Evolutionary Tree Analysis Service
Overview
Evolutionary tree analysis is a specialized analytical approach that constructs and interprets evolutionary trees (phylogenies) to elucidate the evolutionary relationships among species, populations, or genes. By analyzing genetic sequences, morphological traits, or other molecular markers, this method reconstructs the branching patterns of evolutionary history, estimates divergence times, and infers the mechanisms driving lineage diversification. It enables researchers to trace the ancestral connections between organisms, identify key evolutionary transitions, and understand how ecological, genetic, and geographical factors have shaped biodiversity over time. Key advantages include the ability to resolve complex evolutionary scenarios, the integration of diverse data types (e.g., DNA, RNA, proteins), and the capacity to test hypotheses about evolutionary processes (e.g., speciation, adaptation, hybridization).
Our Evolutionary Tree Analysis Service empowers your research with:
- Accurate Phylogenetic Reconstruction: Leverage advanced algorithms/models to construct precise evolutionary trees.
- Multidisciplinary Data Integration: Merge genetic, morphological, ecological, and geological data, including multi-omics.
- Customizable Analytical Pipelines: Craft tailored workflows for unique research.
- Divergence Time Estimation: Use sophisticated models to time evolutionary events
What Is Evolutionary Tree Analysis
This specialized bioinformatics solution clarifies evolutionary relationships among species, genes, or populations. Evolutionary Tree Analysis utilizes advanced computational methods and various molecular datasets. It reconstructs the phylogenetic tree that shows the historical branch pattern. Skilled analysts apply algorithms to estimate divergence times, identify key evolutionary transitions, and infer drivers of lineage diversification. Researchers use this service to address the uncertainties in classification. This method can trace the characteristics of ancestors and reveal how ecological, genetic or geographical factors shape biodiversity. Whether studying speciation in wild organisms, pathogen evolution, or genetic innovations in domesticated species, it provides actionable insights into evolutionary history. Harness phylogenetics to refine hypotheses, integrate multi-omics data, and deepen understanding of life's evolutionary story.
How to Measure
1. Sample Collection and Preparation
Sample Type Selection：Human（Blood, saliva, buccal swabs, formalin-fixed tissues）;
Plants/Animals（Leaves, seeds, hair follicles, muscle/liver biopsies）; Microorganisms Environmental metagenomic samples (soil, water), cultured isolates
- Genotyping/Sequencing Technologies：
Table 1: Comparative genotyping techniques
| Technology | Application Scenario | Key Advantages |
| SNP Arrays (e.g., Axiom) | Rapid population screens, cost-effective projects | High-throughput processing, low per-sample cost, established validation |
| RAD-seq | Non-model organisms, low-budget studies | Reduced genome representation, unbiased locus sampling, no reference genome required |
| WGS | Deep ancestry inference, rare variant detection | Comprehensive genomic coverage, no ascertainment bias, ideal for evolutionary studies |
| Pool-seq | Large population studies, pooled samples | Cost-effective allele frequency estimation, reduced individual genotyping needs |
- Data Quality Control (QC)
- Sample-level QC: Remove duplicate samples; Exclude samples with low DNA concentration (<10 ng/μL); Identify and discard contaminated samples using control checks
- Data-level QC:
Filter SNPs with: Missingness >20%; Hardy-Weinberg equilibrium p-value < 1×10⁻⁶ Minor allele frequency (MAF) <1%
Implement batch effect correction when applicable
2. Statistical Analysis Workflow
Dimensionality Reduction
- Principal Component Analysis (PCA):
Tools: PLINK, smartpca, EIGENSOFT
Application: Visualize population structure and ancestry clusters
Output: Eigenvectors representing principal genetic components
- ADMIXTURE Analysis:
Objective: Estimate individual ancestry proportions
Parameters: Test K=1-10 clusters (or broader range for complex populations)
Validation: Cross-validation to determine optimal K value
Admixture/Migration Inference
- f3-statistics:
Purpose: Detect historical gene flow between populations
Interpretation: Negative f3 values indicate admixture
Tools: ThreePop (TreeMix package), ADMIXTOOLS
- TreeMix Analysis:
Objective: Model population splits and migration events
Parameters: Test 0-5 migration edges
Validation: Compare residual fit to determine optimal model
Selection Sweep Detection
- XP-EHH (Cross-Population Extended Haplotype Homozygosity):
Application: Identify regions under recent positive selection
Implementation: Use selscan software with proper phase-switching correction
- FST Outlier Analysis:
Method: Identify loci with extreme differentiation between populations
Tools: BayeScan, LOSITAN
Threshold: Loci with FST > 0.15 (moderate differentiation) or >0.25 (strong differentiation)
3. Visualization and Reporting
Recommended Visualization Tools
- PCA Plots:
Implementation: ggplot2 (R), matplotlib (Python)
Best Practices: Color-code by population/cluster, include percentage variance explained
- Admixture Bar Plots:
Features: Proportional ancestry visualization per individual
Enhancements: Add population labels, error bars for K uncertainty
- Geographic Maps:
Tools: Leaflet (interactive), ggplot2 (static)
Integration: Overlay genetic clusters with sampling locations
Advanced: Include migration edges from TreeMix analysis
Report Components
- Executive Summary:
Key findings and evolutionary relationships
Summary statistics (sample sizes, QC metrics)
Figure 1: Evolutionary Tree Analysis
What Can We do
Data Acquisition & Preprocessing
- Extensive Data Sourcing: Access genomic, morphological, and ecological data from public repositories (e.g., GenBank, Dryad) and private collaborations, spanning diverse species from microbes to macroorganisms.
- Stringent Quality Control: Implement rigorous data filtering to remove errors, correct sequences, and ensure compatibility with phylogenetic analyses, guaranteeing high-quality inputs for tree reconstruction.
Phylogenetic Tree Construction
- Advanced Statistical Modeling: Use algorithms like Maximum Likelihood (ML), Bayesian Inference (BI), and Neighbor-Joining (NJ) to reconstruct robust evolutionary trees, accounting for model misspecification and uncertainty.
- Tree Validation & Optimization: Assess tree topology with bootstrap support, posterior probabilities, and model comparison tests (e.g., AIC, BIC) to ensure reliability and accuracy.
Divergence Timing & Pacing
- Molecular Clock Calibration: Employ relaxed-clock models and fossil calibrations to estimate divergence times, placing evolutionary events in a geological and ecological context.
- Rate Heterogeneity Analysis: Account for lineage-specific evolutionary rates to improve temporal accuracy and understand evolutionary tempo.
Hypothesis Testing & Validation
- Phylogenetic Comparative Methods: Test hypotheses about trait evolution, adaptation, or coevolution using methods like ancestral state reconstruction, trait-dependent diversification, and phylogenetic signal analysis.
- Model Selection & Robustness Checks: Use Bayesian model averaging, likelihood ratio tests, and cross-validation to validate evolutionary hypotheses and ensure robustness.
Customized Analysis & Reporting
- Tailored Workflows: Design analytical pipelines to address specific questions (e.g., host-parasite coevolution, crop domestication, or conservation genetics).
- Interactive Visualization & Reporting: Generate clear, publication-ready figures (e.g., chronograms, traitgrams) and detailed reports explaining methods, results, and implications, with recommendations for follow-up studies.
- Ongoing Support & Collaboration: Provide post-analysis support, additional analyses, and collaborative opportunities to help you refine interpretations and expand your research impact.
Our Advantages
- Data Versatility: Process a wide range of data inputs, including genetic sequences (DNA, RNA, proteins), morphological traits, and ecological metadata, accommodating diverse evolutionary study designs.
- Pipeline Customization: Tailor analytical workflows to your specific research objectives, whether reconstructing deep evolutionary histories, investigating recent speciation events, or testing hypotheses about adaptive evolution.
- Efficient Automation: Leverage automated, validated pipelines to accelerate analysis while minimizing human error, ensuring reproducibility and consistency across projects.
- Timely Delivery: Prioritize rapid turnaround without sacrificing analytical rigor, meeting urgent deadlines for grant proposals, conference presentations, or manuscript submissions.
- Expert Support: Benefit from bioinformaticians and evolutionary biologists who guide study design, troubleshoot challenges, and interpret results in the context of your research question.
Applications
Human Health & Precision Medicine
Evolutionary Disease Mechanisms: Reconstruct evolutionary trees to trace the origins and spread of pathogens (e.g., SARS-CoV-2 variants) or disease-associated genes (e.g., lactase persistence in humans), aiding pandemic tracking and genetic risk prediction.
Pharmacogenomic Evolution: Analyze evolutionary relationships of drug-metabolizing enzymes across populations to identify variants influencing drug response.
Agricultural & Plant Breeding
Climate-Resilient Crops: Use phylogenetic analysis to identify ancestral traits (e.g., cold tolerance in wild wheat relatives) for breeding programs, accelerating adaptation to climate change.
Crop Domestication Insights: Reconstruct domestication histories to pinpoint genomic regions under selection during crop evolution, guiding efforts to reintroduce lost traits (e.g., nutrient density in ancient wheat).
Animal Science
Livestock Adaptation: Map evolutionary trajectories of livestock (e.g., heat tolerance in chickens) to breed animals resilient to environmental stressors, enhancing sustainable production.
Wildlife Conservation: Analyze evolutionary trees of endangered species to identify genetic diversity hotspots and adaptive signatures, informing captive breeding or reintroduction programs.
Evolutionary Biology & Fundamental Research
Macroevolutionary Patterns: Test hypotheses about deep-time evolutionary trends (e.g., body size evolution in dinosaurs) using divergence time estimation and lineage diversification analyses.
Convergent Evolution: Identify independent evolutionary solutions to similar challenges (e.g., echolocation in bats and dolphins) via phylogenetic comparisons, uncovering universal adaptations.
Demo
Figure 2: Detailed information of Liberibacter species whose genomes were investigated in this study, including their evolutionary relationship, transmitted vectors, hosts, and associated diseases. The evolutionary relationship was derived from phylogenetic trees of 16S rRNA (Tan, 2021)
Case Study
Evolutionary analysis of human respiratory syncytial virus collected in Myanmar between 2015 and 2018
Journal：Infect Genet Evol
Published：2021
This study explored genetic variation in the second hypervariable region (HVR) of the G gene of human respiratory syncytial virus (HRSV) from 1701 nasal swab samples collected from outpatients with acute respiratory infections at two general hospitals in the cities Yangon and Pyinmana in Myanmar from 2015 to 2018.
HRSV genotypes were characterized using phylogenetic trees constructed using the maximum likelihood method. Time-scale phylogenetic tree analyses were performed using the Bayesian Markov chain Monte Carlo method.
In total, 244 (14.3%) samples were HRSV-positive and were classified as HRSV-A (n = 84, 34.4%), HRSV-B (n = 158, 64.8%), and co-detection of HRSV-A/HRSV-B (n = 2, 0.8%). HRSV epidemics occurred seasonally between July (1.9%, 15/785) and August (10.5%, 108/1028), with peak infections in September (35.8%, 149/416) and October (58.2%, 89/153). HRSV infection rate was higher in children ≥1 year of age than in those <1 year of age (70.5% vs. 29.5%). The most common HRSV symptoms in children were cough (80%–90%) and rhinorrhea (70%–100%). The predominant genotypes were ON1for HRSV-A (78%) and BA9 for HRSV-B (64%). Time to the most recent common ancestor was 2014 (95% highest posterior density [HPD], 2012–2015) for HRSV-A ON1 and 2009 (95% HPD, 2004–2012) for HRSV-B BA9. The mean evolutionary rate (substitutions/site/year) for HRSV-B (2.12 × 10−2, 95% HPD, 8.53 × 10−3–3.63 × 10−2) was slightly higher than that for HRSV-A (1.39 × 10−2, 95% HPD, 6.03 × 10−3–2.12 × 10−2).
Figure 2: Time scale phylogenetic tree of the human respiratory syncytial virus (HRSV)-A ON1 G gene hypervariable region generated using the Bayesian Markov chain Monte Carlo method. HRSV-A strains from Myanmar are shown on the right. The years of divergence are indicated as 95% highest probability density (95% HPD) in the horizontal light blue boxes at each branch point. The scale bar represents the unit of time (years).
FAQs
Yes! Our team provides post-analysis support to help you interpret evolutionary patterns, test hypotheses, and contextualize findings within your field of study.
Our service supports:
- Molecular data: DNA/RNA sequences (e.g., mitochondrial genes, whole genomes).
- Morphological traits: Physical characteristics (e.g., skeletal structures, behavioral traits).
- Ecological/geological data: Environmental variables, fossil records, or biogeographic distributions.
- Multi-omics integration: Combining genomics, transcriptomics, or proteomics for deeper insights.
References
- Tan Y, Wang C, Schneider T, et al. Comparative Phylogenomic Analysis Reveals Evolutionary Genomic Changes and Novel Toxin Families in Endophytic Liberibacter Pathogens. Microbiol Spectr. 2021 Oct 31;9(2):e0050921. https://doi.org/10.1128/Spectrum.00509-21
- Phyu WW, Htwe KTZ, Saito R, Kyaw Y, Lin N, Dapat C, Osada H, Chon I, Win SMK, Hibino A, Wagatsuma K, Kyaw LL, Tin HH, et al. Evolutionary analysis of human respiratory syncytial virus collected in Myanmar between 2015 and 2018. Infect Genet Evol. 2021 Sep;93:104927. https://doi.org/10.1016/j.meegid.2021.104927
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