Infer phylogenetic trees from DNA, RNA, or protein alignments using maximum likelihood.
npx skills add https://github.com/roeimed0/rrna-phylo --skill ml-tree-level4
Please help me install this Agent Skill. Skill: ml-tree-level4 Source: https://github.com/roeimed0/rrna-phylo/tree/main/.claude/skills/ml-tree-level4 Command: npx skills add https://github.com/roeimed0/rrna-phylo --skill ml-tree-level4
This Skill automates advanced maximum-likelihood phylogenetic inference with automatic model selection, flexible tree-search strategies, and performance optimizations to produce high-quality evolutionary trees from sequence alignments.
Use the ml-tree-level4 skill to infer a tree from an aligned dataset with automatic model selection and multiple search strategies. Example: run_ml_tree --input data/sequences.fasta --tree-search nni --model auto --bootstrap 100
High-intent search queries and answers about installing and using this skill.
To infer a maximum-likelihood phylogenetic tree, you provide aligned DNA, RNA, or protein sequences to an ML inference tool. This skill automates the process using automatic model selection and multiple tree-search algorithms to generate a publication-ready tree.
The best way to select substitution models for phylogenetic tree search is using automatic model selection. This skill evaluates models using AIC/BIC criteria to balance fit and complexity, ensuring optimal branch-specific rates and FreeRate parameters for your dataset.
NNI and SPR tree search work for large DNA and protein alignments by systematically exploring topological rearrangements. This skill combines NNI, SPR, and TBR algorithms to robustly search tree space and find the highest likelihood topology.
Yes, you can use GPU acceleration for maximum-likelihood phylogenetics in Python. This skill supports optional GPU acceleration and Numba JIT compilation within a Python environment using NumPy and SciPy to process medium to large datasets efficiently.
Yes, automatic model selection supports partitioned datasets with FreeRate models. This skill handles partitions, branch-specific rates, and FreeRate models for DNA, RNA, and protein alignments, providing detailed reporting and bootstrap support.
You use AIC or BIC criteria when building publication-ready phylogenies to avoid overfitting. These criteria balance model fit and complexity during automatic model selection, ensuring the inferred maximum-likelihood tree is statistically sound.
fuzzy-dynamicsPerform end-to-end biological data analyses in Python with scikit-bio.
JosephWoodallManipulate and visualize phylogenetic trees from Newick/NHX formats.
K-Dense-AIAlign sequences with MAFFT, infer maximum-likelihood trees with IQ-TREE 2, and visualize results.
roeimed0Build and compare phylogenetic trees from sequence alignments using distance-based, maximum-likelihood, and Bayesian methods.
