• RRP: £34.99
  • You Save: £3.50 (10%)
FREE Delivery in the UK.
In stock.
Dispatched from and sold by Amazon. Gift-wrap available.
Biological Sequence Analy... has been added to your Basket
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See all 3 images

Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids Paperback – 23 Apr 1998

3.7 out of 5 stars 3 customer reviews

See all formats and editions Hide other formats and editions
Amazon Price
New from Used from
Kindle Edition
"Please retry"
"Please retry"
"Please retry"
£20.88 £20.41
Note: This item is eligible for click and collect. Details
Pick up your parcel at a time and place that suits you.
  • Choose from over 13,000 locations across the UK
  • Prime members get unlimited deliveries at no additional cost
How to order to an Amazon Pickup Location?
  1. Find your preferred location and add it to your address book
  2. Dispatch to this address when you check out
Learn more
£31.49 FREE Delivery in the UK. In stock. Dispatched from and sold by Amazon. Gift-wrap available.
click to open popover

Frequently Bought Together

  • Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
  • +
  • Introduction to Bioinformatics
Total price: £62.98
Buy the selected items together

Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.

  • Apple
  • Android
  • Windows Phone

To get the free app, enter your mobile phone number.

Product details

  • Paperback: 370 pages
  • Publisher: Cambridge University Press; First Edition edition (23 April 1998)
  • Language: English
  • ISBN-10: 0521629713
  • ISBN-13: 978-0521629713
  • Product Dimensions: 17.4 x 1.9 x 24.7 cm
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Bestsellers Rank: 161,387 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description


'This book fills an important gap in the bioinformatics literature and should be required reading for anyone who is interested in doing serious work in biological sequence analysis. For biologists who have little formal training in statistics or probability, it is a long-awaited contribution that, short of consulting a professional statistician who is well versed in molecular biology, is the best source of statistical information that is relevant to sequence-alignment problems. This book seems destined to become a classic. I highly recommend it.' Andrew F. Neuwald, Trends in Biochemical Sciences

'This book is a nice tutorial and introduction to the field and can certainly be recommended to all who wish to analyse biological sequences with computer methods. It can also serve as a basis for a university course for undergraduates.' Trends in Cell Biology

' … an enjoyable opportunity to see a blend of modeling and data analysis at work on an important class of problems in the rapidly growing field of computational biology.' D. Siegmund, Short Book Reviews

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.

What Other Items Do Customers Buy After Viewing This Item?

Customer Reviews

3.7 out of 5 stars
Share your thoughts with other customers

Top Customer Reviews

Format: Paperback
This is one of the best and most concise books on current mathematical techniques as applied to sequences avaliable. The book (and each chapter in retrospect) is completely self contained (although might require a little reading around with regard to the probalistic aspects) and thorough. Well worth the money - especially some similar books are priced at almost double.
Comment 12 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Paperback
Well, this is considered a masterpiece if you are into biological machine learning.
The book does indeed cover the subject pretty well, in particular HMMs and profile HMMs.

Subjects are treated well and are often represented graphically too. Despite this, I think it would be better to have more examples (there are indeed very few of them).

It might be kinda hard for those who DON'T have a strong math/statistics background, since it does cover them in a couple of pages and assumes you know much about probability and stuff.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Paperback
While this is perhaps the best book on Hidden Markov Models in Bioinformatics available, you would do well to read Rabiner's review paper. For me this is the type of book that would put potential students off bioinformatics for life. It is too technical and uses inappropriate notation. It has too many "It is easily shown" phrases which means that actually the real proof would be rather involved. Dynamic programming is not explained very well.

If you have a maths or computer background then go for it but if you prefer your Bio in Bioinformatics then stay well clear and go for something like Krane, Mount or Lesk.
Comment One person found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse

Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 4.5 out of 5 stars 24 reviews
4 of 5 people found the following review helpful
5.0 out of 5 stars Brief and clear 22 Nov. 2003
By wiredweird - Published on Amazon.com
Format: Paperback Verified Purchase
I keep coming back to this book for its readable, applicable summaries of basic algorithms.
One chapter covers the basics of dynamic programming for string matching: a staple of bioinformatics computing. The authors come back to it a number of times as they introduce new variations on the string-matching theme. They give about the clearest description of the Needleman-Wunsch and basic variants (including Smith-Waterman) of any book I know.
The bulk of the book is devoted to Hidden Markov Models (HMMs), as one might have guessed in a book with Eddy as co-author. It covers the basics of model construction, motif finding, and various uses for decoding. Again, it covers all the basics so clearly you'll want to start coding as soon as you read it.
The later sections of the book cover phylogeny and tree building, along with the relationships to multiple alignment. Good, solid, clear writing prepares the reader for texts that may be more specialized, but possibly less transparent.
The next-to-last chapter, on RNA folding, is weaker than the ones before, in my opinion. It ties to the other chapters reasonably well in terms of algorithms, but I don't think it does justice to the thermodynamic models of RNA folding. If there is any weakness in this chapter, though, it does not detract from the strengths elsewhere.
The final chapter, the "background on probability", is the one that I think needs the most support. If you don't already understand its topics, I doubt that this will help very much. (If you do understand them, you won 't need the help.) There's nothing inherently tricky about probability, but individual distributions carry many assumptions, and I did not see those spelled out well.
This shouldn't be the only book in your bioinformatics library. If you really want algorithms, though, it's a good book to have in the collection and one you'll keep coming back to.
24 of 26 people found the following review helpful
5.0 out of 5 stars Fantastic Descriptions of Probabilistic Sequence Algorithms 28 April 2002
By Bob Carpenter - Published on Amazon.com
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). There is a good discussion of the CYK dynamic programming algorithm for non-deterministic context-free grammar parsing; an algorithm that is easily applied to finding the best parse in a probabilistic grammar. The presentations of the dynamic programming algorithms for HMM decoding, edit distance minimization, hierarchical clustering and context-free grammar parsing are as good as I've seen anywhere. They are precise, insightful, and informative without being overly subscripted. The illustrations provided are extremely helpful, including their positioning on pages where they're relevant.
This book is aimed at biologists trying to learn about algorithms, which is clear from the terse descriptions of the underlying biological problems. The technical details were so clear, though, that I was able to easily follow the algorithms even if I wasn't always sure about the genetic applications. After studying some introductions to genetics and coming back to this book, I was able to follow the application discussions much more easily. This book assumes the reader is familiar with algorithms and is comfortable manipulating a lot of statistics; a gentler introduction to exactly the same mathematics and algorithms can be found in Jurafsky and Martin's "Speech and Language Processing". For biologists who want to see how sequence statistics and algorithms applied to language, I would suggest Manning and Schuetze's "Foundations of Statistical Natural Language Processing". Although it is much more demanding computationally, more details on all of these algorithms, as well as some more background on the biology, along with some really nifty complexity analysis can be found in Dan Gusfield's "Algorithms on Strings, Trees and Sequences".
In these days of fly-by-night copy-editing and typesetting, I really appreciate Cambridge University Press's elegant style and attention to detail. Durbin, Eddy, Krogh and Mitchison's "Biological Sequence Analysis" is as beautiful and readable as it is useful.
1 of 1 people found the following review helpful
5.0 out of 5 stars Must Have for any Bioinformatics Student 3 Mar. 2009
By whyzit - Published on Amazon.com
Format: Paperback Verified Purchase
This book is a must have for any bioinformatics student working with sequence or genomic data. Useful for anyone attempting to gain an understanding of stochastic models, hidden markov models, and semi-hidden markov models
6 of 15 people found the following review helpful
3.0 out of 5 stars Good bargain, but... 20 Nov. 2002
By A Customer - Published on Amazon.com
Format: Paperback Verified Purchase
not suffciently precise for being an academic textbook. The definitions are sometimes incomplete, correctness proofs are missing, some exercises are incorrect. On the positive side, it does cover important topics, and brings good examples to illustrate main concepts and algorithms (which partially compemsates for the lack of precisenss).
8 of 8 people found the following review helpful
4.0 out of 5 stars Excellent book ... a little boring to read ... 30 Sept. 2005
By Jurgen Van Gael - Published on Amazon.com
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 ...
Were these reviews helpful? Let us know