💻Computational Biology Unit 7 – Molecular Evolution & Phylogenetics

Key Concepts in Molecular Evolution

• Molecular evolution studies how genes and genomes change over time at the molecular level
• Mutations are the primary source of genetic variation and can be caused by errors in DNA replication, exposure to mutagens, or viral infections
• Types of mutations include point mutations (substitutions), insertions, deletions, and chromosomal rearrangements (inversions, translocations)
• Natural selection acts on genetic variation, favoring beneficial mutations and purging deleterious ones, shaping the evolution of genes and genomes
  • Positive selection promotes the spread of advantageous alleles
  • Purifying selection removes deleterious alleles from the population
• Genetic drift is the random fluctuation of allele frequencies due to chance events, particularly in small populations
• Gene duplication events create paralogous genes, which can evolve new functions or become pseudogenes
• Horizontal gene transfer allows the exchange of genetic material between organisms, even across species boundaries (bacteria, archaea)

Fundamentals of Phylogenetics

• Phylogenetics is the study of evolutionary relationships among organisms or genes
• Phylogenetic trees represent the evolutionary history and relatedness of taxa (species, populations, or genes)
  • Nodes represent common ancestors, and branches depict evolutionary divergence
  • Branch lengths can indicate the amount of evolutionary change or time
• Homologous characters are traits inherited from a common ancestor and used to infer phylogenetic relationships
  • Orthologous genes are homologs that diverged due to speciation events
  • Paralogous genes are homologs that diverged due to gene duplication events
• Convergent evolution occurs when similar traits evolve independently in different lineages due to similar selective pressures (wings in birds and bats)
• Ancestral character states can be inferred using parsimony, likelihood, or Bayesian methods
• Outgroup rooting is used to determine the directionality of evolution in a phylogenetic tree by comparing the ingroup taxa to a more distantly related outgroup

DNA Sequence Analysis Techniques

• DNA sequencing technologies (Sanger, next-generation sequencing) generate nucleotide sequences for phylogenetic analysis
• Multiple sequence alignment is the process of arranging homologous sequences to identify conserved and variable regions
  • Progressive alignment methods (ClustalW) build the alignment incrementally
  • Iterative refinement methods (MUSCLE, MAFFT) improve the alignment by repeatedly dividing and realigning subsets of sequences
• Pairwise sequence alignment algorithms (Needleman-Wunsch, Smith-Waterman) optimize the alignment of two sequences using dynamic programming
• Substitution matrices (PAM, BLOSUM) assign scores to amino acid or nucleotide substitutions based on their evolutionary likelihood
• Sequence similarity searches (BLAST) identify homologous sequences in databases by comparing query sequences to reference sequences
• Phylogenetic signal is the degree to which the evolutionary relationships among sequences are reflected in their similarities and differences
• Sequence saturation occurs when multiple substitutions at the same site obscure the true evolutionary distance between sequences

Evolutionary Models and Algorithms

• Evolutionary models describe the process of sequence evolution and are used to estimate evolutionary parameters and tree topologies
• Nucleotide substitution models (Jukes-Cantor, Kimura 2-parameter, GTR) specify the rates of different types of nucleotide changes
  • Transition-transversion bias accounts for the higher frequency of transitions (A↔G, C↔T) compared to transversions
  • Among-site rate variation allows different sites in a sequence to evolve at different rates (gamma distribution, invariant sites)
• Amino acid substitution models (Dayhoff, JTT, WAG) describe the rates of amino acid replacements based on empirical protein data
• Maximum parsimony (MP) infers the phylogenetic tree that requires the fewest evolutionary changes to explain the observed data
• Maximum likelihood (ML) estimates the tree and model parameters that maximize the probability of observing the data given the model
  • Log-likelihood scores quantify the fit of the model to the data
  • Likelihood ratio tests compare the goodness-of-fit of nested models
• Bayesian inference (BI) incorporates prior knowledge and calculates the posterior probability distribution of trees and parameters
  • Markov chain Monte Carlo (MCMC) algorithms sample from the posterior distribution to estimate tree probabilities and parameter values

Phylogenetic Tree Construction Methods

• Distance-based methods (UPGMA, neighbor-joining) construct trees based on pairwise evolutionary distances between sequences
  • UPGMA assumes a constant rate of evolution and produces rooted trees
  • Neighbor-joining allows varying rates of evolution and produces unrooted trees
• Character-based methods (maximum parsimony, maximum likelihood, Bayesian inference) use the individual character states to infer the optimal tree
• Bootstrapping assesses the statistical support for tree branches by resampling the original data with replacement and constructing multiple trees
• Consensus trees summarize the common branching patterns among a set of trees (strict consensus, majority-rule consensus)
• Supertree methods combine phylogenetic information from multiple source trees to build a comprehensive tree
• Tree rearrangement algorithms (nearest-neighbor interchange, subtree pruning and regrafting) explore the tree space to find the optimal topology

Computational Tools and Software

• Sequence alignment software (ClustalW, MUSCLE, MAFFT) aligns multiple sequences and prepares them for phylogenetic analysis
• Phylogenetic inference programs (PHYLIP, PAUP*, RAxML, MrBayes) implement various tree construction methods and evolutionary models
  • PHYLIP is a pioneering package that includes parsimony, distance, and likelihood methods
  • PAUP* is a comprehensive software for parsimony and likelihood analyses
  • RAxML is a fast and accurate program for maximum likelihood inference of large datasets
  • MrBayes performs Bayesian phylogenetic inference using MCMC sampling
• Tree visualization and editing tools (FigTree, TreeView, iTOL) display and manipulate phylogenetic trees
• Sequence databases (GenBank, ENA, DDBJ) store and provide access to nucleotide and protein sequences
• Genome browsers (UCSC Genome Browser, Ensembl) allow the visualization and analysis of genomic data in a phylogenetic context
• Workflow management systems (Galaxy, Taverna) facilitate the integration and automation of phylogenetic analysis pipelines

Applications in Comparative Genomics

• Phylogenomics uses genome-scale data to infer evolutionary relationships and understand the evolution of genomes
• Genome evolution can be studied by comparing gene content, order, and structure across species
  • Synteny analysis examines the conservation of gene order and orientation between genomes
  • Genome rearrangements (inversions, translocations, fusions, fissions) can be reconstructed using parsimony or likelihood-based methods
• Molecular clock analysis estimates the timing of evolutionary events by assuming a constant rate of molecular evolution
  • Relaxed molecular clocks allow the evolutionary rate to vary among lineages
  • Fossil calibrations provide temporal constraints for molecular clock estimates
• Ancestral genome reconstruction infers the gene content and organization of ancestral genomes based on the comparative analysis of extant genomes
• Phylogeography combines phylogenetic information with geographical distributions to study the evolutionary history of populations and species
• Phylogenetic profiling identifies functionally related genes by comparing their presence or absence across multiple genomes

Challenges and Future Directions

• Massive genomic datasets pose computational challenges for phylogenetic inference and require efficient algorithms and high-performance computing
• Incomplete lineage sorting and gene tree-species tree discordance can lead to conflicting phylogenetic signals and require reconciliation methods
• Horizontal gene transfer and hybridization events complicate the reconstruction of evolutionary histories and may require network-based approaches
• Integration of different data types (molecular, morphological, ecological) can provide a more comprehensive understanding of evolution
• Development of more realistic and complex evolutionary models that capture the intricacies of molecular evolution
  • Incorporation of selection, recombination, and population dynamics into phylogenetic models
  • Accounting for heterogeneous evolutionary processes across sites, genes, and lineages
• Improved methods for visualizing and interpreting large-scale phylogenetic results, facilitating the exploration of complex evolutionary patterns
• Application of machine learning techniques to enhance the accuracy and efficiency of phylogenetic inference and downstream analyses