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 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 . Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer 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.

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.
9. and alignment

Title: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
Author: Richard Durbin / Anders Krogh/ Graeme Mitchison/ Sean R. Eddy
Edition: 1st Edition
ISBN: 0521629713 | 9780521629713
Type: eBook | Solution Manual
Language: English
General Biology
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