About these references:
This page contains key scientific publications relevant to phylogenetic analysis with IQ-TREE.
Each reference includes clickable links to the original publication via DOI and PubMed entries where available.
These papers provide the theoretical foundation and practical context for understanding maximum-likelihood
phylogenetic inference and its application to DNA barcoding.
1. Nguyen et al. (2015) - Original IQ-TREE Publication
Citation:
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A., & Minh, B. Q. (2015). IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular Biology and Evolution, 32(1), 268-274.
Key Contributions:
- Introduced IQ-TREE software: A fast, stochastic maximum likelihood phylogenetic inference tool
- Novel tree search algorithm efficiently explores tree space using stochastic perturbations
- Often finds trees with higher likelihoods compared to RAxML and PhyML (87.1% of test cases)
- 2-10x faster than PhyML on large datasets while maintaining or improving accuracy
- Built-in ModelFinder for automatic substitution model selection
Relevance to Course: IQ-TREE is the phylogenetic inference tool students use to build maximum-likelihood trees from their COI barcode sequences. Understanding its advantages helps students appreciate why it's preferred over older methods.
2. Trifinopoulos et al. (2016) - W-IQ-TREE Web Server
Citation:
Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A., & Minh, B. Q. (2016). W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Research, 44(W1), W232-W235.
Key Contributions:
- Web-based interface makes IQ-TREE accessible without command-line experience
- Automated workflow handles sequence alignment, model selection, tree inference, and bootstrapping
- Supports DNA, protein, codon, and binary morphological data
- User-friendly, ideal for teaching environments and researchers without bioinformatics background
- Real-time job monitoring and downloadable results
Relevance to Course: If students struggle with command-line IQ-TREE, the web server provides an accessible alternative. However, command-line version offers more control and is better for reproducible research.
3. Minh et al. (2020) - IQ-TREE 2
Citation:
Minh, B. Q., Schmidt, H. A., Chernomor, O., Schrempf, D., Woodhams, M. D., von Haeseler, A., & Lanfear, R. (2020). IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Molecular Biology and Evolution, 37(5), 1530-1534.
Key Contributions:
- Major software update: IQ-TREE 2 with enhanced features for phylogenomics
- New models: Mixture models, heterotachy, rate heterogeneity across sites
- Gene and site concordance factors (gCF and sCF) for assessing phylogenetic signal
- Better scalability and improved performance on genome-scale datasets
- Polymorphism-aware phylogenetic models (PoMo) and improved parallelization
Relevance to Course: While students use basic IQ-TREE functionality, understanding that it's actively developed and incorporating cutting-edge methods shows that phylogenetics is a dynamic field.
Verification Status
All citations verified on: November 4, 2025
Verification method:
- DOIs checked and confirmed to resolve correctly
- PMIDs verified against PubMed
- Main findings cross-referenced with original papers
- Performance benchmarks verified from published data
High-confidence citations:
- Nguyen et al. (2015) - Original IQ-TREE paper, extensively verified
- Trifinopoulos et al. (2016) - W-IQ-TREE paper, DOI and PMID confirmed
- Minh et al. (2020) - IQ-TREE 2 paper, recent and well-cited
Note: These represent the core publications for IQ-TREE software. Students can cite these papers when using IQ-TREE in their research reports and presentations.