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@davewalker5 davewalker5 released this 07 Jul 10:10
0.5.0
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Distance Matrix Calculator

This release introduces the project’s first phylogenetic analysis component: a reusable Distance Matrix Calculator.

Previous releases focused on simulating evolutionary histories under strict and relaxed molecular clock models. Version 0.5.0 begins the next stage of the project by providing the tools needed to analyse sequence data and prepare it for phylogenetic reconstruction algorithms such as UPGMA.

Highlights

Distance Matrix Calculator

A new command-line utility has been added for generating pairwise genetic distance matrices from aligned FASTA files.

The calculator supports two distance measures:

  • Hamming distance – the absolute number of differing nucleotide positions.
  • Proportional distance (p-distance) – the fraction of differing positions relative to sequence length.

Distance matrices are exported in both CSV and JSON formats, making them suitable for inspection, downstream analysis and reuse by future algorithms.

Reusable Core Module

The distance calculation logic has been implemented as a reusable Python module with a lightweight command-line wrapper.

This architecture allows the same implementation to be used directly by future phylogenetic reconstruction algorithms without duplicating code.

Documentation

The project documentation has been expanded to include:

  • A detailed description of the Distance Matrix Calculator
  • An overview of the supported distance measures
  • Updates to the project wiki reflecting the growing analysis pipeline

Project Roadmap

With the addition of the Distance Matrix Calculator, the project now covers the first two major stages of molecular clock analysis:

  • Simulating evolutionary histories
  • Measuring genetic distances between taxa

The next planned milestone is the implementation of a simple substitution model and then the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) clustering algorithm, allowing phylogenetic trees to be reconstructed directly from the generated distance matrices.

As with the existing simulation engines, the emphasis will remain on clarity, educational value and implementing the algorithms from first principles.

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