Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
Cambridge University Press, Apr 23, 1998 - Medical - 356 pages
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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affine gap alignment algorithms amino acid assigned assume automaton base pairs Baum-Welch Bayesian calculated Chapter Chomsky Chomsky normal form codon column computational consensus context-free grammars corresponding CpG islands CYK algorithm database dataset define delete derived Dirichlet dynamic programming dynamic programming matrix edge lengths emission probabilities emitting equations estimate evolutionary example forward algorithm frequencies genes give given globin hidden Markov model Initialisation insert log-odds loop Markov chain match maximise maximum likelihood methods multiple alignment node nonterminal nucleotide optimal alignment pair HMM pairwise alignment parameters parse tree parsimony path phylogenetic position possible posterior probability prior probabilistic model problem production rules profile HMM pseudocounts quences Recursion regular grammar relative entropy residues RNA secondary structure root sampling SCFG score sequence alignment sequence analysis shown in Figure stochastic string substitution matrix symbols traceback transition probabilities ungapped unrooted trees values variables Viterbi algorithm weights
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.