Biological Sequence Analysis – Richard Durbin – 1st Edition


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 . For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary , and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms.

This 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 . Written by an interdisciplinary team of authors, it is accessible to biologists, scientists, and mathematicians with no formal knowledge of the other , and at the same time presents the state of the art in this new and important field.

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Table of Contents

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
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