🧬Bioinformatics Unit 6 – Phylogenetics and Evolution Analysis

Key Concepts and Terminology

• Phylogenetics studies the evolutionary relationships among organisms based on their genetic and morphological characteristics
• Evolution the process by which species change over time through the inheritance of genetic variations across generations
• Homology similarity between organisms due to shared ancestry (orthologous genes)
• Analogy similarity between organisms due to convergent evolution (similar environmental pressures)
• Molecular clock hypothesis proposes that DNA and protein sequences evolve at a constant rate over time
  • Allows for estimating the timing of evolutionary events and divergence times between species
• Parsimony principle states that the simplest explanation for observed data is preferred (minimal evolutionary changes)
• Bootstrapping statistical method used to assess the reliability of phylogenetic trees by resampling the original data

Evolutionary Theory Foundations

• Charles Darwin's theory of evolution by natural selection explains the diversity and adaptation of life on Earth
  • Variation exists within populations
  • Organisms with advantageous traits have higher survival and reproductive success (fitness)
  • Beneficial traits are passed on to offspring, leading to changes in populations over time
• Genetic drift random changes in allele frequencies within a population due to chance events (bottleneck effect, founder effect)
• Gene flow transfer of genetic material between populations through migration or interbreeding
• Mutation source of genetic variation and raw material for evolution
  • Point mutations single nucleotide changes (substitutions, insertions, deletions)
  • Chromosomal mutations large-scale changes (duplications, inversions, translocations)
• Speciation formation of new species through reproductive isolation and divergence from ancestral populations (allopatric, sympatric, parapatric)

Molecular Basis of Evolution

• DNA and protein sequences provide a molecular record of evolutionary history
• Mutations in DNA accumulate over time, leading to sequence divergence between species
• Synonymous mutations nucleotide changes that do not alter the amino acid sequence (silent substitutions)
• Non-synonymous mutations nucleotide changes that result in amino acid substitutions (missense, nonsense)
• Purifying selection removes deleterious mutations from a population, conserving functional sequences
• Positive selection favors advantageous mutations, driving adaptive evolution
• Neutral theory proposes that most molecular evolution is driven by genetic drift rather than selection
  • Kimura's neutral theory of molecular evolution

Phylogenetic Tree Construction

• Phylogenetic trees represent the evolutionary relationships among organisms or genes
• Operational taxonomic units (OTUs) taxa or sequences used to construct a phylogenetic tree
• Multiple sequence alignment process of arranging homologous sequences to identify conserved and variable regions
  • Pairwise alignment compares two sequences at a time (Needleman-Wunsch, Smith-Waterman)
  • Progressive alignment builds a multiple sequence alignment by iteratively aligning pairs of sequences (ClustalW, MUSCLE)
• Distance-based methods calculate pairwise distances between sequences to construct a tree (UPGMA, neighbor-joining)
• Character-based methods use discrete characters (nucleotides, amino acids) to infer evolutionary relationships (maximum parsimony, maximum likelihood, Bayesian inference)
• Rooting determines the direction of evolution in a phylogenetic tree (outgroup, midpoint)

Methods of Phylogenetic Analysis

• Maximum parsimony finds the tree that minimizes the total number of evolutionary changes (Fitch, Sankoff)
• Maximum likelihood estimates the probability of observing the data given a tree and evolutionary model
  • Evolutionary models describe the rates and patterns of nucleotide or amino acid substitutions (Jukes-Cantor, Kimura 2-parameter, GTR)
• Bayesian inference combines prior probabilities with the likelihood of the data to estimate posterior probabilities of trees
• Markov chain Monte Carlo (MCMC) algorithms sample from the posterior distribution of trees (Metropolis-Hastings, Gibbs sampling)
• Consensus trees summarize the information from multiple phylogenetic trees (strict consensus, majority-rule consensus)
• Coalescent theory models the genealogy of alleles within a population, accounting for ancestral polymorphism and incomplete lineage sorting

Computational Tools and Software

• BLAST (Basic Local Alignment Search Tool) finds regions of local similarity between sequences
• MEGA (Molecular Evolutionary Genetics Analysis) integrates tools for sequence alignment, phylogenetic tree construction, and evolutionary analysis
• PAUP* (Phylogenetic Analysis Using Parsimony) performs parsimony, likelihood, and distance-based analyses
• RAxML (Randomized Axelerated Maximum Likelihood) efficient maximum likelihood tree inference for large datasets
• MrBayes Bayesian phylogenetic inference using MCMC sampling
• BEAST (Bayesian Evolutionary Analysis Sampling Trees) Bayesian analysis of molecular sequences using MCMC
  • Incorporates relaxed molecular clocks and coalescent models
• Mesquite modular system for evolutionary analysis, including character evolution and comparative methods

Applications in Bioinformatics

• Comparative genomics studies the similarities and differences between genomes of different species
  • Identifies conserved and divergent regions, gene families, and evolutionary events (duplications, losses)
• Molecular epidemiology tracks the spread and evolution of pathogens using phylogenetic methods (viral outbreaks, antibiotic resistance)
• Biodiversity and conservation assesses the genetic diversity and evolutionary history of species for conservation efforts
• Ancestral sequence reconstruction infers the most likely ancestral sequences at internal nodes of a phylogenetic tree
• Protein structure and function prediction uses evolutionary information to predict the structure and function of uncharacterized proteins
• Drug discovery and design identifies evolutionarily conserved drug targets and predicts the effects of mutations on drug resistance
• Metagenomics studies the genetic material from environmental samples, using phylogenetic methods to characterize microbial communities

Challenges and Future Directions

• Computational complexity and scalability of phylogenetic algorithms for large datasets (genomes, metagenomes)
• Incomplete and uneven sampling of taxa can lead to biased or inconsistent phylogenetic estimates
• Horizontal gene transfer non-vertical transmission of genetic material between organisms, complicating phylogenetic inference
• Convergent evolution and homoplasy can obscure true evolutionary relationships
• Integration of different data types (molecular, morphological, fossil) for a comprehensive understanding of evolution
• Developing more realistic and complex evolutionary models that capture the heterogeneity of evolutionary processes across lineages and sites
• Improving the accuracy and efficiency of multiple sequence alignment methods, particularly for divergent sequences
• Incorporating the effects of selection, recombination, and population dynamics into phylogenetic inference