Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids / Edition 1

Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids / Edition 1

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Cambridge University Press


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Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids / Edition 1

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.

Product Details

ISBN-13: 9780521629713
Publisher: Cambridge University Press
Publication date: 11/28/2015
Edition description: New Edition
Pages: 368
Product dimensions: 6.85(w) x 9.72(h) x 0.79(d)

About the Author

Richard Durbin studied at Cambridge University and received his B.A. (Hons) in Mathematics in 1982. He continued at Harvard University (Biophysics) and later at the MRC Laboratory of Molecular Biology in Cambridge, where he was awarded his Ph.D. in July 1987 on "Studies on the Development and Organisation of the Nervous System of Caenorhabditis elegans". During 1990 to 1996 he worked at the same laboratory on informatics for genome data management and analysis, in particular on the genome database ACEDB together with Jean Thierry-Mieg.
Currently, Richard Durbin is Head of Informatics Division at the Sanger Centre.

Table of Contents

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.

What People are Saying About This

From the Publisher

"The book is amply illustrated with biological applications and examples." Cell

"...successfully integrates numerous probabilistic models with computational algorithms to solve molecular biology problems of sequence excellent textbook selection for a course on bioinformatics and a very useful consultation book for a mathematician, statistician, or biometrician working in sequence alignment." Bulletin of Mathematical Biology

"This is one of the more rewarding books I have read within this field. My overall evaluation is that this book is very good and a must read for active participants in the field. In addition, it could be particularly useful for molecular biologists" Theoretical Population Biology

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Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids 5 out of 5 based on 0 ratings. 1 reviews.
Guest More than 1 year ago
If you want to be in the field of bioinformatics or computational biology you simply MUST read and understand this book. It is a bit dated in some areas (published in 1997) but the topics are sound and well covered. I have read most of the books in this field and this is the best overall book of its kind.