Phylogenomics: A Primer

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Detailed Contents
Preface
Acknowledgements
Section I Foundations of Phylogenomics 
	Chapter 1 What is Phylogenomics?
		Phylogenomics and Bioinformatics
		Bioinformatics Tools for Finding Patterns in Biological Experiments
			The rise of phylogenomics
		Sub-Branches of Phylogenomics
		The Phylogenomic Toolbox
		Basic Computational Tools in Phylogenomics
		Statistics Help Compare Genetic Sequences and Generate Phylogenetic Trees
		Parametric Statistics Are Derived from Distributions
		Nonparametric Statistical Analyses Are Useful in Many Situations
		Maximum Likelihood and Bayesian Analysis Are Standard Statistical Methods Used in Phylogenomics
		Key Attributes of Phylogenomicists
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 2 The Biology and Sequencing of Genetic Information: DNA, RNA, and Proteins
		Nucleic Acids
			DNA molecules efficiently transmit information
			DNA is synthesized by specific pairing
			DNA can mutate and lead to descent with modification
			Polymerase chain reaction (PCR) is a milestone development
		Proteins
			Proteins are linear polymers of amino acids
			Proteins have multiple levels of structure
			Translation of DNA to amino acids is accomplished by the genetic code
			Reading frame in nucleic acid sequences
		The DNA Data Explosion
			Nucleic acid sequencing methods are increasingly powerful
			Next-generation sequencing allows for rapid analysis of genomes
			Other applications of next-generation sequencing
		Alternatives to Whole Genome Sequencing
			Single-nucleotide polymorphisms (SNPs) differ at one position in a designated DNA sequence
			Microarrays
			Genome reduction methods
		Analyzing Gene Expression
			RNA-Seq is a method for obtaining transcriptomic data
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 3 Evolutionary Principles: Populations and Trees
		Darwin, Wallace, and Evolutionary Theory
			Four early contributions
			Darwin’s ideas lacked a valid genetic mechanism
			The study of evolution can be divided into microevolution and macroevolution
		Microevolution
			Population genetics focuses on microevolution
			Advances in molecular techniques led to new thinking in evolutionary biology
			Codon changes and usage can provide insights into natural selection
			Microevolutionary studies often rely on computational modeling
		Macroevolution
			Macroevolution studies rely heavily on systematics and phylogenetics
			Relationships and systematics
			There are several approaches to tree building
			Tree thinking
			Phylogenetics can help establish homology
		Species
			The definition of species is heavily debated
			Defining species phylogenetically
		Updates on Darwinian Evolution
			Punctuated equilibrium suggests that not all evolution is gradual
			Epigenetic changes are caused by influences outside of the genetic system
		Summary
		Suggestions for Students
		Evolution Websites
		Evolution textbooks
		Discussion Questions
		Further Reading
Section II Data
	Chapter 4 Data Storage—The Basics
		Databases and Phylogenomics
			DNA sequences are stored in large international databases
			Specific data sets may be held in special repositories
			These databases offer free access and availability for scientific inquiry
		Information Retrieval from the NCBI Database
			Publications are archived in the PubMed database
			Working with molecular sequences stored in GenBank
			Whole genomes are accessible on the Genome Page
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 5 Sequence Alignment and Searching Sequence Databases
		Homology of Genes, Genomic Regions, and Proteins
			Genomes can diverge by speciation and by duplication
			Sequence alignment is an important procedure in phylogenomics
			Basic, paired nucleic acid sequence alignment
			Basic, paired protein sequence alignment
			Dynamic programming and sequence alignment
		Database Searching via Pairwise Alignments: The Basic Local Alignment Search Tool
			BLAST identifies highly similar sequences
			BLAST is optimized for searching large databases
			There are multiple types of BLAST for nucleotide and amino acid sequences
			BLAST searches are easy to do
			Whole genome alignments can also be performed
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 6 Multiple Alignments
		Multiple Sequence Alignment
		Changing Alignment Parameters
			Multiple optimal alignments may exist
		Specialized Alignment Programs
		Choosing an Alignment Program
			Automated alignment results are frequently adjusted “by eye”
			Alignment programs can be compared by use of benchmark data sets
		Dynamic versus Static Alignment
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 7 Genome Sequencing and Annotation
		Whole Genome Sequencing (WGS)
			Next-generation sequencing
			The nuts and bolts of assembly
		Gene Finding and Annotation
			Gene finding can be accomplished via extrinsic, ab Initio, and comparative approaches
			Gene functional annotation
			Genome completeness
		Summary
		Recommendations to Students
		Discussion Questions
		Further Reading
	Chapter 8 Genomics Databases: Genomes and Transcriptomes
		Genome Information Is Stored in Multiple Locations
			BioSample/BioProject/Short Read Archives (SRA) store archival information for projects used in broader genomics research archived in INSDC
		Data Archiving and Databases Outside of the INSCD System
			Organismal-focused genome and transcriptome databases
		Summary
		Recommendations for Students
		Problems and Discussion
		Further Reading
	Chapter 9 Amplicon Databases: BoLD and Bacterial 16S rDNA Databases
		DNA Barcoding and the BoLD Database
			DNA barcoding
			Taxonomy and speciation studies involve the species delimitation
			DNA taxonomy and DNA barcoding
			Character-based or distance-based approaches to DNA barcoding result in identification of species
			Is there enough information in a single gene to do DNA barcoding?
			Potential new species are flagged by DNA barcoding
			The BoLD Repository
		Ribosomal RNA Databases
			Amplicon sequencing, microbiomes, metagenomics, and eDNA
			Databases are used to identify the species in a microbiome, metagenome, and eDNA sample
				Classifiers for identifying microbial species in eDNA, microbiome studies, and metagenomics
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
Section III Phylogenetic/Phylogenomic Analysis
	Chapter 10 Introduction to Tree Building
		Phylogenetic Tree Building Overview
			Which phylogenetic method should be used?
			The number of trees grows with each additional taxon
			Trees can be rooted by several methods
		Characters and Weighting
			Character states in molecular data may include the presence of genes and the sequence of nucleotides or amino acids
			Some discrete and numerical character states are ordered
			Characters can be weighted relative to one another
			Which characters should be used?
			A matrix for demonstrating phylogenetic analysis
		Basics of Parsimony Analysis
			Fitch’s algorithm uses set theory
			Rescoring characters
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 11 Distance and Clustering
		Corrections for Multiple Hits May Be Introduced
		Corrections Using Evolutionary Models
			Neighbor joining is a stepwise-based approach to tree-building
		Minimum Evolution Uses Minimal Distance as a Criterion to Choose the Best Solution among Multiple Trees
		Summary
		Recommendations for students
		Discussion Questions
		Further Reading
	Chapter 12 Maximum Likelihood
		Transformation and Probability Matrices
			Character weighting schemes
			Likelihood analysis incorporate probability matrices
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 13 Search Strategies and Robustness
		So Many Trees, So Little Time
			Tree space basics
			Selection of a starting tree
			Peaks in tree space can be reached by branch swapping
			Moving from local optimality peaks to peaks with higher optimality
		Robustness of Phylogenetic Trees
		Bremer Support Estimates Robustness of a Node
		Resampling to Determine Node Robustness
			Bootstrapping assesses node robustness by resampling with replacement
			Jackknifing assesses node robustness by resampling without replacement
			Parametric bootstrapping applies a distribution model to the data
		Resampling Gene Partitions
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 14 Rate Heterogeneity, Long Branch Attraction, and Likelihood Models
		Long Branch Attraction
		Rate Heterogeneity
			Rate heterogeneity and invariant sites (I)
			Rate heterogeneity and the gamma distribution (Γ or G)
			Combining the invariant-sites parameter and a gamma distribution
			Other methods accommodating rate heterogeneity
		Comparing Likelihood Models
			Programs can compare models
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 15 Bayesian Approaches in Phylogenetics
		Bayesian Inference
			Generating a distribution of trees is an important application of the Bayesian approach
			What do we need from a Bayesian phylogenetic analysis?
			MCMC is critical to the success of Bayesian analysis
		Bayesian Parameters in a Phylogenetic Context
			Model selection can be utilized on any biologically meaningful partition
			Selection of priors
			More MCMC generations improves results at an increased computational cost
			Assessing the efficiency of a Bayesian phylogenetic analysis
			Interpreting posterior probabilities of clades
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 16 Incongruence of Gene Trees
		Incongruence of Trees
			Taxonomic congruence via supertrees
			Character congruence via total evidence supermatrices
			Assessments of incongruence can help decide what to concatenate
			The incongruence length difference test
			Likelihood tests for incongruence
			Fork indices provide measures of tree similarity
		Robinson-Foulds Metric and Subtree Prune-and-Regraft Distance (SPR distance)
		The Gene Tree/Species Tree Problem
			Examples of incomplete lineage sorting in closely related taxa
			Coalescence and the gene tree/species tree problem
		Horizontal Transfer
		Programs That Consider Nonvertical Evolution and Incomplete Lineage Sorting to Infer Phylogeny
			Coalescence programs use both gene trees and species trees as input
			Programs that consider horizontal gene transfer generate nets and webs
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 17 Phylogenetic Programs and Websites
		Website Summaries of Programs
		The Classics
			Likelihood programs
			Bayesian phylogenetic inference programs
			Parsimony programs
			Networks
		The Comparative Method
		Tree Visualization Programs
		All-Purpose Websites and Software Companies
		Programming Languages and Packages
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
Section IV Population Genomics
	Chapter 18 Population Genetics and Genomes
		High-Throughput Methods and Population Genetics
			Kimura and Lewontin contributed important new ways to think about genes in nature
			The Hardy–Weinberg theorem has been extended in modern population genetics
		DNA Variation among Individuals
			Single-nucleotide polymorphisms (SNPs)
			Microsatellites provide another analytical tool for species where SNPs are less abundant
			RAD markers are a source of data for modern population genomics
		Extending Basic Population Genetics to DNA Sequences
			Tajima’s D distinguishes between sequences evolving neutrally and those evolving non-neutrally using allele frequencies
			F statistics measure the degree of isolation of entities
			There are two approaches to estimating population-level statistics
			FST and related measures have four major uses in evolutionary biology
		Imputation
		Population-Level Techniques: Mismatch Distribution Analysis, STRUCTURE Analysis, Principle Components Analysis, and Analysis Platforms
			Mismatch distribution analysis compares haplotype data of populations
			STRUCTURE analysis reveals substructure and genetic cross talk
			Principle components and genomic data
			Population genomics analysis platforms
		Summary
		Recommendations for students
		Discussion Questions
		Further Reading
	Chapter 19 Population Genomics Approaches
		Genome-Wide Association Studies
			A simple example illustrates the association technique
			The National Human Genomics Research Institute maintains a database of genome-wide association studies
		Programs That Can Perform GWAS Analyses
		Role of the Coalescent in Population Genetics
			The coalescent addresses the time for an allele to coalesce and the variation in populations under drift
			The coalescent in practice explores a large number and a broadly representative sample of plausible genealogical scenarios
			High-quality DNA sequence data from a random sample constitute the best input for a coalescence analysis
			Importance sampling and correlated sampling are used to generate a collection of simulated genealogies
			Programs for coalescence analysis include BEAST and Lamarc
		Genetic Hitchhiking and Selective Sweeps
			Selective sweeps are detected in four basic ways
			Empirical examples of selective sweeps include boxers, flies, and humans
			Hard and soft sweeps produce different effects in the genome
			Genome-wide scans to address population genetic and evolutionary questions
			Phylogenetic shadowing identifies regulatory elements in DNA sequences
			Regions of the human genome experience accelerated evolution
			Regions that are both strongly conserved and rapidly deleted are of interest
		Summary
		Recommendations for students
		Discussion Questions
		Further Reading
	Chapter 20 Detecting Natural Selection: The Basics
		Analyzing DNA Sequences for Natural Selection
			DNA sequences can be examined for silent and replacement changes
			Several variables affect the detection of natural selection at the genomic level
			Approximate methods of determining dN/dS
			Basic dN and dS calculations begin with counting the observed number of changes
			Scaling for redundancy and getting the number of potential substitutions is necessary for determining dN/dS
			Pathways of codon change are an important element in calculating dN/dS
			Codon change pathways can be used to account for redundancy
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 21 Refining the Approach to Natural Selection at the Molecular Level
		Accounting for Multiple Hits in DNA Sequences for dN/dS Measures
			The Jukes–Cantor conversion corrects for multiple hits
		Estimating Natural Selection Requires Adjusting the Calculation of Sequence Changes
		Expanding the Search for Natural Selection at the Molecular Level
			Statistical tests of significance are required at various levels
				Species 1
				Species 2
			Natural selection is variable across protein components and across time
			Examples of nonuniformity are seen in Drosophila and in the BRCA1 gene
			Maximum likelihood approaches are implemented in selection studies at the molecular level
			Statistical tests using dN and dS
			There are caveats when detecting selection at the molecular level
			Transcriptomics and whole genome sequencing has opened the way for searches for natural selection at an unprecedented level
		Codon Selection Bias
			Codon selection bias can be calculated manually or by various analytical methods
			Codon usage bias usually occurs in cellular housekeeping genes and varies among species
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
Section V Phylogenomics in Action
	Chapter 22 Constructing Phylogenomic Matrices
		Why Choose the Programs We Focus On?
			Formatting matrices for population genomics analysis
		Formatting Arlequin Files
		Formatting STRUCTURE Files
		Formatting HYPHY Files
		Formatting PAML Files
		Formatting PLINK Files
			Constructing phylogenomic matrices
		Determining Orthology and Constructing Individual Gene Matrices
		Concatenating Individual Gene Alignments
		Partitions and Partitioning
		Formatting Partitions in PAUP* and MrBayes (NEXUS)
		Formatting Partitions in PHYLIP
		Formatting Partitions in RaxML and IQtree
		Formatting Partitions in TNT
		Web-Based Programs for Formatting Phylogenomic Matrices
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 23 Phylogenomics and the Tree of Life
		Problems with Phylogenomic Studies
		Supertrees or Supermatrices
			Grafting supertree approach
			Matrix representation approach
			Divide-and-conquer approach
		Examples of Phylogenomic Studies
			Shallow targeted sequencing of over 70,000 eukaryotes recovers major eukaryotic groups
			Whole genome microbial phylogenomics
				Specific problems in bacterial phylogenomics
				Does a tree of life really exist for bacteria?
				Microbial Trees of Life
			The deep relationships of Metazoa
			Green phylogenies
		Yeast and Drosophila Represent Examples of Concatenation and Lineage Sorting Problems in Phylogenomics
		Coalescence Can Partially Solve the Problem of Incongruence
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 24 Comparative Genomics
		Characterizing Genomes by Orthology
			Clusters of orthologous groups is a method that enables identification of orthologs of genes across multiple species
			Single linkage clustering compares genes in a cross-species context based on sequence
			A presence/absence matrix is constructed via single linkage clustering
		Comparative Genomics Approaches
			Venn diagrams, EDGAR, and Sungear visualize the overlap of genes from two or more genomes
				The pangenome
			Genome content analysis was first accomplished for bacterial genomes
			Caveats with genome content analysis in phylogenetic analysis
			Using genome content in evolutionary studies
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 25 Environmental DNA (eDNA)
		Any Environment Can Be Examined for Its Microbial Makeup
		Amplicon Sequencing, Microbiomes, Metagenomics, and eDNA
			The next-generation approach
			Data management—format
			Data management—processing
			Data management storage
			Shotgun sequencing
			Software
			Making ecological/environmental inferences
			Caveats and recommendations
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
	Chapter 26 Phylogenomic Approaches to Understanding Gene Function and Evolution
		Transcription-Based Approaches
			Transcriptomics is used for class comparison, prediction, and discovery
			Data are transformed for use in dendrograms and other clustering techniques
			Specific next-generation sequencing approaches applied to transcriptome analysis
			Transcriptomic approaches are useful in evolutionary and phylogenomic studies
		Protein–Protein Interactions
			Generating data for protein–protein interaction research
				2H screening
				PCA screening
			Computational methods for examining protein–protein interactions
			Model organism gene and protein function can be studied by Web-based approaches like ENCODE
			Functional phylogenomics employs common ancestry to infer protein function
			Phylogenomic gene partitioning can be used to explore function
			A gene presence/absence matrix was employed to examine evolution in the major metazoan lineages
			Transcript sequences and phylogeny can be used to study plant function
			Gene function clustering in Caenorhabditis elegans from RNA interference phenotypes
			Gene ontology facilitates the comparison of genes
		Summary
		Recommendations for Students
		Discussion Questions
		Further Reading
Index