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High-level description

This directory contains implementations of various algorithms and tools for analyzing evolutionary fitness and phylogenetic relationships in biological sequences. The main components are:

1. A Forest class representing a collection of phylogenetic trees.
2. A Tree class representing individual phylogenetic trees.
3. A SFS (Site Frequency Spectrum) class for analyzing genetic diversity.
4. A SizeMatchedModel class for statistical modeling based on data size.

These tools are designed to analyze genetic diversity, infer evolutionary fitness, and understand the shape of genealogical trees in populations.

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What does it do?

The code in this directory performs several key functions:

1. Manages collections of phylogenetic trees:

   • Loads and saves trees from/to various formats (Newick, pickle, tar.gz).
   • Generates trees through simulation.
   • Concatenates forests of trees.

2. Analyzes individual phylogenetic trees:

   • Annotates nodes with features like depth, number of descendants, imbalance, and Colless index.
   • Calculates tree statistics such as total branch length and site frequency spectrum.
   • Resolves polytomies and rescales trees.

3. Calculates and analyzes Site Frequency Spectra (SFS):

   • Computes SFS from tree structures.
   • Calculates various genetic diversity statistics (Tajima’s D, Fay and Wu’s H, Zeng’s E, Ferretti’s L).
   • Bins SFS data for visualization and analysis.

4. Provides statistical modeling based on data size:

   • Implements a model where parameters vary based on input data size.
   • Calculates p-values and model means for given sizes.

5. Visualizes phylogenetic trees and results.

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Entry points

The main entry points for using this codebase are:

1. Forest class: For managing collections of phylogenetic trees.
2. Tree class: For analyzing individual phylogenetic trees.
3. SFS class: For calculating and analyzing Site Frequency Spectra.
4. SizeMatchedModel class: For statistical modeling based on data size.

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Key Files

1. forest.py: Defines the Forest class for managing collections of trees.
2. tree.py: Defines the Tree class for individual tree analysis.
3. sfs.py: Defines the SFS class for Site Frequency Spectrum analysis.
4. size_matched_model.py: Defines the SizeMatchedModel class for size-based statistical modeling.

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Dependencies

The codebase relies on several external libraries:

1. ete3: For phylogenetic tree manipulation and visualization.
2. Bio.Phylo: For interfacing with Biopython’s phylogenetic tree representation.
3. numpy: For numerical computations and array manipulations.
4. scipy: For various scientific computing tasks and statistical functions.
5. pandas: For data manipulation and analysis.
6. matplotlib: For visualization of results and trees.

Additionally, the code uses custom modules for specific tasks like beta-tree simulation and fitness inference.

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Configuration

The main classes (Forest, Tree, SFS, SizeMatchedModel) use constructor parameters and method arguments for configuration. Key parameters include:

• For Forest and Tree:

  • Paths to input files (Newick, pickle, tar.gz).
  • Parameters for tree generation (e.g., number of leaves, alpha for beta-tree model).
  • Options for tree manipulation (e.g., resolving polytomies, rescaling).

• For SFS:

  • SFS data as numpy array.
  • Binning parameters for SFS analysis.

• For SizeMatchedModel:

  • Bins and parameters for size-based modeling.
  • Statistical distribution for calculations.

Users can adjust these parameters to customize the analysis for their specific needs in evolutionary studies and population genetics research.