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Statistical Theory and Methods for Evolutionary Genomics [Kõva köide]

(Department of Genetics, Development and Cell Biology, Iowa State University, USA)
  • Formaat: Hardback, 272 pages, kõrgus x laius x paksus: 248x177x20 mm, kaal: 706 g, 60 black and white illustrations
  • Ilmumisaeg: 04-Nov-2010
  • Kirjastus: Oxford University Press
  • ISBN-10: 0199213267
  • ISBN-13: 9780199213269
Teised raamatud teemal:
Statistical Theory and Methods for Evolutionary GenomicsSuurem pilt
  • Formaat: Hardback, 272 pages, kõrgus x laius x paksus: 248x177x20 mm, kaal: 706 g, 60 black and white illustrations
  • Ilmumisaeg: 04-Nov-2010
  • Kirjastus: Oxford University Press
  • ISBN-10: 0199213267
  • ISBN-13: 9780199213269
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780199213269.html
Märksõnad:
Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis.

Statistical Theory and Methods for Evolutionary Genomics summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the author includes extensive discussion on how new types of functional genomic data (e.g. microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.
1 Basics in Molecular Evolution
1(32)
1.1 Evolutionary distance of DNA Sequences
1(11)
1.1.1 Jukes and Cantor's Model: a Tutorial
1(2)
1.1.2 Models of nucleotide substitution
3(3)
1.1.3 One-parameter method
6(1)
1.1.4 Kimura's Two-parameter method
6(1)
1.1.5 The general stationary and time-reversible model
7(1)
1.1.6 Estimation of d under variable rates
8(1)
1.1.7 The LogDet distance
9(3)
1.2 Evolutionary distance between protein-encoding sequences
12(3)
1.2.1 Poisson distance of protein sequence
12(1)
1.2.2 Amino acid substitution matrix
12(1)
1.2.3 Synonymous and nonsynonymous distances
13(2)
1.3 Phylogenetics trees: an overview
15(1)
1.4 Distance method for Phylogenetic inference
16(3)
1.4.1 Principle: minimum-evolution (ME)
16(1)
1.4.2 Algorithm: neighbor-joining (NJ) method
16(2)
1.4.3 Four-point condition and NJ algorithm
18(1)
1.4.4 The Q-score of Studier and Keppler
19(1)
1.5 Parsimony methods for phylogenetic inference
19(2)
1.6 Maximum-likelihood (ML) methods for phylogenetic inference
21(2)
1.6.1 Likelihood function
21(1)
1.6.2 Time-reversibility and the root problem
22(1)
1.6.3 Search Strategies for ML trees
22(1)
1.7 Bayesian methods for phylogenetic inference
23(1)
1.8 Ancestral sequence inference
24(4)
1.8.1 The maximum parsimony approach
24(1)
1.8.2 The probabilistic (Bayesian) approach
25(2)
1.8.3 Deletions and insertions
27(1)
1.9 Rate variation among sites
28(5)
1.9.1 Number of substitutions at a site
29(1)
1.9.2 Estimation of α
30(3)
2 Basics in Bioinformatics and Statistics
33(14)
2.1 Bioinformatic resources for evolutionary genomics
33(3)
2.2 Basic statistics for homologous search
36(1)
2.3 Sequence alignment
37(3)
2.3.1 Pairwise alignment
38(1)
2.3.2 Multiple alignment with a guide tree: Clustal
39(1)
2.4 Microarrays and statistics
40(4)
2.4.1 Types of microarray data
40(1)
2.4.2 Sources of noises
41(1)
2.4.3 Multiple gene problem
41(1)
2.4.4 False discovery rate (FDR)
42(2)
2.4.5 ANOVA analysis of many genes
44(1)
2.5 Markov chain Monte Carlo (MCMC)
44(3)
2.5.1 Metropolis Hastings algorithm
45(1)
2.5.2 Calculation of posterior distribution
45(2)
3 Functional Divergence after gene Duplication: Statistical Modeling
47(26)
3.1 Modeling functional divergence
47(2)
3.2 Poisson model for type-I functional divergence
49(9)
3.2.1 Two-state model
49(1)
3.2.2 The Poisson-gamma model for protein sequence evolution
50(1)
3.2.3 The likelihood function
51(1)
3.2.4 Maximum-likelihood estimation (MLE)
52(2)
3.2.5 Predicting critical amino acid residues
54(3)
3.2.6 Reduced rate correlation between duplicate genes: An alternative view of θ1
57(1)
3.3 Markov chain model for type-I functional divergence
58(5)
3.3.1 The markov chain model
58(3)
3.3.2 Case study: COX (cyclooxygenase) gene family
61(2)
3.3.3 comparisons between the poisson model and the Markov chain model
63(1)
3.4 Statistical method for type-II functional divergence
63(6)
3.4.1 Modeling type-II functional divergence
63(2)
3.4.2 Two clusters by gene duplication
65(1)
3.4.3 Poisson model in the late-stage
65(1)
3.4.4 Maximum-likelihood estimation
66(1)
3.4.5 Predicting critical amino acid residues: empirical Bayesian approach
67(2)
3.5 A unifying model for type-I and -II functional divergences
69(4)
4 Functional Divergence after Gene Duplication: Applications and others
73(22)
4.1 Diverge-based analysis
73(11)
4.1.1 Functional-structural basis of shifted evolutionary Rates between caspases
73(4)
4.1.2 Pseudokinase domain in Jak protein kinase is functional
77(5)
4.1.3 Pattern of type-II functional divergence
82(2)
4.2 Functional distance analysis
84(6)
4.2.1 Distance of functional divergence
84(2)
4.2.2 Three-cluster analysis
86(1)
4.2.3 Examples: vertebrate developmental gene families
87(3)
4.3 other methods for type-I functional divergence
90(5)
4.3.1 Knudsen-Miyamoto method
90(1)
4.3.2 Gaucher-Miyamoto-Benner method
91(1)
4.3.3 Codon-based methods
91(1)
4.3.4 Heterotachy model
92(1)
4.3.5 The alpha shift measure (ASM) method
93(1)
4.3.6 Nam et al.'s method for detecting functional divergence Of protein domains
93(2)
5 Phylogenomic Expression Analysis between Duplicate Genes
95(18)
5.1 Brownian-related stochastic model
95(6)
5.1.1 Expression likelihood under phylogeny
95(5)
5.1.2 Method of expression distance
100(1)
5.2 Aucestral gene expression inference
101(2)
5.3 Oakley et al.'s model
103(2)
5.4 Expression divergence under stabilizing selection: The Ornstein-Uhlenback (OU) model
105(1)
5.5 Likelihood and distance methods under experimental correlations
106(1)
5.6 Yeast Glns gene family: an example
107(2)
5.7 Estimating expression divergence based on Massively parallel sequencing technology
109(4)
5.7.1 The Poisson-lognormal model
109(1)
5.7.2 U-distance for expression divergence
110(3)
6 Expression between Duplicate Genes: Genome-Wide Analysis
113(14)
6.1 Coding sequence divergence vs expression divergence
113(1)
6.2 Regulatory motif divergence vs expression divergence Between duplicates
114(1)
6.3 Gene duplication and expression diversification
115(1)
6.4 Expression divergence and retention of duplicate genes
116(2)
6.5 Evolutionary distance of expression divergence
118(2)
6.6 Rate of expression divergence between yeast duplicate genes
120(3)
6.7 Asymmetric expression evolution after gene duplications
123(2)
6.8 Concluding remarks
125(2)
7 Tissue-Driven Hypothesis of Genomic Evolution
127(16)
7.1 Tissue-driven hypothesis of genomic evolution
127(5)
7.1.1 Expression divergence under stabilizing model
127(2)
7.1.2 Tissue-dependent rate of protein evolution
129(1)
7.1.3 Tissue-driven hypothesis
130(2)
7.2 Testing the tissue-driven hypothesis
132(5)
7.2.1 Estimation of genomic distances
132(1)
7.2.2 Tissue expression divergence between human and mouse
133(1)
7.2.3 Correlation (Eti - Du) between tissue expression And sequence divergence
134(1)
7.2.4 Tissue correlation (Eti - Dti) between inter-species and duplicate expression divergence
135(1)
7.2.5 Evolutionary rate of protein sequence under Multiple tissue constraints
135(1)
7.2.6 Some comments
136(1)
7.3 Compound-Poisson model of expression evolution
137(1)
7.4 Expression shifts in the human brain
138(5)
7.4.1 Evolution of the human brain
138(1)
7.4.2 enard et al.'s analysis
138(1)
7.4.3 Gu and Gu's analysis
138(3)
7.4.4 Concluding comments
141(2)
8 Gene Pleiotropy and Evolution of Protein Sequence
143(24)
8.1 Model for protein sequence evolution
143(5)
8.1.1 Fisher's model and molecular phenotypes
143(3)
8.1.2 Stabilizing selection
146(1)
8.1.3 Micro-adaptation
146(1)
8.1.4 Distribution of mutational effects
147(1)
8.1.5 S-distribution
148(1)
8.2 Selection intensity and model classification
148(3)
8.2.1 Mean of selection intensity
148(2)
8.2.2 Model classification
150(1)
8.3 Evolutionary rate of protein sequences
151(4)
8.3.1 General formula
151(2)
8.3.2 K-mode and B-mode for the rate of protein evolution
153(1)
8.3.3 Effect of Bi-variation on the evolutionary rate
154(1)
8.4 Estimation of gene pleiotropy and selection intensity
155(4)
8.4.1 The second-moment of evolutionary rate
155(1)
8.4.2 Effective gene pleiotropy (Ke)
155(2)
8.4.3 Estimation pipeline
157(1)
8.4.4 Effective Selection intensity
157(1)
8.4.5 Bias-corrected estimation of effectively gene pleiotropy
158(1)
8.5 Preliminary analysis of gene pleiotropy
159(6)
8.5.1 Extent of gene pleiotropy
160(1)
8.5.2 Biological relevance
160(5)
8.6 Comments on gene pleiotropy
165(2)
9 Modeling the Genomic Evolution of Gene Contents
167(20)
9.1 The birth-death model of gene content evolution
167(7)
9.1.1 Joint size distribution of gene families
167(2)
9.1.2 Genome distances and gene content information
169(1)
9.1.3 Extended gene content and genome distance estimation
170(2)
9.1.4 Simulations and case study
172(2)
9.2 Likelihood of four genomes under simple gene contents
174(8)
9.2.1 Likelihood function: case A
175(4)
9.2.2 Likelihood function: Case B
179(1)
9.2.3 Likelihood function
180(2)
9.3 Birth-death model with lateral gene transfer
182(3)
9.3.1 General birth-death process considering LGT
182(1)
9.3.2 Extended gene content under LGT
183(1)
9.3.3 Simple gene content and LGT
184(1)
9.3.4 Comments
185(1)
9.4 Other Models
185(2)
9.4.1 Blocks model
185(1)
9.4.2 Equal birth-death rate model
186(1)
9.4.3 Constant-birth, proportional-death model
186(1)
10 Advanced Topics in Systems Biology and Network Evolution
187(36)
10.1 GC mutational bias rather than adaptation driving tyrosine loss in metazoan genome evolution
188(2)
10.2 Contribution of duplicate genes to genetic robustness
190(6)
10.2.1 Functional compensation between duplicate genes
191(1)
10.2.2 When an essential gene is duplicated
191(3)
10.2.3 When a dispensable gene is duplicated
194(1)
10.2.4 Hypothesis: duplication of dispensable genes to maintain genetic buffering
194(2)
10.3 Evolution of gene-gene interactions
196(4)
10.3.1 p-value representation of gene-gene interaction
196(1)
10.3.2 General framework
197(1)
10.3.3 Estimation of 711, 710, 701, and 700
198(1)
10.3.4 Comments
199(1)
10.4 Origin of modularity and complexity
200(11)
10.4.1 Some backgrounds
200(1)
10.4.2 Scale-free network and modularity
201(2)
10.4.3 Origin of modularity in a scale-free network
203(4)
10.4.4 Protein-protein interaction data analysis
207(1)
10.4.5 Hypothesis: random loss of interactions may shape Modularity in a complex gene network
208(3)
10.5 Network motif analysis and yeast genome duplication
211(5)
10.6 Evolutionary kinetic (EK) analysis of duplicate genes
216(7)
10.6.1 Reprogramming in duplicate backup circuits
216(1)
10.6.2 Responsive backup circuits (RBC) and regulatory designs
217(2)
10.6.3 Expression-triggered backup circuit hypothesis
219(4)
References 223(28)
Index 251
Xun Gu obtained his Ph.D from the University of Texas in 1996 and is now Professor in the Department of Genetics, Development and Cell Biology at Iowa State University. His research has been focused on statistical and computational methods for understanding genome complexity and evolution, and high throughput comparative genomics analyses and applications. In his research career, Dr. Gu has published over 100 papers in peer-reviewed scientific journals, and he was the 2001 recipient of the Dupont Young Professor Award. In addition, Dr. Gu has served as the associate editor, guest editor, and member of editorial board in a number of scientific journals.