Resources
Related Categories
|
|
|
Codon Evolution
Mechanisms and Models
Edited by Gina M. Cannarozzi and Adrian Schneider
296 pages
|
50 illustrations
|
246x189mm
978-0-19-960116-5
|
Hardback
|
23 February 2012
|
|
|
|
|
- The first authoritative and up-to-date review of codon evolution, investigating the mechanisms and particularities of coding regions using the latest statistical analyses and codon-based models of evolution
- Describes the applications of codon models to current problems in biology
- Highlights future research directions, describing the approaches and experiments which might be used to conduct that future work
- Contributions from the leading researchers in the field
Codon-based models of evolution are a relatively new addition to the toolkit of computational biologists, and in recent years remarkable progress has been made in this area. The study of evolution at the codon level captures information contained in both amino acid and synonymous DNA substitutions. By combining these two types of information, codon analyses are more powerful than those of either amino acid or DNA evolution alone. This is a clear benefit for most evolutionary analyses, including phylogenetic reconstruction, detection of selection, ancestral sequence reconstruction, and alignment of coding DNA. Despite the theoretical advantages of codon based models, their relative complexity delayed their widespread use. Only in recent years, when
large-scale sequencing projects produced sufficient genomic data and computational power increased, did their usage become more common.
In Codon Evolution, leading researchers in the field of molecular evolution provide the latest insights from codon-based analyses of genetic sequences. The first part of the book provides comprehensive coverage of the developments of various types of codon substitution models such as parametric and empirical models used in maximum likelihood as well as Bayesian frameworks. Subsequent chapters examine the use of codon models to infer selection and other applications of codon models to biological systems. The second part of the book focuses on codon usage bias. Both the underlying mechanisms as well as current methods to analyse codon
usage bias are presented.Readership: This advanced research level text is suitable for graduate students and researchers in molecular evolution, population genetics, computer science, and evolutionary bioinformatics. Newcomers to the field will benefit from clear background introductions as well as boxes explaining key concepts and important terms.
|
|
|
Edited by Gina M. Cannarozzi, University of Bern, Switzerland, and Adrian Schneider, University of Utrecht, The Netherlands 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. Contributors:
Gabriela Aguileta: Université de Paris Sud, France
Maria Anisimova: Swiss Federal Institute of Technology (ETH Zurich) and Swiss Institute of Bioinformatics (SIB), Switzerland
Miguel
Arenas: University of Bern, Switzerland
Steven A. Benner: Foundation for Applied Molecular Evolution, Gainesville, USA
Constanze Bickelmann: Museum für Naturkunde Berlin, Leibniz-Institute für Evolutions- und Biodiversitätsforschung an der Humboldt-Universität zu Berlin, Germany
Joseph P. Bielawski: Dalhousie University, Canada
Gina Cannarozzi: University of Bern, Switzerland
Maria do Céu Santos: University of Aveiro, Portugal
Belinda S.W. Chang: University of Toronto, Canada
Sang Chul Choi: Cornell University, Ithaca, USA
Jingjing Du: University of Toronto, Canada
Katherine A. Dunn: Dalhousie University, Canada
Tatiana Giraud: Université de Paris Sud, France
Hong Gu:
Dalhousie University, Canada.
Gavin A. Huttley: Australian National University, Australia
Nicolas Lartillot: Université de Montréal, Canada
David A. Liberles: University of Wyoming, USA
James M. Morrow: University of Toronto, Canada
Johannes Müller: Museum für Naturkunde Berlin, Leibniz-Institute für Evolutions- und Biodiversitätsforschung an der Humboldt-Universität zu Berlin, Germany
David Posada: University of Vigo, Spain
Tal Pupko: Tel Aviv University, Israel
Nicolas Rodrigue: Agriculture and Agri-Food Canada, Ottawa, Canada
Alexander Roth: University of Zurich, Switzerland
Nimrod D. Rubinstein: Tel Aviv University, Israel
Adrian Schneider: Utrecht University,
Netherlands
Manuel A. S. Santos: University of Aveiro, Portugal
Tomislav Smuc: Rudjer Boskovic Institute, Zagreb, Croatia
Fran Supek: Rudjer Boskovic Institute, Zagreb, Croatia
Jeffrey L. Thorne: North Carolina State University, Raleigh, USA
Cameron J. Weadick: University of Toronto, Canada
Von Bing Yap: National University of Singapore
D. David Yu: University of Toronto, Canada
Kai Zeng: University of Edinburgh, United Kingdom
|
|
|
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
|
|
|
|
|
|
|
|
|
The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
|
|
