Includes bibliographical references (pages 326-344) and indexes.
CONTENTS NOTE
Text of Note
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.
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SUMMARY OR ABSTRACT
Text of Note
Presents up-to-date computer methods for analysing DNA, RNA and protein sequences.
Text of Note
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.
OTHER EDITION IN ANOTHER MEDIUM
Title
Biological sequence analysis.
International Standard Book Number
9780521629713
TOPICAL NAME USED AS SUBJECT
Amino acid sequence-- Data processing.
Amino acid sequence-- Statistical methods.
Markov processes.
Nucleotide sequence-- Data processing.
Nucleotide sequence-- Statistical methods.
Numerical analysis.
Probabilities.
Amino Acid Sequence.
Base Sequence.
Probability.
Sequence Analysis-- methods.
Analyse numérique.
Probabilités.
Séquence des acides aminés-- Méthodes statistiques.