Introduction -- Pairwise alignment -- Markov chains and hidden Markov models -- Pairwise alignment using HMMs -- Profile HMMs for sequence families -- Multiple sequence alignment methods -- Building phylogenetic trees -- Probabilistic approaches to phylogeny -- Transformational grammars -- RNA structure analysis -- Background on probability
Summary
Presents up-to-date computer methods for analysing DNA, RNA and protein sequences
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
Bibliography
Includes bibliographical references (pages 326-344) and indexes
