Environmental Microbial Evolution: Methods and Protocols

Preface
Contents
Contributors
Part I: Evolutionary Timescales
Chapter 1: Dating Microbial Evolution with MCMCtree
1 Introduction
2 Software and Data
3 Bayesian Molecular-Clock Dating with Approximate Likelihood
4 Dating a Microbial Phylogeny Using Fossil Calibrations
4.1 Estimation of the Branch Length MLEs, Gradient, and Hessian
4.2 MCMC Sampling from the Posterior Distribution
4.3 MCMC Diagnostics
4.4 Sampling from the Prior
5 Dating a Phylogeny with Serially Sampled Taxa
6 Dating Microbial Phylogenies Without Fossil Calibrations nor Sampling Times
References
Chapter 2: A Computational Protocol for Dating the Evolution of Cyanobacteria
1 Introduction
2 Methods
2.1 Taxon Sampling
2.2 Orthologue Identification and Phylogenomic Tree Reconstruction
2.3 Calibrate the Phylogenomic Tree of Cyanobacteria
2.4 Prepare the Input Molecular Data
2.5 Divergence Time Estimation Using MCMCTree
2.5.1 Estimate the Rough Substitution Rate of Input Gene Sequences
2.5.2 Estimate the Branch Lengths Using the Approximate Method in MCMCTree
2.5.3 Clock Model Selection
2.5.4 Bayesian Estimation of Divergence Times
2.6 Assessment of the Molecular Dating Analysis
3 Conclusion
4 Notes
Data Availability
References
Chapter 3: Standard Candles for Dating Microbial Lineages
1 Introduction: The Challenges of Dating Microbial Lineages
1.1 What Is a ``Standard Candle´´?
1.2 Dating Trees of Substrate-Utilizing Lineages
1.3 Multiplicity of Substrate-Producing Lineages
1.4 Substrate-Utilizing Lineages
1.5 Origin vs. Ecological Availability
1.6 Direct vs. Indirect Molecular Clock Implementations
1.7 Estimating Stem Histories
1.8 Clade Fidelity
2 Metazoan-Based Standard Candles
2.1 Animal-Microbial Interactions
2.2 Collagen
2.3 Chitin
2.4 Glycosaminoglycans
2.5 Keratin
3 Algae and Plant-Based Standard Candles
3.1 Red Algal Sulfated Galactans
3.2 Green Algal Ulvans
3.3 Brown Algal Alginates and Fucose-Containing Sulfated Polysaccharides (FCSPs)
3.4 Plant Cell Wall Polymers and Terrestrialization-Related Genes
4 Outlook
References
Chapter 4: Relative Time Inference Using Lateral Gene Transfers
1 Introduction
1.1 Entangled Gene Histories
1.2 Transfers Carry Timing Information
1.3 Inferring Transfers Using Phylogenetic Reconciliations
1.4 Time Consistency
1.5 Adding External Sources of Information
2 Methods
2.1 Selection of Genomes and Preparation of Data
2.2 Gene Family Inference
2.3 Gene Tree Inference
2.4 Species Tree Inference
2.5 Reconciliations
2.6 Relative Time Inference
References
Chapter 5: Estimating the Divergence Times of Alphaproteobacteria Based on Mitochondrial Endosymbiosis and Eukaryotic Fossils
1 Introduction
2 Prerequisites
2.1 Software
2.2 Genomic Data
2.2.1 Genomic Data for Phylogenomic Analysis
2.2.2 Genomic Data for Dating Analysis
2.3 Genes
2.4 Calibrations
3 Methods
3.1 Phylogenomic Reconstruction
3.1.1 Sequence Alignment, Trimming, and Concatenation
3.1.2 Recoding Amino Acids
3.1.3 Building the Phylogeny (See Note 1)
3.1.4 Testing for Alternative Topologies
3.2 Molecular Dating Analysis Using MCMCTree
3.2.1 Mapping the Calibrations onto the Species Tree
3.2.2 Selection of the Best-Fit Clock Model
3.2.3 Running MCMCTree
3.2.4 Sampling from the Prior Distributions
3.2.5 Assessing the Uncertainties Associated with Molecular Dating Analysis
3.3 MCMC Convergence Diagnostics
3.3.1 Effective Sample Size (ESS)
3.3.2 Comparison Within the Same Run and Between Different Runs
3.4 Data Visualization
4 Notes
Data and Code Availability
References
Part II: Deep Phylogeny
Chapter 6: Phylogenetic Analysis That Models Compositional Heterogeneity over the Tree
1 Introduction
1.1 Compositional Effects
1.2 NDCH and NDCH2 Models
2 An Example Analysis Using Bacterial SSU rRNA Genes
2.1 Analysis with Homogeneous Models
2.2 Compositional Heterogeneity
2.3 Maximum Likelihood with the NDCH Model
2.4 Bayesian Analysis
2.4.1 NDCH2
2.5 Rooting Using Tree-Heterogeneous Models
3 Software and Scripts
References
Chapter 7: Assembling a Reference Phylogenomic Tree of Bacteria and Archaea by Summarizing Many Gene Phylogenies
1 Introduction
2 Materials
2.1 Computing Environment
2.2 Software Tools
2.3 Genomic Data
3 Methods
3.1 Quality Filtering of Genomic Data
3.2 Unbiased Sampling of Genomes
3.2.1 Calculate Genome Distances
3.2.2 Perform Prototype Selection
3.3 Global Marker Genes Representing Genome Evolution
3.3.1 Identify Protein-Coding Genes
3.3.2 Identify Global Marker Genes
3.3.3 Further Filter Genomes by Marker Count
3.4 Reconstruction of Many Gene Trees
3.4.1 Alignment of Protein Sequences
3.4.2 Quality Filtering of Sequence Alignments
3.4.3 Phylogenetic Filtering of Gene Sequences
3.4.4 Selection of the Substitution Model
3.4.5 Reconstruction of Gene Trees
3.5 Summarize Many Gene Trees to Model Genome Evolution
3.5.1 Scalable Gene Tree Summary Using ASTRAL
3.5.2 Calculate Branch Lengths in the Expected Number of Substitutions
3.6 Working with Large Phylogenetic Trees
3.6.1 Interactive Visualization of Trees
3.6.2 Programmable Manipulation of Trees
4 Notes
4.1 Marker Genes
4.2 Alignment Filtering
4.3 Gene Tree Filtering
4.4 Gene Paralogy
References
Chapter 8: Testing Phylogenetic Stability with Variable Taxon Sampling
1 Introduction
1.1 Topology Instability: Common Causes and Solutions
1.1.1 Choice of Phylogenetic Method
1.1.2 Modeling the Evolutionary Process
1.2 The Case of Taxon Sampling
2 Methods
2.1 Reconstruction of Species Phylogenies
2.2 Phylogenetic Assessment of Taxon Sampling
3 PATS Deconstructed
3.1 Orthology Determination
3.1.1 BLAST
3.1.2 Proteinortho
3.2 Selection of Orthologs
3.3 Alignment, Gap Filtering, and Concatenation
3.4 Tree Reconstruction
3.5 Taxon Sampling Scenarios
3.5.1 Keep One
3.5.2 Remove One
3.5.3 Remove Group
3.6 Phylogenetic Comparisons
4 Notes
References
Chapter 9: Rooting Species Trees Using Gene Tree-Species Tree Reconciliation
1 Introduction
2 Rooting a Tiny Tree of Life Using ALE
3 Workflow for Estimating Rooted Species Trees Using ALE
3.1 Preparing Data for Analysis
3.2 Inferring Pairwise Alignments
3.3 Clustering Genes Using MCL
3.4 Constructing Gene Family Alignments
3.5 Infer Bootstrap Distribution of Trees for Each Gene Family
3.6 Inferring the Unrooted Species Tree
3.7 Computing Reconciled Gene Trees for Each Candidate Rooted Species Tree
3.8 Comparing Support for Candidate Root Positions
3.9 Evaluating the Nature of the Root Signal (Robustness Checks)
3.10 Gene Content Evolution on the Most Likely Rooted Species Tree
4 Comparison to Related Methods
5 Conclusions
References
Chapter 10: Reconstructing Gene Gains and Losses with BadiRate
1 Introduction
2 Methods
2.1 Stochastic Turnover Models
2.2 Branch Models
2.3 Root Family Size Model
2.4 Contrasting Hypotheses Under Different Models
3 Running the Program
3.1 Prepare the Software and the Data
3.2 Running BadiRate
3.2.1 Inference Under the GR Model
3.2.2 Specifying Branch-Specifics Models
3.2.3 Inference Under the Most Complex Model, the FR Model
3.3 Output
3.3.1 Interpretation
4 Notes
References
Chapter 11: Deciphering Microbial Gene Family Evolution Using Duplication-Transfer-Loss Reconciliation and RANGER-DTL
1 Introduction
2 Description of Software Tools
3 Suggested Computational Protocol
3.1 Step 1: Species Tree Estimation
3.2 Step 2: Gene Tree Construction and Error-Correction
3.3 Step 3: Gene Tree Rooting
3.4 Step 4: DTL Reconciliation
3.5 Step 5: Interpreting Reconciliation Output
4 Notes
5 Conclusion
References
Part III: Trait Evolution
Chapter 12: Ancestral State Reconstruction Using BayesTraits
1 Introduction
2 Methods
2.1 Reconstructing Multistate Traits
2.2 Reconstructing Traits Using Independent Contrast Models
2.3 Interpolating Trait Data
3 Notes
3.1 Decay in Signal
3.2 Evolutionary Rates and Branch Lengths
3.3 Creating Tags in BayesTrees
3.4 Model of Evolution
3.5 Uncertainty in Trait Data
3.6 Priors
References
Chapter 13: An Integrated Method to Reconstruct Ancient Proteins
1 Introduction
2 Methods
2.1 Collection and Curation of an Extant Protein Sequence Dataset
2.2 Multiple Sequence Alignment
2.3 Model Selection and Phylogenetic Reconstruction
2.4 Inference of Ancestral Protein Sequences
2.5 Selection of Ancestral Protein Sequences for Experimental Study
2.6 Ancestral Gene Synthesis
3 Conclusions
4 Notes
References
Chapter 14: Methodologies for Microbial Ancestral Sequence Reconstruction
1 Introduction
2 Overview of Ancestral Sequence Reconstruction Methods
2.1 Ancestral Sequence Reconstruction Based on Maximum Parsimony
2.2 Ancestral Sequence Reconstruction Based on Maximum Likelihood and Bayesian Approaches
3 Biases in Ancestral Sequence Reconstruction Methodologies
3.1 Influence of Genetic Diversity on Ancestral Sequence Reconstruction
3.2 Biases Caused by Ignored Recombination
3.3 Biases Caused by the Substitution Process
4 ASR in Practice: Inferring Ancestral Sequences Accounting for the Protein Structure with the Evolutionary Framework ProtASR
5 Discussion and Future Research
References
Chapter 15: Reconstruction of State-Dependent Diversification: Integrating Phenotypic Traits into Molecular Phylogenies
1 Introduction
1.1 Speciation and Extinction Are Long-Termed Processes That Result in Current Clade Diversity
1.2 Brief Overview of Widely Used Diversification Models: Diversity-Dependence, Key Innovation Model, Bayesian Analysis of Mac...
1.3 Information Summarized by Nodes and Branches in a Phylogenetic Reconstruction: The Standard Birth-Death Process
2 Traits Influencing Speciation and Extinction
2.1 The Binary-State Model by Maddison
2.2 Examples of Trait States Spurring Diversification Rates in Macroorganisms: State-Dependent Diversification Has a Huge Pote...
3 Working Example of a SecSSE Analysis in the R Environment
3.1 Dataset
3.2 Setting Models of Trait Evolution: Gradual and Jumping Trait Evolution
3.3 Setting Models of Speciation and Trait Dependence
3.4 Setting Extinction-Dependent Diversification Models
3.5 Setting the Remaining Parameters
3.6 Typical Model Comparison and Parameter Estimation
4 Additional Important Issues
4.1 Robustness of the Analysis: Tree Size, Resolved Tree, and Number of Trait States
4.2 Likelihood Landscape and Tricks for Handling the Number of Free Parameters
References
Chapter 16: Lifestyle Evolution Analysis by Binary-State Speciation and Extinction (BiSSE) Model
1 Introduction
2 Theoretical Background of BiSSE
3 Analysis Using R
3.1 Data Preparation
3.2 Maximum Likelihood Method
3.3 Statistical Test for Evolutionary Parameters Using the ML Method
3.4 Bayesian Inference and MCMC Methods
3.5 Statistical Test for Evolutionary Parameters Using the BI and MCMC Methods
4 A Case Study: Generalist and Specialist Microbial Lifestyle Evolution
5 Conclusion
References
Chapter 17: Assessing a Role of Genetic Drift for Deep-Time Evolutionary Events
1 Introduction
2 Prerequisites
2.1 Software
2.2 Genomic Data
2.3 Underlying Principles of RCCalculator
3 Bioinformatics Procedure
3.1 Phylogenomic Tree Construction
3.2 Ortholog Identification and Sequence Alignment
3.3 Calculation of Transition/Transversion Ratio
3.4 Calculation of dR/dC Ratio
3.5 Statistical Analysis
3.6 Continue with Existing Results
4 Data Interpretation
References
Index