👻Intro to Computational Biology Unit 5 – Phylogenetic Analysis

What's Phylogenetic Analysis?

• Phylogenetic analysis is the study of evolutionary relationships among biological entities (species, genes, or proteins)
• Involves constructing and interpreting phylogenetic trees to infer these relationships
• Relies on the comparison of homologous characteristics (shared due to common ancestry) to establish evolutionary connections
• Utilizes molecular data (DNA or protein sequences) and morphological data (physical traits) to infer evolutionary history
• Provides a framework for understanding the diversity and evolution of life on Earth
• Helps to identify common ancestors and trace the divergence of lineages over time
• Enables the reconstruction of the evolutionary history of genes, genomes, and species

Why Do We Care?

• Phylogenetic analysis is crucial for understanding the evolutionary history and relationships among organisms
• Helps to identify the origins and spread of infectious diseases (HIV, influenza) and inform public health strategies
• Enables the identification of genes and proteins with shared evolutionary history, facilitating functional annotation and prediction
• Provides insights into the mechanisms of evolution, such as adaptive radiation, convergent evolution, and horizontal gene transfer
• Informs conservation efforts by identifying evolutionarily distinct and endangered species
• Contributes to the development of new drugs and therapies by identifying evolutionarily conserved drug targets
• Enhances our understanding of the tree of life and the interconnectedness of all living organisms

Key Concepts and Terms

• Phylogenetic tree: a branching diagram representing the evolutionary relationships among entities
• Cladogram: a type of phylogenetic tree that depicts the relative order of branching events without indicating the amount of evolutionary change
• Phylogram: a type of phylogenetic tree that includes branch lengths proportional to the amount of evolutionary change
• Homology: similarity due to shared ancestry, used to infer evolutionary relationships
• Homoplasy: similarity due to convergent evolution or parallel evolution, not indicative of shared ancestry
• Monophyletic group: a group of entities that includes an ancestor and all its descendants
• Paraphyletic group: a group of entities that includes an ancestor but not all its descendants
• Polyphyletic group: a group of entities that does not include their most recent common ancestor

Building Phylogenetic Trees

• Phylogenetic trees are constructed based on the comparison of homologous characteristics (molecular or morphological data)
• Multiple sequence alignment is performed to identify conserved and variable regions in DNA or protein sequences
• Evolutionary models (Jukes-Cantor, Kimura 2-parameter) are used to estimate the probabilities of nucleotide or amino acid substitutions over time
• Distance-based methods (UPGMA, neighbor-joining) calculate pairwise distances between sequences and cluster them based on similarity
  • UPGMA assumes a constant rate of evolution and produces rooted trees
  • Neighbor-joining allows for varying rates of evolution and produces unrooted trees
• Character-based methods (maximum parsimony, maximum likelihood) evaluate alternative tree topologies and select the most parsimonious or likely tree
  • Maximum parsimony minimizes the total number of character state changes required to explain the data
  • Maximum likelihood estimates the probability of observing the data given a tree topology and an evolutionary model
• Bootstrapping is used to assess the statistical support for each branch in the tree by resampling the data and calculating the frequency of each grouping

Popular Methods and Algorithms

• UPGMA (Unweighted Pair Group Method with Arithmetic Mean): a distance-based method that assumes a constant rate of evolution
• Neighbor-joining: a distance-based method that allows for varying rates of evolution and produces unrooted trees
• Maximum parsimony: a character-based method that minimizes the total number of character state changes required to explain the data
• Maximum likelihood: a character-based method that estimates the probability of observing the data given a tree topology and an evolutionary model
• Bayesian inference: a probabilistic method that incorporates prior knowledge and calculates the posterior probability of trees
• Markov chain Monte Carlo (MCMC): a sampling technique used in Bayesian inference to explore the space of possible trees
• Coalescent theory: a population genetic framework for inferring gene trees within species trees and estimating demographic parameters

Tools and Software

• MEGA (Molecular Evolutionary Genetics Analysis): a user-friendly software package for sequence alignment, phylogenetic tree construction, and evolutionary analysis
• PAUP* (Phylogenetic Analysis Using Parsimony): a comprehensive software package for parsimony-based phylogenetic analysis
• RAxML (Randomized Axelerated Maximum Likelihood): a fast and accurate program for maximum likelihood-based phylogenetic inference
• MrBayes: a program for Bayesian inference of phylogeny using MCMC sampling
• BEAST (Bayesian Evolutionary Analysis Sampling Trees): a software package for Bayesian analysis of molecular sequences using MCMC
• PhyML: a fast and accurate algorithm for estimating maximum likelihood phylogenies
• IQ-TREE: an efficient software package for phylogenomic analysis using maximum likelihood
• BioPython: a Python library for computational molecular biology, including modules for sequence analysis and phylogenetics

Real-World Applications

• Tracing the evolutionary history and geographic spread of viral outbreaks (SARS-CoV-2, Ebola)
• Identifying the origins and transmission routes of foodborne pathogens (Salmonella, E. coli)
• Reconstructing the evolutionary relationships among crop species and their wild relatives to guide breeding efforts
• Investigating the evolution of antibiotic resistance in bacterial pathogens and developing strategies to combat resistance
• Studying the co-evolution of hosts and parasites to understand the dynamics of infectious diseases
• Inferring the evolutionary history of gene families and identifying orthologs and paralogs across species
• Reconstructing the phylogenetic relationships among extinct and extant species using ancient DNA
• Guiding conservation efforts by identifying evolutionarily distinct and endangered species and prioritizing their protection

Challenges and Limitations

• Incomplete or biased sampling of taxa can lead to inaccurate or misleading phylogenetic inferences
• Homoplasy (convergent evolution, parallel evolution) can obscure true evolutionary relationships
• Horizontal gene transfer can introduce discordance between gene trees and species trees
• Rapid radiations and short internal branches can be difficult to resolve with confidence
• Long-branch attraction can cause distantly related taxa to be artificially grouped together
• Computational complexity and scalability issues arise when analyzing large datasets (many taxa or long sequences)
• Choosing an appropriate evolutionary model and accounting for model misspecification can be challenging
• Assessing the robustness and statistical support of phylogenetic inferences requires careful consideration of methodological assumptions and data quality