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 Project. For example, hidden are used for analyzing sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, 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 , and more generally to probabilistic of analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer , 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.

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