Pairwise alignment, hidden Markov models, multiple alignment, profile searches, RNA secondary structure analysis, and phylogenetic inference are treated at length. This book provides the first unified, up-to-date, and tutorial-level overview of sequence analysis methods, with particular emphasis on probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the significance of sequence alignments, the use of hidden Markov models as the basis for profile searches to identify distant members of sequence families, and the inference of phylogenetic trees using maximum likelihood approaches. Many of the most powerful sequence analysis methods are now based on principles of probabilistic modelling. Demands for sophisticated analyses of biological sequences are driving forward the newly-created and explosively expanding research area of computational molecular biology, or bioinformatics. The need to understand the data is becoming ever more pressing. ![]() ![]() Among the most exciting advances are large-scale DNA sequencing efforts such as the Human Genome Project which are producing an immense amount of data. Biological sequence analysis Probabilistic models of proteins and nucleic acids The face of biology has been changed by the emergence of modern molecular genetics.
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