6.1 Molecular evolution

Molecular evolution examines changes in DNA, RNA, and proteins over time, providing insights into evolutionary processes at the molecular level. This field forms the foundation for various bioinformatics tools and algorithms used in sequence analysis and phylogenetics.

Understanding molecular evolution principles enhances bioinformatics analyses by enabling accurate interpretation of genetic data and evolutionary relationships. Key concepts include genetic variation sources, the molecular clock hypothesis, and the neutral theory of evolution.

Fundamentals of molecular evolution

• Molecular evolution examines changes in DNA, RNA, and proteins over time, providing insights into evolutionary processes at the molecular level
• Understanding molecular evolution principles enhances bioinformatics analyses by enabling accurate interpretation of genetic data and evolutionary relationships
• Molecular evolution concepts form the foundation for various bioinformatics tools and algorithms used in sequence analysis and phylogenetics

Genetic variation sources

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• Mutations introduce new genetic variants through changes in DNA sequences
  • Point mutations alter single nucleotides (transitions, transversions)
  • Insertions and deletions modify the length of genetic sequences
• Recombination shuffles existing genetic material during meiosis
  • Crossing over exchanges segments between homologous chromosomes
  • Independent assortment randomly distributes chromosomes to gametes
• Gene flow transfers genetic variation between populations through migration
• Horizontal gene transfer moves genetic material between different species (prokaryotes)

Molecular clock hypothesis

• Proposes that genetic changes accumulate at a relatively constant rate over time
• Assumes neutral mutations occur at a steady pace, independent of natural selection
• Enables estimation of divergence times between species based on genetic differences
• Calibration requires fossil evidence or other known divergence times
• Limitations include rate variation among lineages and genes

Neutral theory of evolution

• Postulates that most genetic changes are selectively neutral and do not affect fitness
• Random genetic drift drives the fixation of neutral mutations in populations
• Explains the observed high levels of genetic polymorphism within species
• Predicts that the rate of molecular evolution is approximately constant
• Serves as a null hypothesis for detecting natural selection in molecular evolution studies

Evolutionary rates

• Evolutionary rates measure the speed at which genetic changes accumulate over time
• Understanding evolutionary rates helps bioinformaticians interpret sequence divergence and estimate divergence times
• Variation in evolutionary rates among genes and lineages impacts phylogenetic analyses and molecular clock applications

Synonymous vs nonsynonymous changes

• Synonymous changes alter the DNA sequence without changing the encoded amino acid
  • Often occur in the third position of codons due to genetic code redundancy
  • Generally considered neutral and subject to less selective pressure
• Nonsynonymous changes result in amino acid substitutions in the protein sequence
  • Can affect protein structure and function
  • More likely to be subject to natural selection (positive or negative)
• Comparing synonymous and nonsynonymous rates helps infer selection pressures on genes

Selection pressures on genes

• Positive selection favors advantageous mutations, increasing their frequency in the population
  • Results in higher nonsynonymous substitution rates
  • Often observed in genes involved in immunity or sensory perception
• Negative (purifying) selection removes deleterious mutations from the population
  • Leads to lower nonsynonymous substitution rates
  • Common in essential genes with conserved functions
• Balancing selection maintains multiple alleles in the population
  • Can result from heterozygote advantage or frequency-dependent selection
• Relaxed selection occurs when functional constraints on a gene are reduced

Codon usage bias

• Refers to the unequal usage of synonymous codons in protein-coding genes
• Influenced by factors such as tRNA abundance, translation efficiency, and GC content
• Varies among species and even between genes within a genome
• Can affect gene expression levels and protein folding
• Bioinformatics tools analyze codon usage patterns to identify highly expressed genes or foreign DNA

Phylogenetic analysis

• Phylogenetic analysis reconstructs evolutionary relationships between organisms or genes
• Crucial for understanding species evolution, gene family histories, and comparative genomics
• Bioinformatics applies various methods to infer phylogenies from molecular sequence data

Distance-based methods

• Calculate pairwise distances between sequences to construct phylogenetic trees
• Neighbor-joining algorithm creates trees by iteratively joining closest sequence pairs
  • Computationally efficient and suitable for large datasets
  • Does not always find the optimal tree topology
• UPGMA (Unweighted Pair Group Method with Arithmetic Mean) assumes a constant evolutionary rate
  • Produces ultrametric trees with equal branch lengths from root to tips
  • Less accurate for datasets with varying evolutionary rates
• Advantages include speed and ability to handle large datasets

Maximum parsimony

• Seeks the tree topology requiring the fewest evolutionary changes to explain the observed data
• Assumes that the simplest explanation for the data is the most likely
• Identifies informative sites in the sequence alignment to infer relationships
• Can handle both nucleotide and amino acid sequences
• Disadvantages include long computation times for large datasets and susceptibility to long-branch attraction

Maximum likelihood

• Estimates the probability of observing the given sequence data under a specific evolutionary model
• Searches for the tree topology and branch lengths that maximize this likelihood
• Incorporates complex models of sequence evolution
  • Allows for different substitution rates among sites
  • Can account for rate variation among lineages
• Computationally intensive but generally produces accurate results
• Provides statistical framework for hypothesis testing and model comparison

Sequence alignment in evolution

• Sequence alignment identifies homologous positions between related DNA or protein sequences
• Critical for accurate phylogenetic analysis and evolutionary rate estimation
• Bioinformatics tools employ various algorithms to optimize alignment quality and speed

Pairwise alignment techniques

• Global alignment aligns entire sequences from end to end
  • Needleman-Wunsch algorithm guarantees optimal global alignment
  • Suitable for closely related sequences of similar length
• Local alignment identifies regions of high similarity within sequences
  • Smith-Waterman algorithm finds optimal local alignments
  • Useful for detecting conserved domains or motifs
• Scoring matrices (PAM, BLOSUM) quantify the likelihood of substitutions between residues
• Gap penalties account for insertions and deletions in the evolutionary process

Multiple sequence alignment

• Aligns three or more sequences simultaneously to identify conserved regions
• Progressive alignment methods build the alignment incrementally
  • ClustalW aligns most similar sequences first, then adds more distant ones
  • T-Coffee improves alignment quality by considering global and local information
• Iterative methods refine alignments through multiple rounds of optimization
  • MUSCLE uses fast distance estimation and progressive alignment stages
  • MAFFT employs fast Fourier transform for rapid homology detection
• Consistency-based methods (PROBCONS) improve accuracy by considering all pairwise alignments

Profile hidden Markov models

• Statistical models representing sequence families or protein domains
• Capture position-specific information about conserved and variable regions
• Used for sensitive sequence searches and multiple sequence alignment
• HMMER software package implements profile HMM algorithms for bioinformatics applications
• Advantages include ability to detect remote homologs and handle insertions/deletions effectively

Detecting natural selection

• Identifying signatures of natural selection in molecular sequences reveals evolutionary forces
• Bioinformatics methods analyze patterns of genetic variation to infer selection pressures
• Understanding selection helps interpret gene function and adaptation processes

Ka/Ks ratio analysis

• Compares the rate of nonsynonymous substitutions (Ka) to synonymous substitutions (Ks)
• Ka/Ks ratio < 1 indicates purifying selection
• Ka/Ks ratio > 1 suggests positive selection
• Ka/Ks ratio ≈ 1 implies neutral evolution
• Sliding window analysis detects localized selection within genes
• Limitations include averaging effects and inability to detect certain types of selection

McDonald-Kreitman test

• Compares the ratio of nonsynonymous to synonymous changes within and between species
• Utilizes both polymorphism and divergence data
• Neutrality Index (NI) quantifies the deviation from neutral expectations
  • NI > 1 suggests purifying selection
  • NI < 1 indicates positive selection
• Advantages include robustness to demographic effects and ability to detect selection on entire genes

Tajima's D statistic

• Compares the number of segregating sites to the average number of pairwise differences
• Negative Tajima's D suggests recent selective sweep or population expansion
• Positive Tajima's D indicates balancing selection or population subdivision
• Calculated using the formula: $D = \frac{\pi - \theta_W}{\sqrt{Var(\pi - \theta_W)}}$
• Sensitive to demographic changes, requiring careful interpretation of results

Molecular evolution models

• Mathematical models describe the process of nucleotide or amino acid substitution over time
• Essential for accurate phylogenetic inference and evolutionary rate estimation
• Bioinformatics software implements various models to account for different evolutionary scenarios

Jukes-Cantor model

• Simplest model of nucleotide substitution
• Assumes equal base frequencies and equal substitution rates between all nucleotides
• Single parameter (α) represents the overall substitution rate
• Probability of observing a difference between two sequences after time t: $P(t) = \frac{3}{4}(1 - e^{-\frac{4\alpha t}{3}})$
• Limitations include unrealistic assumptions for most real-world scenarios

Kimura two-parameter model

• Extends Jukes-Cantor model by distinguishing between transitions and transversions
• Assumes equal base frequencies but different rates for transitions (α) and transversions (β)
• Accounts for the observed higher frequency of transitions in real sequences
• Probability of observing a transition after time t: $P_{transition}(t) = \frac{1}{4}(1 - e^{-4\beta t}) + \frac{1}{4}(1 - e^{-2(\alpha + \beta)t})$
• More realistic than Jukes-Cantor but still simplifies some aspects of evolution

General time-reversible model

• Most complex and flexible model of nucleotide substitution
• Allows for unequal base frequencies and different rates for all possible substitutions
• Six substitution rate parameters and three base frequency parameters
• Time-reversible assumption simplifies calculations while maintaining flexibility
• Widely used in phylogenetic analyses due to its ability to fit diverse datasets

Comparative genomics

• Analyzes and compares genome sequences from different species to understand evolution
• Reveals patterns of gene conservation, loss, and gain across lineages
• Bioinformatics tools enable large-scale genomic comparisons and functional predictions

Orthology vs paralogy

• Orthologs are genes in different species derived from a common ancestral gene
  • Result from speciation events
  • Often maintain similar functions across species
• Paralogs are genes within a species resulting from gene duplication
  • Can diverge in function or acquire new roles
  • Classified as in-paralogs (recent duplications) or out-paralogs (ancient duplications)
• Distinguishing orthologs and paralogs crucial for accurate functional prediction and phylogenetic analysis

Synteny analysis

• Examines the conservation of gene order and content between genomes
• Identifies regions of conserved synteny indicating evolutionary relationships
• Reveals genome rearrangements, duplications, and deletions
• Aids in gene annotation and prediction of gene function
• Tools like SynMap and Genomicus facilitate large-scale synteny analysis

Gene family evolution

• Studies the changes in gene copy number and function within related groups of genes
• Birth-and-death model explains gene family dynamics through duplication and loss events
• Concerted evolution homogenizes gene family members through gene conversion
• Analyses reveal patterns of gene family expansion, contraction, and functional diversification
• Understanding gene family evolution aids in interpreting gene function and adaptation processes

Population genetics concepts

• Population genetics examines genetic variation within and between populations
• Provides theoretical framework for understanding evolutionary processes
• Bioinformatics applies population genetics principles to analyze genomic data

Hardy-Weinberg equilibrium

• Describes the expected genotype frequencies in a non-evolving population
• Assumes random mating, large population size, and absence of selection, mutation, and migration
• Genotype frequencies remain constant from generation to generation under these conditions
• For a biallelic locus with alleles A and a: $p^2 + 2pq + q^2 = 1$ where p and q are the frequencies of A and a, respectively
• Deviations from Hardy-Weinberg equilibrium can indicate evolutionary forces at work

Genetic drift effects

• Random changes in allele frequencies due to sampling error in small populations
• More pronounced in small populations, leading to loss of genetic variation
• Founder effect occurs when a new population is established by a small number of individuals
• Bottleneck effect results from a drastic reduction in population size
• Inbreeding increases homozygosity and can amplify the effects of genetic drift

Coalescent theory basics

• Traces the genealogical history of a sample of genes back to their most recent common ancestor
• Provides a framework for modeling genetic variation in populations
• Assumes neutral evolution and constant population size
• Time to coalescence follows an exponential distribution
• Coalescent simulations generate data under various demographic scenarios for hypothesis testing

Molecular evolution software

• Bioinformatics software packages implement algorithms for analyzing molecular evolution
• Enable researchers to perform complex analyses on large genomic datasets
• Continual development improves accuracy, speed, and user-friendliness of evolutionary analyses

PAML package overview

• Phylogenetic Analysis by Maximum Likelihood
• Implements various models for detecting selection and estimating evolutionary rates
• Includes programs for codon-based analyses (codeml) and DNA/protein analyses (baseml)
• Allows for branch-specific and site-specific tests of selection
• Widely used for detecting positive selection in protein-coding genes

MEGA software capabilities

• Molecular Evolutionary Genetics Analysis
• User-friendly software for conducting evolutionary analyses on sequence data
• Features include sequence alignment, phylogenetic tree construction, and molecular clock analysis
• Implements distance-based, maximum parsimony, and maximum likelihood methods
• Provides tools for calculating evolutionary distances and testing evolutionary hypotheses

MrBayes for phylogenetics

• Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) methods
• Allows for complex models of sequence evolution and rate variation
• Produces a posterior distribution of trees rather than a single best tree
• Enables estimation of branch lengths and divergence times
• Advantages include ability to incorporate prior information and assess uncertainty in tree topology

Applications in bioinformatics

• Molecular evolution principles and methods have diverse applications in bioinformatics
• Enable researchers to extract meaningful biological insights from genomic data
• Continual development of new techniques expands the scope of evolutionary analyses

Ancestral sequence reconstruction

• Infers the sequences of ancestral genes or proteins based on extant sequences
• Uses phylogenetic trees and models of sequence evolution to estimate ancestral states
• Applications include studying protein function evolution and resurrecting ancient proteins
• Methods include maximum parsimony, maximum likelihood, and Bayesian inference
• Challenges include handling uncertainty in ancestral state predictions

Molecular dating techniques

• Estimate divergence times between species or genes using molecular clock approaches
• Relaxed clock models allow for rate variation among lineages
• Bayesian methods (BEAST) incorporate fossil calibrations and uncertainty in date estimates
• Applications include studying speciation events and timing of gene duplications
• Challenges include calibration uncertainty and model selection

Horizontal gene transfer detection

• Identifies genes or genomic regions transferred between distantly related organisms
• Methods include phylogenetic incongruence, abnormal GC content, and codon usage analysis
• Crucial for understanding bacterial evolution and antibiotic resistance spread
• Impacts tree of life reconstruction, especially for prokaryotes
• Bioinformatics tools (HGTector) automate the detection of horizontal gene transfer events