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 are used for analyzing sequences, linguistic-grammar-based probabilistic models for identifying 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 , and more generally to probabilistic of . Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal of the other fields, and at the same time presents the state of the art in this new and important field.

Table of Content

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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