The the first authoritative and up-to-date review of evolution at the codon level, investigating the mechanisms and particularities of coding regions using the latest statistical analyses and codon-based models of evolution.
The the first authoritative and up-to-date review of evolution at the codon level, investigating the mechanisms and particularities of coding regions using the latest statistical analyses and codon-based models of evolution.
Gina Cannarozzi was a Senior Scientific Researcher in the Computer Science Department at the ETH Zurich, Switzerland from 2000-2010 and currently holds the same position in the Biology Department at the University of Bern. She obtained her Ph.D. in Physical Chemistry from the University of California, San Diego in 1995 and has been interested in molecular evolution, protein structure and the origins of life since working with Prof. Steven A. Benner at the University of Florida in 1996-1998. Her current research focuses are: codon models of evolution, codon bias as well as phylogeny and ancestral reconstruction. She is on the Editorial Board of Trends in Evolutionary Biology. Adrian Schneider received his Ph.D. in 2009 from the ETH Zurich, Switzerland for his thesis on codon-based models of evolution and Mammalian phylogeny. Afterwards, he worked as a post-doctoral researcher at the Institute of Evolutionary Biology at the University of Edinburgh, Scotland and is currently a post-doctoral researcher at the University of Utrecht, Netherlands. His current work includes the development of new semi-empirical codon models.
Inhaltsangabe
* Foreword * Preface * PART I: MODELING CODON EVOLUTION * 1: Adrian Schneider and Gina M. Cannarozzi: Background * 2: Maria Anisimova: Parametric Models of Codon Evolution * 3: Adrian Schneider and Gina M. Cannarozzi: Empirical and Semi-empirical Models of Codon Evolution * 4: Nicolas Rodrigue and Nicolas Lartillot: Monte Carlo Computational Approaches in Bayesian Codon Substitution Modeling * 5: Hong Gu, Katherine A. Dunn, and Joseph P. Bielawski: Likelihood Based Clustering (LiBaC) for Codon Models * 6: Maria Anisimova and David A. Liberles: Detecting and Understanding Natural Selection * 7: Jeffrey L. Thorne, Nicolas Lartillot, Nicolas Rodrigue, and Sang Chul Choi: Codon Models as a Vehicle for Reconciling Population Genetics with Interspecific Sequence Data * 8: Gavin A. Huttley and Von Bing Yap: Robust Estimation of Natural Selection Using Parametric Codon Models * 9: Miguel Arenas and David Posada: Simulation of Coding Sequence Evolution * 10: Steven A. Benner: Use of Codon Models in Molecular Dating and Functional Analysis * 11: Belinda S.W. Chang, Jingjing Du, Cameron J. Weadick, Johannes Müller, Constanze Bickelmann, D. David Yu, and James M. Morrow: The Future of Codon Models in Studies of Molecular Function: Ancestral Reconstruction, and Clade Models of Functional Divergence * 12: Gabriela Aguileta and Tatiana Giraud: Codon Models Applied to the Study of Fungal Genomes * PART II: CODON USAGE BIAS * 13: Alexander Roth, Maria Anisimova, and Gina M. Cannarozzi: Measuring Codon Usage Bias * 14: Nimrod D. Rubinstein and Tal Pupko: Detection and Analysis of Conservation at Synonymous Sites * 15: Fran Supek and Tomislav Smuc: Distance Measures and Machine Learning Approaches for Codon Usage Analyses * 16: Kai Zeng: The Application of Population Genetics in the Study of Codon Usage Bias * 17: Maria do Céu Santos and Manuel A. S. Santos: Structural and Molecular Features of Non-standard Genetic Codes * Index
* Foreword * Preface * PART I: MODELING CODON EVOLUTION * 1: Adrian Schneider and Gina M. Cannarozzi: Background * 2: Maria Anisimova: Parametric Models of Codon Evolution * 3: Adrian Schneider and Gina M. Cannarozzi: Empirical and Semi-empirical Models of Codon Evolution * 4: Nicolas Rodrigue and Nicolas Lartillot: Monte Carlo Computational Approaches in Bayesian Codon Substitution Modeling * 5: Hong Gu, Katherine A. Dunn, and Joseph P. Bielawski: Likelihood Based Clustering (LiBaC) for Codon Models * 6: Maria Anisimova and David A. Liberles: Detecting and Understanding Natural Selection * 7: Jeffrey L. Thorne, Nicolas Lartillot, Nicolas Rodrigue, and Sang Chul Choi: Codon Models as a Vehicle for Reconciling Population Genetics with Interspecific Sequence Data * 8: Gavin A. Huttley and Von Bing Yap: Robust Estimation of Natural Selection Using Parametric Codon Models * 9: Miguel Arenas and David Posada: Simulation of Coding Sequence Evolution * 10: Steven A. Benner: Use of Codon Models in Molecular Dating and Functional Analysis * 11: Belinda S.W. Chang, Jingjing Du, Cameron J. Weadick, Johannes Müller, Constanze Bickelmann, D. David Yu, and James M. Morrow: The Future of Codon Models in Studies of Molecular Function: Ancestral Reconstruction, and Clade Models of Functional Divergence * 12: Gabriela Aguileta and Tatiana Giraud: Codon Models Applied to the Study of Fungal Genomes * PART II: CODON USAGE BIAS * 13: Alexander Roth, Maria Anisimova, and Gina M. Cannarozzi: Measuring Codon Usage Bias * 14: Nimrod D. Rubinstein and Tal Pupko: Detection and Analysis of Conservation at Synonymous Sites * 15: Fran Supek and Tomislav Smuc: Distance Measures and Machine Learning Approaches for Codon Usage Analyses * 16: Kai Zeng: The Application of Population Genetics in the Study of Codon Usage Bias * 17: Maria do Céu Santos and Manuel A. S. Santos: Structural and Molecular Features of Non-standard Genetic Codes * Index
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