phylogenetics

Automating phylogenetic tree inference requires linking multiple sequence alignment with maximum likelihood tree building. This skill streamlines that pipeline for viral genomes and microbial sequences, using MAFFT for alignment and IQ-TREE 2 for high-accuracy evolutionary reconstruction.

Building a phylogenetic tree from a large protein family dataset is best handled with high-speed approximate inference. This skill utilizes FastTree to efficiently generate evolutionary relationships for large biological datasets when maximum likelihood methods become computationally restrictive.

Yes, you need to install MAFFT and IQ-TREE 2 to run this end-to-end phylogenetics workflow. Executing the full computational pipeline also requires FastTree, ETE3, and matplotlib to support tree inference, evolutionary analysis, and publication-quality visualization.

Maximum likelihood tree inference via IQ-TREE 2 provides high-accuracy branching patterns for molecular clock studies, while FastTree offers high-speed approximate inference. This skill supports both approaches, allowing researchers to balance computational speed and phylogenetic accuracy.

Yes, you can generate publication-quality phylogenetic visualizations using ETE3 and matplotlib. This skill integrates both dependencies to compute statistical support for branching patterns and render visual outputs suitable for evolutionary biology research publications.