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Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids Paperback – May 13, 1998

ISBN-13: 978-0521629713 ISBN-10: 0521629713 Edition: 1st

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Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids + Problems and Solutions in Biological Sequence Analysis + Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology
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Product Details

  • Paperback: 356 pages
  • Publisher: Cambridge University Press; 1 edition (May 13, 1998)
  • Language: English
  • ISBN-10: 0521629713
  • ISBN-13: 978-0521629713
  • Product Dimensions: 9.4 x 7.1 x 0.8 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (23 customer reviews)
  • Amazon Best Sellers Rank: #111,691 in Books (See Top 100 in Books)

Editorial Reviews

Review

"The book is amply illustrated with biological applications and examples." Cell

"...successfully integrates numerous probabilistic models with computational algorithms to solve molecular biology problems of sequence alignment...an excellent textbook selection for a course on bioinformatics and a very useful consultation book for a mathematician, statistician, or biometrician working in sequence alignment." Bulletin of Mathematical Biology

"This is one of the more rewarding books I have read within this field. My overall evaluation is that this book is very good and a must read for active participants in the field. In addition, it could be particularly useful for molecular biologists" Theoretical Population Biology

Book Description

Probabilistic methods are assuming greater significance in the analysis of nucleotide sequence data. This book provides the first unified, up-to-date and self-contained account of such methods, and more generally of probabilistic methods of sequence analysis, presented in a Bayesian framework.

Customer Reviews

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Mandatory that this is on every computational biologist/bioinformatics scientist bookshelf!
Mgavi Brathwaite
The package specifically uses hidden Markov models to perform sequence analysis using the methods outlined in the book.
Dr. Lee D. Carlson
The illustrations provided are extremely helpful, including their positioning on pages where they're relevant.
Bob Carpenter

Most Helpful Customer Reviews

37 of 40 people found the following review helpful By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on April 1, 2001
Format: Paperback
This book is a very well written overview to hidden Markov models and context-free grammar methods in computational biology. The authors have written a book that is useful to both biologists and mathematicians. Biologists with a background in probability theory equivalent to a senior-level course should be able to follow along without any trouble. The approach the author's take in the book is very intuitive and they motivate the concepts with elementary examples before moving on to the more abstract definitions. Exercises also abound in the book, and they are straightforward enough to work out, and should be if one desires an in-depth understanding of the main text. In addition, there is a software package called HMMER, developed by one of the authors (Eddy) that is in the public domain and can be downloaded from the Internet. The package specifically uses hidden Markov models to perform sequence analysis using the methods outlined in the book.
Probabilistic modeling has been applied to many different areas, including speech recognition, network performance analysis, and computational radiology. An overview of probabilistic modeling is given in the first chapter, and the authors effectively introduce the concepts without heavy abstract formalism, which for completeness they delegate to the last chapter of the book. Bayesian parameter estimation is introduced as well as maximum likelihood estimation. The authors take a pragmatic attitude in the utility of these different approaches, with both being developed in the book.
This is followed by a treatment of pairwise alignment in Chapter Two, which begins with substitution matrices. They point out, via some exercises, the role of physics in influencing particular alignments (hydrophobicity for example).
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23 of 25 people found the following review helpful By Bob Carpenter on April 28, 2002
Format: Paperback Verified Purchase
I picked up this book at the recommendation of a number of colleagues in computational linguistics and speech processing as a way to find out what's going on in biological sequence analysis. I was hoping to learn about applications of the kinds of algorithms I know for handling speech and language, such as HMM decoding and context-free grammar parsing, to biological sequences. This book delivered, as recommended.
As the title implies, "Biological Sequence Analysis" focuses almost exlusively on sequence analysis. After a brief overview of statistics (more a reminder than an introduction), the first half of the book is devoted to alignment algorithms. These algorithms take pairs of sequences of bases making up DNA or sequences of amino acids making up proteins and provide optimal alignments of the sequences or of subsequences according to various statistical models of match likelihoods. Methods analyzed include edit distances with various substitution and gapping penalties (penalties for sections that don't match), Hidden Markov Models (HMMs) for alignment and also for classification against families, and finally, multiple sequence alignment, where alignment is generalized from pairs to sets of sequences. I found the section on building phylogenetic trees by means of hierarchical clustering to be the most fascinating section of the book (especially given its practical application to classifying wine varietals!). The remainder of the book is devoted to higher-order grammars such as context-free grammars, and their stochastic generalization. Stochastic context-free grammars are applied to the analysis of RNA secondary structure (folding).
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14 of 16 people found the following review helpful By biochemprof on April 17, 2001
Format: Paperback
This is the best introduction to latest probabilistic sequence analysis methods. However, the book suffers from somewhat convoluted writing and organization. More importantly, it lacks a broader theoretical overview of the different methods. The methods are presented as a bunch of tools without enough critical assessment of their effectiveness or the relative strengths of their underlying theoretical models. I would have welcomed more discussion of how they all fit in a bigger probabilistic picture... what are the different simplifications and assumptions made for the sake of simplicity and computation?
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20 of 24 people found the following review helpful By A Customer on May 5, 2000
Format: Paperback
This is that rarest of rare: an intro-level, multi-authored monograph which is thorough, internally consistent, and a joy to read. Unlike the reviewer above, although not the first book I read on the subject, had it been so, I could have saved myself a great deal of time. As an introductory work it is simply unparalleled. In view of the rate of information growth in the field, this reader thinks it deserves to be amended annually, not merely reprinted periodically. The authors and editors are to be congratulated for producing a real gem. Your choice of which of the 30 or so "advanced" (i.e., costing >$60) books on probabilistic sequence analysis will be much more informed if you read this one first.
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5 of 5 people found the following review helpful By Jurgen Van Gael on September 29, 2005
Format: Paperback
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 ...
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