Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

Front Cover
Richard Durbin
Cambridge University Press, Apr 23, 1998 - Medical - 356 pages
8 Reviews
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 Markov models 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 sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.
  

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Contents

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Page 334 - C, 1994. Many of the immunoglobulin superfamily domains in cell adhesion molecules and surface receptors belong to a new structural set which is close to that containing variable domains.
Page 335 - Holm, L. and Sander, C. (1993) Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233, 123-138.
Page 334 - Genome sequence comparison and scenarios for gene rearrangements: A test case", Genomics 30 (1995) 299-31 1.

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