Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic AcidsCambridge University Press, 23 באפר׳ 1998 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. 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 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. |
תוכן
models | 2-23 |
Pairwise alignment using HMMs | 7-4 |
Profile HMMs forsequence families | 7-5 |
modelling | 7-9 |
HMM training | 10-6 |
Background on probability | 276 |
Bibliography | 301 |
מונחים וביטויים נפוצים
acid alignment algorithms ancestral sequence assignment automaton base pairs Baum–Welch Bayesian bootstrap calculated Chapter Chomsky Chomsky normal form column computational contextfree grammars corresponding cost CpG islands dataset define delete derived Dirichlet distribution dynamic programming dynamic programming matrix edge lengths entropy equations estimation evolutionary example Exercise Felsenstein Figure forward algorithm frequencies genes genome given hidden Markov model Initialisation insert inside–outside inthe Jukes–Cantor leaves logodds match maximise maximum likelihood methods Molecular Biology multiple alignment neighbourjoining nodes nonterminal nucleotide ofthe pairwise alignment parameters parse tree parsimony path phylogenetic phylogeny position possible posterior probability prior probabilistic model production rules profile HMM pseudocounts pseudoknots pushdown random recursion regular grammar residues RNA secondary structure root sampling SCFG score sequence alignment sequence analysis sequence graph shown string substitution matrix symbols threedimensional topology tothe traceback transition probabilities ungapped unrooted tree values variables Viterbi Viterbi algorithm