Focusing on problems in contemporary genetics and molecular biology, this text describes basic statistical methods used in genetics. It covers cluster analysis, combinatorial optimization, and dynamic programming, along with the core topics of genome mapping, biological sequence analysis, and the analysis of gene expression arrays. The author also explores Bayesian approaches, such as hidden Markov models and block motif methods, as well as modern tools of Bayesian analysis, including Markov chain Monte Carlo (MCMC). The text features a number of worked examples and problem sets at the end of each chapter.
Very useful for those taking courses in statistics and geneticists. --Pediatric Endocrinology Reviews, Vol. 7, No. 4, June 2010
Basic Molecular Biology. Basics of Likelihood-Based Statistics. Mapping Genomes. Genetic Mapping of Genomes. Extending Basic Linkage Analysis. Quantitative Traits. Model-Free Methods in Linkage. Gene Mapping in Practice. Sequence Alignment. Alignment in Practice. Markov Chains. Multiple Sequence Alignment. Motif Recognition. Measuring Gene Expression. Testing for Differential Expression. Cluster Analysis for Microarrays. Classification.
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Cavan Reilly is associate professor of biostatistics at the University of Minnesota.
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