http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (xv, 280 pages) : illustrations
An authoritative and up-to-date review of evolution at the codon level, this book investigates the mechanisms and particularities of coding regions using the latest statistical analyses and codon-based models of evolution.
Cover; Contents; Foreword; Preface; List of Contributors; Part I: Modelling codon evolution; 1: Background; 1.1 Models of molecular evolution; 1.2 Markov models; 1.3 Maximum-likelihood estimation; 1.4 Performance assessment; 2: Parametric models of codon evolution; 2.1 Basic Markov models of codon substitution; 2.2 Evaluating selective pressure at the protein level; 2.3 Measuring selection on physico-chemical properties of amino acids; 2.4 Modelling site-dependence in coding sequences; 2.5 Further development of parametric models; 3: Empirical and semi-empirical models of codon evolution
3.1 Introduction3.2 Empirical model by Schneider et al. (2005); 3.3 Combined model by Doron-Faigenboim and Pupko (2007); 3.4 Model by Kosiol et al. (2007); 3.5 Codon test; 3.6 Empirical search for the most important parameters; 3.7 Summary; 4: Monte Carlo computational approaches in Bayesian codon-substitution modelling; 4.1 Introduction; 4.2 The Bayesian framework; 4.3 Site-independent models of codon substitution; 4.4 Site-interdependent models of codon substitution; 4.5 Other recent modelling innovations and overall rankings; 4.6 Future directions
5: Likelihood-based clustering (LiBaC) for codon models5.1 Introduction; 5.2 Theory for likelihood-based clustering (LiBaC); 5.3 Detecting positive selection in a large-scale analysis of real gene sequences; 5.4 Objective comparison of model-based classifications; 5.5 Simulation studies of model-based classification; 5.6 Recommendations for using LiBaC; 6: Detecting and understanding natural selection; 6.1 Selective mechanisms operating on gene sequences; 6.2 Brief overview of statistical methodologies for detecting positive selection
6.3 The utility and the interpretation of the d[sub(N)]/d[sub(S)] measure6.4 Accounting for indels and overlapping ORFs; 6.5 Model-based approaches and common misconceptions; 6.6 Selection and adaptive traits; 6.7 Lessons from genomic studies and implications for studies of genetic disease; 7: Codon models as a vehicle for reconciling population genetics with inter-specific sequence data; 7.1 Introduction; 7.2 The importance of phenotype; 7.3 The Halpern-Bruno approach; 7.4 Limitations of the Halpern-Bruno approach; 7.5 Future directions
8: Robust estimation of natural selection using parametric codon models8.1 Introduction; 8.2 Context-dependent substitution models; 8.3 Evaluating properties of dinucleotide models; 8.4 Evaluating properties of codon models; 8.5 Impact of model definitions on statistical power; 8.6 Conclusion; 9: Simulation of coding sequence evolution; 9.1 Introduction; 9.2 Simulation of coding sequences; 9.3 Uses of simulated coding data; 9.4 Software implementations; 10: Use of codon models in molecular dating and functional analysis; 10.1 Introduction
edited by Gina M. Cannarozzi, Adrian Schneider.
Includes bibliographical references and index.
Oxford ; New York : Oxford University Press, 2012