Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

by Richard Durbin, Sean R. Eddy, Anders Krogh

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.

Genres: Biology, Science, Textbooks, Computer Science, Reference, Evolution, Mathematics

356 pages, Paperback

First published April 23, 1998

Reviews

[Michiel · Rating 4 out of 5]

Perhaps a quite old book, but very relevant for any bioinformatician! Have recently read Gusfields 'algorithms on strings', I think this one is the more relevant text on algorithms for biological sequences. The reason for this is that for working with real-life data a sound probabilistic framework is required! Anyone familiar with the standard machine learning tools will likely see how to link these methods and how to apply them for building classifiers for example.

The introduction on dynamics programming and HMM is good, though very descriptive. Might be necessary to consult additional material if you want to implement these tools yourself.

The part about inferring phylogenies was interesting and shows the difficulty of the field.

The last part about stochastic grammers and RNA was, to me extremely fascinating. The authors present some world which really goes at the heart of bioinformatics (or language modelling in general) and what we can and cannot do. A pity this section is only two chapters.

[Bradley Woolf · Rating 5 out of 5]

Goes shockingly deep given its smaller size. Last 3 chapters really end it well. References and recommended readings are really insightful for further learning.