7.4 Applications of phylogenetics in computational biology
4 min read•august 14, 2024
Phylogenetics is a powerful tool in computational biology, helping us understand how organisms and genes are related. It's used in many areas, from tracking disease outbreaks to studying biodiversity and developing new drugs.
By looking at the similarities in DNA or physical traits, scientists can build family trees for species or genes. This helps them figure out how things evolved and how different biological systems work together.
Phylogenetics in Computational Biology
Applications of Phylogenetics
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Phylogenetics studies the evolutionary relationships among organisms, genes, or other biological entities based on their molecular or morphological similarities
Phylogenetic methods infer the evolutionary history and construct phylogenetic trees depicting the branching patterns and divergence times of the studied entities
uses phylogenetics to identify conserved regions, detect selection pressures, and reconstruct ancestral sequences (ancient gene variants)
employs phylogenetic approaches to trace the origin and spread of pathogens (SARS-CoV-2), investigate disease outbreaks, and develop targeted interventions
Biodiversity studies apply phylogenetics to assess species richness, understand evolutionary processes shaping communities (adaptive radiations), and prioritize conservation efforts
Drug discovery and development utilize phylogenetic methods to identify potential drug targets, predict drug resistance, and guide the selection of model organisms for testing (zebrafish)
Phylogenetic Diversity and Community Analysis
measures, such as Faith's phylogenetic diversity and UniFrac distances, quantify the evolutionary distinctiveness and relatedness of microbial communities
These measures help compare the diversity and structure of microbial communities across different environments (human gut, soil, marine ecosystems)
Phylogenetic approaches uncover the functional potential and evolutionary adaptations of microbial communities in various ecological niches
Integrating phylogenetic information with environmental factors and community dynamics provides insights into the assembly and functioning of microbial ecosystems
Phylogenetic Methods for Gene Evolution
Gene Family Evolution
Gene families are groups of genes that have evolved from a common ancestral gene through duplication, speciation, and divergence events
of gene families distinguishes between orthologous and , providing insights into the evolutionary history and functional divergence of genes
Orthologous genes are homologous genes that have diverged due to a speciation event (human and mouse insulin genes)
Paralogous genes have diverged due to a duplication event within a species (human alpha and beta hemoglobin genes)
Studying using phylogenetics enables the identification of lineage-specific gene expansions, contractions, and adaptations (olfactory receptor genes in mammals)
Orthology and Comparative Genomics
compare gene trees with species trees to infer gene duplication, loss, and horizontal transfer events
Orthology relationships inferred from phylogenetic analysis are crucial for functional annotation, comparative genomics, and evolutionary studies across species
Comparative genomics relies on accurate orthology assignments to identify conserved functional elements (transcription factor binding sites) and study the evolution of gene regulation
, which compares the presence or absence of orthologous genes across species, can reveal co-evolving gene sets and predict functional associations (enzymes in a metabolic pathway)
Phylogenetic Analysis of Microbial Communities
Metagenomics and Microbial Diversity
involves the sequencing and analysis of DNA from environmental samples, enabling the study of microbial communities without the need for cultivation
Phylogenetic methods are applied to metagenomics data to assess the taxonomic composition and diversity of microbial communities
Marker gene-based approaches, such as , construct phylogenetic trees and compare microbial community structures across samples (healthy vs. diseased gut microbiomes)
allows for the reconstruction of draft genomes and the identification of novel microbial lineages using phylogenetic placement methods
Microbial Community Structure and Function
Phylogenetic approaches reveal the taxonomic composition and evolutionary relationships within microbial communities
Comparing the phylogenetic structure of microbial communities across different environments (soil, water, host-associated) provides insights into the factors shaping community assembly and function
Phylogenetic diversity metrics quantify the evolutionary distinctiveness and relatedness of microbes within a community, serving as indicators of community stability and resilience
Integrating phylogenetic information with functional annotations and metabolic capabilities helps elucidate the functional roles and interactions of microbes in complex ecosystems (nutrient cycling, host-microbe symbiosis)
Phylogenetic Integration with Omics Data
Multi-Omics Integration
Phylogenetic information can be combined with other omics data, such as transcriptomics, proteomics, and metabolomics, to gain a systems-level understanding of biological processes
Phylogenetic trees serve as a framework for integrating and visualizing multi-omics data, allowing for the identification of evolutionary patterns and co-evolution of molecular components
study the evolution of gene expression, protein abundance, and metabolic pathways across species (brain gene expression in primates)
techniques infer the evolutionary history of molecular traits and identify key evolutionary events associated with functional changes (evolution of photosynthetic pathways)
Evolutionary Systems Biology
Integrating phylogenetics with other omics data enables the identification of evolutionarily conserved or divergent molecular mechanisms, providing insights into the functional diversification and adaptation of biological systems
Phylogenetic information guides the selection of representative species or strains for multi-omics experiments, ensuring a broad evolutionary coverage and maximizing the information gained
approaches combine phylogenetics, omics data, and computational modeling to unravel the evolutionary dynamics of complex biological networks (gene regulatory networks, signaling pathways)
Comparative analyses of omics data across species reveal the evolutionary conservation and divergence of molecular processes, helping to identify functionally important elements and evolutionary innovations (mammalian brain evolution)