phylogenetic-methods

What problem does it solve?

Researchers often struggle to construct and evaluate phylogenetic trees from sequence data, juggling multiple methods, alignment requirements, and inconsistent outputs. This Skill provides a cohesive framework to implement and compare distance-based, ML, and Bayesian approaches in a reproducible manner.

Core Features & Use Cases

• Distance-based methods (UPGMA, Neighbor-Joining) for rapid exploratory trees from distance matrices.
• Maximum Likelihood & Bayesian methods (e.g., RAxML-NG, IQ-TREE, MrBayes, BEAST) for rigorous inference with model selection and support assessments.
• Alignment, distance matrices, and formats: support for generating alignments, calculating distances, and exporting trees in Newick/Nexus formats.
• Bootstrap and support evaluation: tools to perform resampling and map support values onto trees.
• Comparison & visualization: utilities to compare topologies and visualize trees for interpretation.
• Real-world use case: Given a multiple sequence alignment, build multiple trees with different methods, compare bootstrap supports, and present a consensus.

Quick Start

Start by preparing a sequence alignment, choose a method (e.g., UPGMA for quick view, ML for publication-quality trees), run distance calculation, build trees, and compare them. Use helper utilities to export results to Newick or Nexus formats.