Details

Autor(en) / Beteiligte

• Didier, Gilles
• Guindon, Stephane

Titel

Models and Methods for Biological Evolution : Mathematical Models and Algorithms to Study Evolution

Auflage

1st ed

Ort / Verlag

Newark : John Wiley & Sons, Incorporated,

Erscheinungsjahr

2024

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. Trees: Combinatorics and Models -- 1.1. Introduction -- 1.2. Preliminary definitions -- 1.3. Counting trees -- 1.3.1. Fully labeled non-rooted trees -- 1.3.2. Binary trees with labeled leaves -- 1.3.3. Binary trees with labeled leaves and ordered internal nodes -- 1.3.4. Number of orders of internal nodes of a given tree -- 1.3.5. Directed binary trees -- 1.4. Probabilities of trees resulting from branching processes -- 1.5. Birth-death processes -- 1.5.1. Probability density of a birth-death tree -- 1.6. The coalescent -- 1.6.1. Links with "classical" models in population genetics -- 1.6.2. Moran's model -- 1.6.3. The Wright-Fisher model -- 1.6.4. Generic model -- 1.6.5. Coalescent-generated tree probability density -- 1.7. Conclusion -- 1.8. References -- Chapter 2. Models of Sequences and Discrete Traits Evolution -- 2.1. Introduction -- 2.2. Discrete set-valued continuous-time Markov process -- 2.2.1. Poisson processes -- 2.2.2. Finite set-valued continuous-time Markov process -- 2.3. Models of DNA sequence evolution -- 2.3.1. The Jukes-Cantor model -- 2.3.2. The Kimura model -- 2.3.3. The Felsenstein model -- 2.3.4. The HKY model -- 2.3.5. The general time reversible model -- 2.4. Models of rate evolution along the sequence -- 2.4.1. Independent and identically distributed rates along the sequence -- 2.4.2. Hidden Markov model -- 2.5. Models of discrete trait evolution -- 2.6. References -- Chapter 3. Evolutionary Models of Continuous Traits -- 3.1. Motivations -- 3.1.1. Comparative methods -- 3.1.2. Studies of evolutionary phenomena -- 3.2. Brownian motion -- 3.2.1. Description -- 3.2.2. Phylogenetic regression and statistical transformations -- 3.2.3. Recursive algorithms for inference -- 3.3. Multivariate analysis -- 3.3.1. Description.
• 3.3.2. Phylogenetic contrasts -- 3.3.3. Phylogenetic PCA -- 3.4. Gaussian models -- 3.4.1. Some limits of the Brownian motion -- 3.4.2. Ornstein-Uhlenbeck process -- 3.4.3. Biological interpretations and caveats -- 3.4.4. Further Gaussian processes -- 3.4.5. Heterogeneous evolution -- 3.4.6. Observation models -- 3.4.7. Model selection -- 3.5. Extensions and generalizations -- 3.5.1. Non-Gaussian models -- 3.5.2. Tree-trait interactions -- 3.5.3. Interactions between species -- 3.5.4. Trait of high dimension -- 3.6. Useful references -- 3.7. Acknowledgements -- 3.8. References -- Chapter 4. Correlated Evolution: Models and Methods -- 4.1. Introduction -- 4.2. Correlated evolution between traits -- 4.2.1. Species are not independent -- 4.2.2. The phylogenetically independent contrasts -- 4.2.3. Extending the linear model to account for phylogeny -- 4.2.4. Correlation between discrete traits -- 4.2.5. Examples of correlated traits -- 4.2.6. Jointly modeling traits and sequences -- 4.3. Correlated evolution within genomes -- 4.3.1. Within genes, between nucleotides -- 4.3.2. Within proteins, between amino acids -- 4.3.3. Within genomes, between genes -- 4.4. Genetics is also correlated evolution -- 4.4.1. In individuals -- 4.4.2. In pedigrees -- 4.4.3. In the population -- 4.5. Conclusion -- 4.6. References -- Chapter 5. A Century of Genomic Rearrangements -- 5.1. Introduction -- 5.2. Orderings of genes and the rearrangements that act on them -- 5.2.1. Basic representations and definitions -- 5.2.2. DCJ operations and the breakpoint graph -- 5.3. Counting DCJ scenarios -- 5.3.1. Scenarios for a balanced cycle of length 2m -- 5.3.2. The (many) cycle decompositions of a breakpoint graph -- 5.4. Chromosomal contact data and weighted scenarios -- 5.4.1. A model incorporating chromosomal contacts -- 5.4.2. Planar trees and an algorithm for exploring them.
• 5.4.3. Planar trees -- 5.5. Conclusion -- 5.6. References -- Chapter 6. Phylogenetic Inference: Distance-Based Methods -- 6.1. Introduction -- 6.2. Mathematical basis -- 6.3. Distance estimation -- 6.3.1. Estimating distances from aligned sequences -- 6.3.2. Other approaches to estimate distances -- 6.4. Tree inference -- 6.4.1. Fitting branch lengths with least squares -- 6.4.2. Scoring trees: from least squares to minimum evolution -- 6.4.3. NJ and other agglomerative algorithms -- 6.4.4. Beyond distances -- 6.5. Conclusion -- 6.6. References -- Chapter 7. Computing Inference in Phylogenetic Trees -- 7.1. Inferences and modeling -- 7.1.1. Inferences -- 7.1.2. Parsimony and likelihood -- 7.1.3. Maximum parsimony -- 7.2. Dynamic programming -- 7.2.1. Over the branches -- 7.2.2. Over the nodes -- 7.2.3. Over the tree -- 7.2.4. At the root -- 7.2.5. Recursion relations -- 7.2.6. Complexity reduction -- 7.2.7. Root management -- 7.3. Maximum parsimony -- 7.3.1. Ancestral interference -- 7.4. Likelihood -- 7.4.1. Root management -- 7.4.2. Computation at the nodes -- 7.4.3. Maximization, differentiation -- 7.4.4. Ancestral interference -- 7.5. References -- Chapter 8. The Bayesian Paradigm in Molecular Phylogeny -- 8.1. Introduction -- 8.2. General principles of the Bayesian approach in phylogeny -- 8.2.1. Markov chain Monte Carlo sampling -- 8.2.2. Summary of posterior distribution and sampling -- 8.3. Demarginalization of the likelihood function -- 8.3.1. Parameter expansion -- 8.3.2. Data augmentation -- 8.4. Bayesian selection of substitution models -- 8.4.1. Relative model comparison via the Bayes factor -- 8.4.2. Absolute evaluation of models via predictive posterior simulation -- 8.5. Impacts and future directions -- 8.6. References -- Chapter 9. Measures of Branch Support in Phylogenetics -- 9.1. Introduction.
• 9.2. Local supports: parametric and non-parametric aLRT -- 9.2.1. Null branch test and its limitations -- 9.2.2. Local aLRT test, parametric version -- 9.2.3. Local aLRT test, SH-like nonparametric version -- 9.2.4. Comparison with an example of aLRT support and bootstrap -- 9.3. Phylogenetic bootstrap -- 9.3.1. Statistic bootstrap -- 9.3.2. The Felsenstein bootstrap -- 9.3.3. Transfer bootstrap -- 9.3.4. Comparison with an example of bootstrap supports -- 9.4. Bayesian supports -- 9.4.1. Principle, use of Markov Monte Carlo chains -- 9.4.2. Local Bayesian support -- 9.4.3. Comparison of Bayesian supports with an example -- 9.5. Discussion -- 9.6. References -- Chapter 10. Fossils and Phylogeny -- 10.1. Inferences on topology -- 10.1.1. First approaches -- 10.1.2. Traits usable in paleontology -- 10.1.3. First quantitative approach: phenetics -- 10.1.4. Stratophenetics -- 10.1.5. Cladistics -- 10.1.6. Model-based approaches: likelihood, Bayesian approaches -- 10.1.7. Fossils and molecular data -- 10.2. Dating the tree of life -- 10.2.1. First qualitative approaches -- 10.2.2. First statistical approaches -- 10.2.3. Molecular dating -- 10.2.4. Tip dating -- 10.2.5. Birth-death model-based dating -- 10.3. Conclusion -- 10.4. References -- Chapter 11. Phylodynamics -- 11.1. Reconciling ecology, evolution and mathematics -- 11.2. Data and processors -- 11.2.1. New generation sequencing -- 11.2.2. PCR and capture -- 11.3. Infection phylogenies -- 11.3.1. Link to transmission chains -- 11.3.2. Dating and evolutionary rates -- 11.3.3. Biological applications of time calibration -- 11.4. Phylodynamics -- 11.4.1. A field in search of definition -- 11.4.2. As closely as possible to epidemiology -- 11.4.3. Coalescent -- 11.4.4. Birth-death models -- 11.4.5. Limitations of likelihood approaches -- 11.4.6. ABC phylodynamics -- 11.5. Infection phylogeography.
• 11.6. Infection and viral life history traits -- 11.7. Perspectives and challenges -- 11.8. References -- Chapter 12. Inference of Demographic Processes in Human Populations -- 12.1. Introduction -- 12.2. Demographic inferences from population genetics data -- 12.2.1. Reconstruction of the history of Central African Pygmies -- 12.2.2. Inference of the history of populations in Central Asia -- 12.2.3. Impact of lifestyle on population growth dynamics -- 12.3. Inferring human expansions from next-generation sequence data -- 12.4. Reconstructing population dynamics from genetic and cultural data -- 12.4.1. Simultaneous analysis of genetic and linguistic diversity -- 12.4.2. Detecting the intergenerational transmission of reproductive success -- 12.5. Conclusion -- 12.6. References -- List of Authors -- Index -- EULA.
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