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Biological sequence analysis : probabilistic models of protein and nucleic acids / Richard Durbin...[et al.].

By: Contributor(s): Material type: TextTextPublication details: Cambridge : CUP, 2012.Description: xi, 356 p. ; illISBN:
  • 9780521540797
Subject(s): DDC classification:
  • 000SB:572.8633 23 D953
Contents:
1. Introduction-- 2. Pairwise alignment-- 3. Markov chains and hidden Markov models-- 4. Pairwise alignment using HMMs-- 5. Profile HMMs for sequence families-- 6. Multiple sequence alignment methods-- 7. Building phylogenetic trees-- 8. Probabilistic approaches to phylogeny-- 9. Transformational grammars-- 10. RNA structure analysis-- 11. Background on probability-- Bibliography-- Indexes.
Summary: This book is a very well written overview to hidden Markov models and context-free grammar methods in computational biology. 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.
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Includes bibliographical references and index.

1. Introduction--
2. Pairwise alignment--
3. Markov chains and hidden Markov models--
4. Pairwise alignment using HMMs--
5. Profile HMMs for sequence families--
6. Multiple sequence alignment methods--
7. Building phylogenetic trees--
8. Probabilistic approaches to phylogeny--
9. Transformational grammars--
10. RNA structure analysis--
11. Background on probability--
Bibliography--
Indexes.

This book is a very well written overview to hidden Markov models and context-free grammar methods in computational biology. 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.

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