Biological Sequence Analysis
Probabilistic Models of Proteins and Nucleic Acids

Presents up-to-date computer methods for analysing DNA, RNA and protein sequences.

Richard Durbin (Author), Sean R. Eddy (Author), Anders Krogh (Author), Graeme Mitchison (Author)

9780521629713, Cambridge University Press

Paperback, published 23 April 1998

370 pages, 100 b/w illus. 50 tables
24.4 x 17 x 2 cm, 0.643 kg

' … an enjoyable opportunity to see a blend of modeling and data analysis at work on an important class of problems in the rapidly growing field of computational biology.' D. Siegmund, Short Book Reviews

Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing 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 sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly 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. Phylogenetic trees
9. Phylogeny and alignment
Index.

Subject Areas: Genetic engineering [TCBG], Biotechnology [TCB], Molecular biology [PSD]