IQ-TREE

IQ-TREE reconstructs maximum-likelihood phylogenetic trees and assesses branch support using the ultrafast bootstrap approximation and the IQPNNI (Iterative Quartet PNNI) algorithm for evolutionary inference.

Key Features:

• IQPNNI algorithm: Implements IQPNNI (Iterative Quartet PNNI) to accelerate tree search, providing approximately a 10-fold speedup over traditional methods.
• Supported data types: Handles DNA, protein, codon, binary, and morphological data and accepts common alignment formats.
• Evolutionary models: Supports an extensive range of evolutionary models, including mixture models and partition models.
• Model selection and partitioning: Performs rapid model selection and partition scheme identification.
• Tree reconstruction: Conducts efficient maximum-likelihood tree reconstruction.
• Branch support: Assesses branch support using the ultrafast bootstrap approximation.
• Statistical tests: Performs branch tests and tree topology tests.

Scientific Applications:

• Maximum-likelihood phylogenetic inference: Reconstructing evolutionary relationships using maximum-likelihood methods.
• Model selection and partitioning: Selecting substitution models and partition schemes to optimize phylogenetic reconstructions.
• Branch support assessment: Quantifying statistical support for tree branches via ultrafast bootstrap.
• Topology and hypothesis testing: Testing alternative phylogenetic hypotheses and tree topologies.
• Multi-data analyses: Analyzing DNA, protein, codon, binary, and morphological datasets in phylogenetic studies.
• Large-scale phylogenetic analyses: Enabling complex and large-scale phylogenetic analyses through accelerated tree search.
• Evolutionary biology and systematics: Supporting research in evolutionary biology, systematics, and related fields.

Methodology:

Implements the IQPNNI (Iterative Quartet PNNI) algorithm for accelerated maximum-likelihood tree search; uses the ultrafast bootstrap approximation for branch support; supports mixture and partition models; provides rapid model selection and partition scheme identification; and performs branch tests and tree topology tests.