Glossary

6.2 Phylogenetic tree construction

12 min read•august 21, 2024

Phylogenetic trees are essential tools in bioinformatics for understanding evolutionary relationships. They provide visual representations of hypothesized evolutionary histories, allowing researchers to infer common ancestors and divergence patterns among organisms or genes.

Constructing accurate phylogenetic trees involves analyzing molecular sequence data using various computational methods. These methods employ different algorithms and statistical models to estimate the most likely tree topology and branch lengths, helping researchers uncover evolutionary relationships.

Fundamentals of phylogenetic trees

  • Phylogenetic trees serve as crucial tools in bioinformatics for understanding evolutionary relationships among organisms or genes
  • These trees provide visual representations of hypothesized evolutionary histories, allowing researchers to infer common ancestors and divergence patterns
  • Constructing accurate phylogenetic trees involves analyzing molecular sequence data, often utilizing various computational methods and statistical models

Tree components and terminology

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  • Nodes represent taxonomic units (species, genes, or populations) and can be internal (hypothetical ancestors) or external (extant taxa)
  • Branches connect nodes and represent evolutionary lineages, with branch lengths often indicating genetic distance or time
  • Clades consist of all descendants of a common ancestor, forming monophyletic groups within the tree
  • Polytomies occur when more than two lineages diverge from a single node, indicating unresolved relationships

Evolutionary relationships representation

  • Topology of the tree illustrates the branching pattern and relative relationships among taxa
  • Sister taxa share a most recent common ancestor and appear as adjacent branches on the tree
  • Outgroups serve as reference points for rooting trees and determining the direction of evolution
  • Horizontal axis typically represents genetic distance or time, while vertical axis arranges taxa for clarity

Rooted vs unrooted trees

  • Rooted trees have a defined root representing the most recent common ancestor of all taxa in the tree
  • Unrooted trees show relationships among taxa without specifying the evolutionary direction or root position
  • Rooted trees provide information about the order of divergence events and ancestral relationships
  • Unrooted trees can be useful when the root position is uncertain or when focusing on relative relationships among taxa

Methods of tree construction

  • Phylogenetic tree construction methods in bioinformatics aim to infer evolutionary relationships from molecular sequence data
  • These methods employ various algorithms and statistical models to estimate the most likely tree topology and branch lengths
  • Choosing the appropriate method depends on the research question, dataset size, and computational resources available

Distance-based methods

  • Calculate pairwise distances between sequences to construct a distance matrix
  • Use algorithms to convert the distance matrix into a tree structure
  • (NJ) method iteratively joins the closest pairs of taxa to build the tree
  • Advantages include computational efficiency and suitability for large datasets
  • Limitations involve potential loss of information when reducing sequences to distances

Maximum parsimony

  • Seeks the tree topology that requires the fewest evolutionary changes to explain the observed data
  • Identifies the most parsimonious tree by minimizing the number of character state changes along branches
  • Useful for closely related sequences with low levels of homoplasy
  • Can handle both molecular and morphological data
  • May struggle with long branch attraction and rate heterogeneity among lineages

Maximum likelihood

  • Evaluates the probability of observing the given sequence data under different evolutionary models and tree topologies
  • Selects the tree with the highest likelihood of producing the observed data
  • Incorporates complex models of sequence evolution, allowing for rate variation among sites
  • Computationally intensive, especially for large datasets
  • Provides a statistical framework for hypothesis testing and model comparison

Bayesian inference

  • Uses Bayesian probability theory to estimate the posterior probability distribution of trees
  • Incorporates prior knowledge about evolutionary processes and tree topologies
  • Employs Markov Chain Monte Carlo (MCMC) algorithms to sample from the posterior distribution
  • Produces a set of trees with associated probabilities rather than a single best tree
  • Allows for uncertainty quantification and integration of multiple sources of information

Sequence alignment for phylogenetics

  • Sequence alignment plays a crucial role in phylogenetic analysis by identifying homologous positions across multiple sequences
  • Proper alignment ensures that comparisons are made between evolutionarily related sites, improving the accuracy of tree inference
  • Bioinformatics tools for sequence alignment must account for various evolutionary processes, including substitutions, insertions, and deletions

Multiple sequence alignment

  • Aligns three or more sequences simultaneously to identify conserved regions and evolutionary patterns
  • Progressive alignment methods (ClustalW, MUSCLE) build alignments iteratively, starting with the most similar sequences
  • Consistency-based methods (T-Coffee, MAFFT) consider information from all pairwise alignments to improve overall alignment quality
  • Profile-based methods (HMMER) use position-specific scoring matrices to align sequences to existing alignments or profiles

Substitution models

  • Describe the rates of different types of nucleotide or amino acid substitutions over evolutionary time
  • Simple models (JC69, K2P) assume equal base frequencies and limited rate variation
  • More complex models (GTR, WAG) account for unequal base frequencies and rate heterogeneity among sites
  • Model selection tools (ModelTest, ProtTest) help identify the best-fitting for a given dataset
  • Appropriate model selection improves the accuracy of phylogenetic inference and estimation

Gaps and indels handling

  • Gaps in alignments represent insertion or deletion events (indels) during evolution
  • Treatment of gaps affects phylogenetic inference and can be handled in various ways:
    • Treating gaps as missing data
    • Coding gaps as binary characters (presence/absence)
    • Using more complex indel models that consider gap length and position
  • Proper gap handling improves alignment quality and phylogenetic accuracy, especially for divergent sequences
  • Some methods (POY, SATé) simultaneously optimize alignment and tree topology to address the interdependence of these processes

Tree building algorithms

  • Tree building algorithms in bioinformatics convert sequence alignment or distance data into phylogenetic tree structures
  • These algorithms employ different strategies to search the tree space and identify optimal topologies
  • The choice of algorithm depends on the dataset size, computational resources, and specific research objectives

Neighbor-joining method

  • Agglomerative clustering algorithm that constructs trees based on a distance matrix
  • Starts with a star-like tree and iteratively joins the closest pair of taxa or nodes
  • Adjusts distances to account for previously joined nodes, maintaining additivity
  • Computationally efficient, making it suitable for large datasets
  • Produces an that can be rooted using an outgroup or midpoint rooting

UPGMA

  • Unweighted Pair Group Method with Arithmetic Mean constructs ultrametric trees
  • Assumes a constant evolutionary rate across all lineages ()
  • Iteratively clusters taxa based on average distances between groups
  • Produces a with all leaves equidistant from the root
  • Simple and fast, but often unrealistic due to the strict molecular clock assumption

Fitch-Margoliash method

  • Least squares method that minimizes the difference between observed and expected pairwise distances
  • Allows for unequal evolutionary rates among lineages, unlike UPGMA
  • Iteratively adjusts branch lengths to improve the fit between observed and expected distances
  • Can handle datasets with heterogeneous evolutionary rates
  • Computationally more intensive than Neighbor-joining or UPGMA

Statistical support for trees

  • Statistical support measures in bioinformatics quantify the confidence in phylogenetic tree topologies and specific clades
  • These measures help researchers assess the reliability of inferred evolutionary relationships and identify areas of uncertainty
  • Different methods provide complementary information about tree robustness and can be used in combination for comprehensive evaluation

Bootstrap analysis

  • Resamples columns from the original alignment with replacement to create multiple pseudo-replicate datasets
  • Constructs trees for each pseudo-replicate and calculates the frequency of observed clades
  • Bootstrap values represent the percentage of pseudo-replicate trees supporting a given
  • Values above 70% generally indicate strong support for a clade
  • Limitations include sensitivity to model misspecification and inability to detect systematic bias

Jackknife resampling

  • Similar to bootstrap but involves subsampling without replacement
  • Typically removes a fixed percentage (e.g., 50%) of the original data for each replicate
  • Jackknife support values indicate the proportion of subsamples supporting a given clade
  • Less commonly used than bootstrap but can be useful for assessing the impact of individual characters
  • May be more appropriate for datasets with many invariant sites or when testing the effect of alignment length

Posterior probabilities

  • Derived from Bayesian phylogenetic analysis, representing the probability of a clade given the data and model
  • Calculated as the proportion of trees in the posterior distribution that contain a given clade
  • Generally higher than bootstrap values and more sensitive to detecting true clades
  • Can be inflated in some cases, especially with complex models or limited data
  • Provide a direct probabilistic interpretation of clade support within the Bayesian framework

Tree visualization and interpretation

  • Visualization tools in bioinformatics enable researchers to effectively communicate and analyze phylogenetic tree structures
  • Proper interpretation of tree visualizations requires understanding of both biological and statistical aspects of tree construction
  • Various software packages offer different visualization options and analytical features to aid in tree interpretation

Tree drawing software

  • Dedicated phylogenetic software (, FigTree) provide basic tree visualization and editing capabilities
  • Advanced visualization tools (iTOL, EvolView) offer interactive features and customization options
  • Programming libraries (ape in R, Biopython) allow for programmatic tree manipulation and visualization
  • Web-based platforms (Phylo.io, PhyloCanvas) enable easy sharing and collaborative analysis of phylogenetic trees

Branch lengths and scales

  • Branch lengths represent genetic distance or time, depending on the tree construction method
  • Scale bars indicate the amount of genetic change or time corresponding to a given branch length
  • Ultrametric trees have equal root-to-tip distances, often used for divergence time estimation
  • Non-ultrametric trees allow for variable evolutionary rates among lineages
  • Some visualizations use cladograms with uniform branch lengths to emphasize topology over genetic distance

Clade identification

  • Monophyletic clades include all descendants of a common ancestor and are often highlighted in tree visualizations
  • Paraphyletic groups exclude some descendants and are not considered valid taxonomic units
  • Polyphyletic groups include taxa from multiple evolutionary lineages and indicate incorrect classification
  • Clade credibility values (bootstrap, posterior probabilities) can be displayed on nodes or branches
  • Collapsing poorly supported nodes or highlighting strongly supported clades can simplify tree interpretation

Molecular clock hypothesis

  • The molecular clock hypothesis in bioinformatics posits that genetic changes accumulate at a roughly constant rate over time
  • This concept allows researchers to estimate divergence times and infer evolutionary timescales from molecular sequence data
  • Various models and methods have been developed to account for rate variation and calibrate molecular clocks

Calibration of molecular clocks

  • Uses external information (fossils, biogeographic events) to assign absolute ages to specific nodes in the tree
  • Fossil calibrations provide minimum age constraints based on the oldest known fossil of a lineage
  • Secondary calibrations use age estimates from previous studies to calibrate nodes in new analyses
  • Cross-validation techniques assess the consistency of multiple calibration points
  • Careful selection and application of calibrations are crucial for accurate divergence time estimation

Relaxed clock models

  • Allow evolutionary rates to vary among lineages, relaxing the strict molecular clock assumption
  • Uncorrelated models (UCLN, UExp) draw rates independently for each branch from a specified distribution
  • Autocorrelated models (CIR, log-normal) assume rates are correlated between ancestral and descendant lineages
  • Local clock models allow rate changes at specific points in the tree while maintaining constant rates within clades
  • Improve fit to data and provide more realistic estimates of divergence times for many datasets

Divergence time estimation

  • Integrates molecular clock models, tree topology, and calibration information to estimate node ages
  • Bayesian methods (BEAST, MCMCTree) provide a flexible framework for incorporating uncertainty in all parameters
  • Penalized likelihood approaches (r8s, treePL) use semi-parametric rate smoothing to estimate divergence times
  • Relative rate tests can be used to assess whether a strict molecular clock is appropriate for a given dataset
  • Results are often presented as time-calibrated trees (chronograms) with confidence intervals for node ages

Phylogenetic tree applications

  • Phylogenetic trees serve diverse applications in bioinformatics, ranging from basic research to applied fields
  • These tools provide a framework for understanding evolutionary relationships and processes across various biological scales
  • Integration of phylogenetic approaches with other data types enhances our understanding of biological systems

Species classification

  • Inform taxonomic decisions by revealing evolutionary relationships among species
  • Identify cryptic species that are morphologically similar but genetically distinct
  • Resolve taxonomic disputes by providing a phylogenetic context for classification
  • Support the development of DNA barcoding systems for rapid species identification
  • Aid in the discovery and description of new species, especially in microbial and poorly studied taxa

Evolutionary history reconstruction

  • Infer ancestral character states and trait evolution across lineages
  • Identify key evolutionary innovations and their impact on diversification
  • Reconstruct biogeographic patterns and historical species distributions
  • Investigate coevolution between hosts and parasites or symbiotic partners
  • Examine the evolution of complex traits (morphological, behavioral, or genomic) in a phylogenetic context

Gene family evolution

  • Trace the history of gene duplication and loss events across species
  • Identify orthologs (genes derived from speciation) and paralogs (genes derived from duplication)
  • Investigate the evolution of gene function and subfunctionalization after duplication
  • Detect instances of horizontal gene transfer, especially in microbial genomes
  • Inform functional predictions for uncharacterized genes based on their evolutionary relationships

Challenges in tree construction

  • Phylogenetic tree construction in bioinformatics faces various challenges that can affect the accuracy and interpretation of results
  • These challenges arise from biological complexities, limitations of inference methods, and data quality issues
  • Understanding and addressing these challenges is crucial for robust phylogenetic analyses and reliable evolutionary inferences

Long branch attraction

  • Phenomenon where distantly related taxa with long branches are erroneously grouped together in the tree
  • Occurs due to the accumulation of multiple substitutions along long branches, obscuring true relationships
  • More prevalent in maximum parsimony analyses but can also affect likelihood and distance-based methods
  • Mitigation strategies include:
    • Increasing taxon sampling to break up long branches
    • Using more complex substitution models that account for multiple hits
    • Employing methods less susceptible to LBA (, )

Horizontal gene transfer

  • Transfer of genetic material between distantly related organisms, common in prokaryotes
  • Violates the assumption of vertical inheritance in traditional phylogenetic models
  • Can lead to conflicting phylogenetic signals and incongruence between gene trees and species trees
  • Detection methods include:
    • Identifying unusual gene distribution patterns across taxa
    • Analyzing compositional biases and codon usage patterns
    • Using reconciliation methods to compare gene trees with species trees
  • Network-based approaches (phylogenetic networks, split networks) can represent reticulate evolution

Incomplete lineage sorting

  • Occurs when ancestral polymorphisms persist through speciation events, leading to gene tree-species tree discordance
  • More common in rapidly diverging lineages or those with large effective population sizes
  • Can result in inconsistent phylogenetic signals across different genomic regions
  • Addressing ILS requires:
    • Using coalescent-based methods that explicitly model the process (ASTRAL, *BEAST)
    • Analyzing multiple independent loci to capture the distribution of gene trees
    • Employing summary statistics methods to infer species trees from collections of gene trees

Advanced phylogenetic concepts

  • Advanced phylogenetic concepts in bioinformatics extend beyond traditional tree-based methods to address complex evolutionary scenarios
  • These approaches integrate population genetics, genomics, and statistical modeling to provide more comprehensive evolutionary insights
  • Understanding and applying these concepts enables researchers to tackle challenging questions in evolutionary biology and genomics

Coalescent theory

  • Describes the genealogical process of genetic lineages merging backwards in time to a common ancestor
  • Provides a framework for modeling the relationship between gene trees and species trees
  • Multispecies coalescent models account for incomplete lineage sorting in phylogenetic inference
  • Applications include:
    • Estimating effective population sizes and divergence times
    • Inferring species trees from multiple gene trees
    • Detecting and quantifying introgression between species

Phylogenomics

  • Applies phylogenetic methods to genome-scale data, often incorporating hundreds or thousands of genes
  • Aims to improve phylogenetic resolution and accuracy by leveraging large amounts of genomic information
  • Challenges include:
    • Handling computational complexity and big data issues
    • Addressing gene tree heterogeneity and conflicting phylogenetic signals
    • Developing methods for ortholog identification and alignment of large datasets
  • Approaches include concatenation (supermatrix) and gene tree reconciliation (supertree) methods

Supertree methods

  • Combine information from multiple input trees to construct a single, comprehensive phylogeny
  • Useful for integrating trees from different data sources or studies
  • Methods include:
    • Matrix representation with parsimony (MRP)
    • Maximum likelihood supertree estimation
    • Bayesian supertree inference
  • Advantages include the ability to handle missing data and incorporate trees with partially overlapping taxon sets
  • Challenges involve resolving conflicts among input trees and ensuring proper weighting of different data sources

Key Terms to Review (18)

Bayesian inference: Bayesian inference is a statistical method that applies Bayes' theorem to update the probability for a hypothesis as more evidence or information becomes available. This approach allows researchers to incorporate prior knowledge along with new data, making it a powerful tool in areas such as phylogenetics and evolutionary biology. By combining prior distributions with likelihoods from observed data, Bayesian methods help in estimating parameters and making predictions about evolutionary relationships, timing, and genomic features.
Bootstrap analysis: Bootstrap analysis is a statistical method used to assess the reliability of phylogenetic trees by resampling data with replacement. This technique generates numerous pseudoreplicates from the original dataset, allowing researchers to estimate the confidence levels of various branches in the tree. By quantifying the stability of tree structures, bootstrap analysis provides insight into the robustness of evolutionary relationships inferred from the data.
Branch length: Branch length refers to the distance or length of the lines connecting nodes on a phylogenetic tree, representing the amount of evolutionary change or time that has occurred since two species or taxa diverged from a common ancestor. This measurement is crucial for understanding the evolutionary relationships and timelines of the organisms being studied, providing insight into how closely related they are and the nature of their divergence.
Clade: A clade is a group of organisms that includes a common ancestor and all its descendants, forming a branch on the tree of life. Clades are essential for understanding evolutionary relationships, as they allow scientists to categorize organisms based on shared traits and ancestry, highlighting the interconnectedness of life forms through time.
Cladistics: Cladistics is a method of classifying organisms based on common ancestry and the branching patterns of evolution. This approach emphasizes the importance of shared derived characteristics, or synapomorphies, to define groups called clades. By organizing organisms into clades, cladistics provides insights into evolutionary relationships, making it a valuable tool in understanding molecular evolution and constructing phylogenetic trees.
Homology: Homology refers to the similarity in structure, function, or sequence between biological entities that arises from shared ancestry. This concept is crucial in understanding evolutionary relationships, as homologous sequences are used to infer common descent and can be key in identifying conserved functions across species. Homology plays a vital role in methods like multiple sequence alignment and phylogenetic tree construction, where it helps to identify evolutionary connections between organisms based on genetic similarities.
Jackknife resampling: Jackknife resampling is a statistical technique used to estimate the precision of sample statistics by systematically leaving out one observation at a time from the dataset. This method helps in assessing the stability and reliability of the estimates obtained from the data, which is particularly useful in constructing phylogenetic trees. By providing a way to evaluate how different subsets of data influence results, jackknife resampling aids in identifying the robustness of phylogenetic inferences.
Maximum Likelihood: Maximum likelihood is a statistical method used to estimate the parameters of a model by maximizing the likelihood function, which measures how well the model explains the observed data. This approach is widely applied in various fields, including evolutionary biology, to infer ancestral relationships and model molecular evolution. By providing a systematic way to evaluate how likely specific evolutionary hypotheses are given the observed data, maximum likelihood becomes essential in constructing phylogenetic trees and analyzing genomic data.
Mega: In biological and bioinformatics contexts, 'mega' often refers to a million units, typically in relation to the size of data sets or molecular sequences. It can denote large-scale analyses, such as those involving extensive phylogenetic trees or vast genomic datasets, which are crucial for understanding evolutionary relationships and genetic variation across species.
Molecular clock: A molecular clock is a method used to estimate the time of evolutionary events by analyzing the rate of genetic mutations over time. This concept allows scientists to infer the timing of divergences in species and understand evolutionary relationships. By comparing the genetic material of different organisms, researchers can build a timeline of when species split from common ancestors, which aids in understanding both molecular evolution and phylogenetic relationships.
Monophyletic group: A monophyletic group, also known as a clade, is a set of organisms that includes an ancestor and all its descendants, representing a complete branch on the tree of life. This concept is essential for understanding evolutionary relationships, as it ensures that all members of the group share a common ancestor, making it a fundamental component in phylogenetic tree construction.
Neighbor-joining: Neighbor-joining is a distance-based method for constructing phylogenetic trees that allows researchers to infer evolutionary relationships between a set of species or sequences. This method works by creating a tree that minimizes the total branch length based on pairwise distance data, efficiently grouping similar sequences while accommodating for varying rates of evolution among different lineages.
Nucleotide Sequences: Nucleotide sequences are the ordered arrangements of nucleotides in a DNA or RNA molecule, which encode the genetic information necessary for the functioning of living organisms. These sequences play a crucial role in understanding genetic relationships, evolutionary processes, and functional properties of genes through comparisons and alignments. The analysis of nucleotide sequences allows researchers to identify similarities and differences across species, aiding in the construction of phylogenetic trees and enhancing our understanding of biological functions.
Protein sequences: Protein sequences are linear chains of amino acids that make up proteins, determined by the genetic code. They play a crucial role in understanding protein structure and function, as well as evolutionary relationships between different species. Analyzing these sequences through various alignment methods helps in identifying similarities, differences, and functional motifs, which are essential in bioinformatics.
Raxml: RAxML (Randomized Axelerated Maximum Likelihood) is a software tool used for estimating phylogenetic trees based on DNA or protein sequence data. It employs maximum likelihood methods to build trees that best represent the evolutionary relationships among a set of taxa, utilizing statistical models of evolution. RAxML is particularly noted for its efficiency and ability to handle large datasets, making it an essential tool in evolutionary biology and bioinformatics.
Rooted tree: A rooted tree is a type of tree structure in which one node is designated as the root, and all other nodes are organized hierarchically beneath it, forming a clear parent-child relationship. This structure is important in phylogenetic analysis as it represents evolutionary relationships, with branches illustrating the connections and divergence of species over time. The root signifies the common ancestor of all descendants represented in the tree.
Substitution model: A substitution model is a mathematical framework used to predict the likelihood of one nucleotide or amino acid being replaced by another during evolution. It helps in understanding the processes of molecular evolution and is essential for constructing phylogenetic trees by estimating the rates at which substitutions occur across different lineages.
Unrooted tree: An unrooted tree is a type of diagram used in phylogenetics that illustrates the relationships among a set of species or taxa without indicating a common ancestor. This means that the tree does not have a designated root, making it impossible to determine the direction of evolution or the sequence of lineage divergence. Unrooted trees are particularly useful for representing the degree of similarity or difference between species based on genetic data.
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