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Biological Sequence Analysis
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  • 100 b/w illus. 50 tables
  • Page extent: 370 pages
  • Size: 247 x 174 mm
  • Weight: 0.59 kg

Library of Congress

  • Dewey number: 572.8/633
  • Dewey version: 21
  • LC Classification: QP620 .B576 1998
  • LC Subject headings:
    • Nucleotide sequence--Statistical methods
    • Amino acid sequence--Statistical methods
    • Numerical analysis
    • Probabilities

Library of Congress Record

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 (ISBN-13: 9780521629713 | ISBN-10: 0521629713)

DOI: 10.2277/0521629713

  • There was also a Hardback of this title but it is no longer available | Adobe eBook
  • Published April 1998

Manufactured on demand: supplied direct from the printer

 (Stock level updated: 17:00 GMT, 30 November 2015)


Probabilistic models are becoming increasingly important in analysing 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 analysing 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 aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.

• A much-needed textbook in a new and rapidly expanding area of science • Interdisciplinary - aimed at both biologists and computer scientists • Up-to-the-minute - presents the most recent sequence analysis methods and their underlying concepts in a coherent framework


1. Introduction; 2. Pairwise sequence alignment; 3. Multiple alignments; 4. Hidden Markov models; 5. Hidden Markov models applied to biological sequences; 6. The Chomsky hierarchy of formal grammars; 7. RNA and stochastic context-free grammars; 8. Phylogenetic trees; 9. Phylogeny and alignment; Index.


'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

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