356 pages, Figs
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. Biological Sequence Analysis 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.
"This book fills an important gap in the bioinformatics literature and should be required reading for anyone who is interested in doing serious work in biological sequence analysis. For biologists who have little formal training in statistics or probability, it is a long-awaited contribution that, short of consulting a professional statistician who is well versed in molecular biology, is the best source of statistical information that is relevant to sequence-alignment problems. This book seems destined to become a classic. I highly recommend it."
- Andrew F. Neuwald, Trends in Biochemical Sciences
"This book is a nice tutorial and introduction to the field and can certainly be recommended to all who wish to analyse biological sequences with computer methods. It can also serve as a basis for a university course for undergraduates."
- Trends in Cell Biology
"[...] 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
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
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