Biological Sequence Analysis: probabalistic models of proteins and nucleic acids
Richard Durbin
Biological Sequence Analysis: probabalistic models of proteins and nucleic acids - Cambridge University Press 1998 - xi, 356 pages : illustrations ; 26 cm
Introduction -- Pairwise sequence alignment -- Multiple alignments -- Hidden Markov models -- Hidden Markov models applied to biological sequences -- The Chomsky hierarchy of formal grammars -- RNA and stochastic context-free grammars -- Phylogenetic trees -- Phylogeny and alignmen.
Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by largescale DNAsequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguisticgrammarbased probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, uptodate and selfcontained 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 stateoftheart in this new and highly important field.
9780521540797
Nucleotide sequence -- Statistical methods. Amino acid sequence -- Statistical methods. Numerical analysis.
572.8633 DUR- B
Biological Sequence Analysis: probabalistic models of proteins and nucleic acids - Cambridge University Press 1998 - xi, 356 pages : illustrations ; 26 cm
Introduction -- Pairwise sequence alignment -- Multiple alignments -- Hidden Markov models -- Hidden Markov models applied to biological sequences -- The Chomsky hierarchy of formal grammars -- RNA and stochastic context-free grammars -- Phylogenetic trees -- Phylogeny and alignmen.
Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by largescale DNAsequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguisticgrammarbased probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, uptodate and selfcontained 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 stateoftheart in this new and highly important field.
9780521540797
Nucleotide sequence -- Statistical methods. Amino acid sequence -- Statistical methods. Numerical analysis.
572.8633 DUR- B