduction to Statistical Data Analysis of Microarrays.......2004-09-28

The targeted audience of this book is biologists who are eager to get an understanding of the analysis tools they use for microarrays. The book does an excellent job addressing this tier of audience.

The book has plenty of examples. Almost all the examples, whether fake or real, are microarray-related. Whenever needed, figures or charts are provided to illustrate ideas. A few chapters that introduce basic statistical concepts provide solved problems and exercises. All these efforts are worthwhile making difficult statistical concepts easy to understand in the context of microarrays and making the book especially valuable for biologists who do not have strong background in statistics.

This book has an emphasis on major statistical aspects of microarray data analysis. There are 17 chapters in this book. About 8 of them are directly related to statistics. Especially, there is one whole chapter devoted to multiple hypothesis testing, one chapter for ANOVA, and one chapter for experimental design. The above subjects are presented in a thorough, yet easy-to-follow style. Statistical issues are often not well addressed in published papers using microarrays. This book on microarray data analysis does an excellent job emphasizing this aspect.

The title of the book indicates "data analysis". However, since this is not a clearly defined term, you should be aware that the book only deals with "the bare minimum" of data analysis. That is routines, such as normalization, transformation, statistical testing, and clustering, that have to be carried out each and every time. Exploratory data visualizing and data mining algorithms are not covered thoroughly in this book. For example, principal component analysis (PCA) is presented as a subsection of a chapter. It does not provide explanations on concepts such as loading factors nor scree test. Series data (e.g. time series) are on two pages only and there is no mention of Fourier transformation. Support vector machine (SVM), which is widely used today as a supervised classification method, is not presented at all.

As I mentioned at the beginning, the targeted audience is biologists. If you are a statistician or a bioinformatician who wants to mathematically explore data analysis algorithms, you should look somewhere else. You may be disappointed that many concepts are not rigorously or accurately defined in this book. For example, the book uses capital letters to denote random variables. But the concept of random variables is not rigorously defined in the book. One of the consequences is the weak definition of mathematical expectation. Another example is the inflation of Type I error rate. On page 220, the author claims that the probability of "drawing the correct conclusion" is 1 - p, where p is the calculated probability of a statistic versus a parameter. However, if the probability of making a correct conclusion excludes the probability of making Type II errors, 1 - p should be stated as the probability of not making Type I errors.

In summary, this is a good book on microarray analysis tools for biologists using microarrays. However, people who are seeking in-depth descriptions of these algorithms should look somewhere else.

Statistical Methods in Molecular Evolution (STATISTICS FOR BIOLOGY AND HEALTH)

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Excelent Reference Book on Molecular Evolution

Statistical Methods in Molecular Evolution (STATISTICS FOR BIOLOGY AND HEALTH)
Rasmus, Ed. Nielsen
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0387223339

Book Description

In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics.

Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders of field and they will take the reader from basic introductory material to the state-of-the-art statistical methods.

This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory.

Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole RÃ¸mer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book.

From the reviews:

"...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society

"I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006

"Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006

"Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological research under the umbrella of molecular evolution. Although Nielsen did not contribute a chapter to his book, his work permeates all its chapters. This book gives an overview of his interests and current achievements in molecular evolution. In short, this book should be on your bookshelf." Peter Beerli for Evolution, 60(2), 2006

Customer Reviews:

Excelent Reference Book on Molecular Evolution.......2005-10-14

For those biologist and mathematicians willing to expand their knowledge in Molecular Evolution, this book is the right source. Chapters were written by leading researchers in their fields and are divided in sections from basic concepts to more advanced methods of molecular analysis. Language is clear, topics well organized. I recommend this book to any grad student, post doc or researcher interested in clear, informative reviews in several areas of Molecular Evolution.

Statistical Methods in Genetic Epidemiology

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Statistical Methods in Genetic Epidemiology
Duncan C. Thomas
Manufacturer: Oxford University Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 019515939X

Book Description

This well-organized and clearly written text has a unique focus on methods of identifying the joint effects of genes and environment on disease patterns. It follows the natural sequence of research, taking readers through the study designs and statistical analysis techniques for determining whether a trait runs in families, testing hypotheses about whether a familial tendency is due to genetic or environmental factors or both, estimating the parameters of a genetic model, localizing and ultimately isolating the responsible genes, and finally characterizing their effects in the population. Examples from the literature on the genetic epidemiology of breast and colorectal cancer, among other diseases, illustrate this process. Although the book is oriented primarily towards graduate students in epidemiology, biostatistics and human genetics, it will also serve as a comprehensive reference work for researchers. Introductory chapters on molecular biology, Mendelian genetics, epidemiology, statistics, and population genetics will help make the book accessible to those coming from one of these fields without a background in the others. It strikes a good balance between epidemiologic study designs and statistical methods of data analysis.

Statistical Approach to Genetic Epidemiology: Concepts and Applications

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Statistical Approach to Genetic Epidemiology: Concepts and Applications
Andreas Ziegler
Manufacturer: John Wiley & Sons, Inc.
ProductGroup: Book
Binding: Hardcover

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ASIN: 3527312528

Book Description

Covering the latest developments, this advanced textbook is the first to focus on introducing the relevant statistical methods applied in this field. Written by the prize-winning scientist Andreas Ziegler, President of the German Region of the International Biometric Society, and Inke KÃ¶nig, who contributes more than five years of teaching experience, this is ideal for epidemiologists, geneticists, statistics specialists, biomathematicians, and graduate students.
After providing a concise introduction to genetic fundamentals, the authors explain both linkage analysis and association analysis in detail. The textbook features more than 100 problems and solutions.
With a foreword by Robert C. Elston, Director, Division of Genetic and Molecular Epidemiology at the Case Western Reserve University, Cleveland, Ohio.

Linkage Disequilibrium and Association Mapping: Analysis and Applications (Methods in Molecular Biology (Cloth))

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Linkage Disequilibrium and Association Mapping: Analysis and Applications (Methods in Molecular Biology (Cloth))
Andrew R., Ed. Collins
Manufacturer: Humana Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 1588296695

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As researchers continue to make enormous progress in mapping disease genes, exciting, novel, and complex analyses have emerged. In Linkage Disequilibrium and Association Mapping: Analysis and Applications, scientists from around the world, who are leaders in this field, contribute their vast experience and expertise to produce a comprehensive and fascinating text for researchers and clinicians alike.

The volume comprises four general sections: the first presents an overview and historical basis of the subject. The second section considers the developing methodology and recent findings from studies which have characterized the genome-wide linkage disequilibrium structure in enormous detail. The following section examines all aspects of disease association mapping methodology, and the final two chapters review the early successes in mapping genes involved in two of the most important human diseases: asthma and type 2 diabetes.

The Statistics of Gene Mapping (STATISTICS FOR BIOLOGY AND HEALTH)

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The Statistics of Gene Mapping (STATISTICS FOR BIOLOGY AND HEALTH)
David Siegmund
Manufacturer: Springer
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Binding: Hardcover

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ASIN: 038749684X

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Gene mapping is used in experimental genetics to improve the hardiness or productivity of animals or plants of agricultural value, to explore basic mechanisms of inheritance, or to study animal models of human inheritance. In human populations it is used as a first step to identify genes associated with human health and disease. This book presents a unified discussion of the statistical concepts applied in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. The development involves elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. The viewpoint reflects the modern approach of using anonymous DNA markers distributed throughout the genome to identify regions likely to contain genes of interest. The reader is assumed to have some familiarity with probability/statistics and with elementary genetics. Important topics are reviewed in the first three chapters. The R programming language is developed in the text. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The book is suitable for upper level undergraduate students or graduate students of genetics or statistics.

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Narrow and shallow
Short but helpful

Mathematics of Genome Analysis
Jerome K. Percus
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0521585260

Book Description

The massive research effort known as the Human Genome Project is an attempt to record the sequence of the three trillion nucleotides that make up the human genome and to identify individual genes within this sequence. The description and classification of sequences is heavily dependent on mathematical and statistical models. This short textbook presents a brief description of several ways in which mathematics and statistics are being used in genome analysis and sequencing.

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Customer Reviews:

Narrow and shallow.......2003-10-10

Genome analysis is a huge field - any title that promises to address it all has taken on a huge task.

This brief book does not deliver on the title's promise. It provides a cursory introduction to the assembly problem. That intro is so brief, however, that I don't think a reader will come away understanding what genome assembly is really about.

It continues with a disappointing analysis of nucleotide frequencies. The probability analysis is competent enough, within its limits, but I don't see any mention of why the analysis is interesting, or how to extend it the same techniques proteins. The author proposes spectral analysis as a tool, and argues for Walsh vectors as basis functions. Spectral analysis is offbeat, to say the least, but the author does not explain what (if any) biological insight the technique generates. More mainstream tools, including Markov Models, get little or no mention.

The chapter on sequence comparison is so short and skips so much critical material, that I'm tempted to call it negligent.

Perhaps you have specific reason for wanting the narrow and idiosyncratic view that Percus brings. ...

Short but helpful.......2002-01-02

This book is a short overview of some of the important mathematical techniques used to study genome sequences. In spite of the length of the book, the author does a fine job of introducing these techniques. Students of computational biology will especially benefit from its perusal.

The first section is a brief overview of the structure of DNA, m-RNA, and t-RNA. Recognizing that DNA is two large for direct analysis, restriction fragments are discussed in the second section, with emphasis on the restriction-enzyme fingerprint. The author's goal is to find the probability of occurences of a 6-letter word in a strand and the mean distance between occurrences of this word (assuming no overlap between the words or the occurences and equal probabilities for the bases). The effect of successive pair correlation (Markov chain effect) is considered briefly. This is followed by a calculation of the probability that a base pair is contained in a given clone. The author omits any discussion of algorithms for optical mapping, but does give a brief discussion of restriction maps.

The mathematics becomes more rigorous in chapter two, wherein the author analyzes a chain that exists as a set of cloned subchains with unknown overlap. This is the 'fingerprint assembly' problem the object of which is to produce a physical map of the full sequence. The fingerprint of the clone is a collection of lengths of a particular restriction fragments. This algorithm involves a sequence of contiguous clones called 'islands'; and 'contigs', which are two or more clones. The average number and size of islands are calculated assuming that the clones have equal length and identical overlap threshold. The method of anchoring is also discussed as a second method for obtaining the physical map of the genome. The author then considers the problem of covering the whole sequence by first placing n markers on a genome and covering by intervals centered at these markers. This is the restriction-fragment-length polymorphism analysis, the combinatorics of which the author solves by using Laplace and Fourier transforms. He also considers adaptive and non-adaptive pooling, in order to find a particular set of proteins on a large fragment.

The third chapter addresses sequence statistics, with the author addressing the nonhomogeneity of sequences and the correlation dependence in the bases. The chi-square test is discussed is some detail and the author discusses the accuracy of the Markov chain assumption. Noting that very long chains would be needed to determine the parameters for the expressions for the conditional correlations, he uses the maximum likelihood method to find the intrinsic correlation length, and then estimates the parameters by modeling the parameter set.

The author then studies the isochore regions and discusses their detection via the Jensen-Shannon entropy. Asking whether there are correlations between these long regions and within them motivates him to consider the long-range properties of DNA. This leads to the examination of a long fragment of a single strand of DNA, and with the assumption that strand-symmetry holds, the correlation coefficients are studied, with the decay properties of the auto- and cross-correlation discussed. Then, distinguishing only dual pairs, the author considers the probability that a pair is separated by an integer after an integral number of steps, a calculation that reduces to finding the largest eigenvalue of a 'transfer matrix', a procedure well-known in statistical physics.

Next, a consideration of simple sequence repeats leads to a difference equation that is solved by the method of moments. Windows of bases are then discussed, in order to improve on the statistics. Correlations within and between windows are calculated. Interestingly, the consideration of long-range correlations gives a power-law dependence for the correlations, which is related to the Hurst index for self-similar patterns. Readers get their first taste of hidden Markov models in this chapter, which are currently very popular in sequence analysis. Even more interesting is the discussion of walking Markov models, wherein a first-order base-to-base Markov chain is chosen to depend on a hidden parameter, and the time evolution is shown to satisfy a Fokker-Planck (diffusion) equation. Spectral analysis and information theoretic criteria are also discussed.

In the next chapter of the book, the author considers the most important part of sequence analysis, namely the comparison between sequences according to their linear ordering. The problem is to find the probability of a common subsequence of two linear chains with a given length. The first calculation assumes that the matches are mutually exclusive, and the result is an upper bound on the probability. The author then considers the matches to be independent events, and again bounds are given for the probability, the so-called Chen-Stein estimate). He also gives an estimate of the probability in terms of an asymptotic series. Extreme value methods are then used to calculate the expectation value and the variance of the length of the longest match. An interesting exercise is assigned for the reader; namely of finding the effect on the Fourier and Walsh power spectrum with the assumption that the base correlations are fractal in form. The alignment problem is then generalized to include replication errors, mutations, etc. The chapter ends, appropriately, with a discussion of multisequence comparison. The author poses the problem as one of finding the best match of a word to an n-tuple of words, which he tackles first using 'information content'. The category analysis of separating subsequence configurations into clusters is briefly discussed via simulated annealing, discriminant analysis, Bayesian analysis, and neural networks.

Book Description

Genetic Data Analysis, first published in 1990, became the standard reference for ways to interpret discrete population genetic data. Genetic Data Analysis II retains the strengths of the original book and, based upon the suggestions of users, includes many new features, notably the revision of Chapter 10 (Phylogeny Reconstruction) to incorporate newer methods, and new chapters on Linkage and Individual Identification.

Genetic Data Analysis II features an expanded set of Exercises, with solutions, and an expanded list of references. In addition, a suite of Windows-based programs written by Paul O. Lewis and Dmitri Zaykin is available without charge from the Web site maintained by the program in Statistical Genetics at North Carolina State University.

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